ml/Large_dataset_build_model_all_joints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large Dataset Model Building On All Joints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Datasets and Clean\n",
    "In this configuration the relevant data set should be loaded from the same folder as the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('jackson_megadata_10_23.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of timestamps from the two hardware devices and a diff between them. When the two hardware data streams were stitched together an effor was made to minimize this diff, but the driver configuration did not easily permit eliminating it. This information is included to understand the accuracy of the data, but will not be used during the training.\n",
    "\n",
    "The time data is followed by the 8 channels from the Myo, this data will be used as input features.\n",
    "\n",
    "This is followed by the 63 positional points from the Leap cameras. These will be used as labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Leap timestamp</th>\n",
       "      <th>timestamp diff</th>\n",
       "      <th>emg timestamp</th>\n",
       "      <th>ch1</th>\n",
       "      <th>ch2</th>\n",
       "      <th>ch3</th>\n",
       "      <th>ch4</th>\n",
       "      <th>ch5</th>\n",
       "      <th>ch6</th>\n",
       "      <th>ch7</th>\n",
       "      <th>ch8</th>\n",
       "      <th>Wrist x</th>\n",
       "      <th>Wrist y</th>\n",
       "      <th>Wrist z</th>\n",
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       "      <th>Thumb Proximal y</th>\n",
       "      <th>Thumb Proximal z</th>\n",
       "      <th>Thumb Intermediate x</th>\n",
       "      <th>Thumb Intermediate y</th>\n",
       "      <th>Thumb Intermediate z</th>\n",
       "      <th>Thumb Distal x</th>\n",
       "      <th>Thumb Distal y</th>\n",
       "      <th>Thumb Distal z</th>\n",
       "      <th>Thumb Tip x</th>\n",
       "      <th>Thumb Tip y</th>\n",
       "      <th>Thumb Tip z</th>\n",
       "      <th>Index Proximal x</th>\n",
       "      <th>Index Proximal y</th>\n",
       "      <th>Index Proximal z</th>\n",
       "      <th>Index Intermediate x</th>\n",
       "      <th>Index Intermediate y</th>\n",
       "      <th>Index Intermediate z</th>\n",
       "      <th>Index Distal x</th>\n",
       "      <th>Index Distal y</th>\n",
       "      <th>Index Distal z</th>\n",
       "      <th>Index Tip x</th>\n",
       "      <th>Index Tip y</th>\n",
       "      <th>Index Tip z</th>\n",
       "      <th>Middle Proximal x</th>\n",
       "      <th>Middle Proximal y</th>\n",
       "      <th>Middle Proximal z</th>\n",
       "      <th>Middle Intermediate x</th>\n",
       "      <th>Middle Intermediate y</th>\n",
       "      <th>Middle Intermediate z</th>\n",
       "      <th>Middle Distal x</th>\n",
       "      <th>Middle Distal y</th>\n",
       "      <th>Middle Distal z</th>\n",
       "      <th>Middle Tip x</th>\n",
       "      <th>Middle Tip y</th>\n",
       "      <th>Middle Tip z</th>\n",
       "      <th>Ring Proximal x</th>\n",
       "      <th>Ring Proximal y</th>\n",
       "      <th>Ring Proximal z</th>\n",
       "      <th>Ring Intermediate x</th>\n",
       "      <th>Ring Intermediate y</th>\n",
       "      <th>Ring Intermediate z</th>\n",
       "      <th>Ring Distal x</th>\n",
       "      <th>Ring Distal y</th>\n",
       "      <th>Ring Distal z</th>\n",
       "      <th>Ring Tip x</th>\n",
       "      <th>Ring Tip y</th>\n",
       "      <th>Ring Tip z</th>\n",
       "      <th>Pinky Proximal x</th>\n",
       "      <th>Pinky Proximal y</th>\n",
       "      <th>Pinky Proximal z</th>\n",
       "      <th>Pinky Intermediate x</th>\n",
       "      <th>Pinky Intermediate y</th>\n",
       "      <th>Pinky Intermediate z</th>\n",
       "      <th>Pinky Distal x</th>\n",
       "      <th>Pinky Distal y</th>\n",
       "      <th>Pinky Distal z</th>\n",
       "      <th>Pinky Tip x</th>\n",
       "      <th>Pinky Tip y</th>\n",
       "      <th>Pinky Tip z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>0.006472</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>27.481928</td>\n",
       "      <td>0.635315</td>\n",
       "      <td>44.404579</td>\n",
       "      <td>3.819370</td>\n",
       "      <td>-10.501190</td>\n",
       "      <td>57.583588</td>\n",
       "      <td>-39.021576</td>\n",
       "      <td>-14.662689</td>\n",
       "      <td>37.134148</td>\n",
       "      <td>-69.938087</td>\n",
       "      <td>-15.536011</td>\n",
       "      <td>26.992786</td>\n",
       "      <td>-90.684303</td>\n",
       "      <td>-16.765839</td>\n",
       "      <td>18.792263</td>\n",
       "      <td>-31.480103</td>\n",
       "      <td>3.195557</td>\n",
       "      <td>-11.996174</td>\n",
       "      <td>-48.617424</td>\n",
       "      <td>12.979187</td>\n",
       "      <td>-47.950493</td>\n",
       "      <td>-56.789734</td>\n",
       "      <td>2.440277</td>\n",
       "      <td>-66.780174</td>\n",
       "      <td>-59.230820</td>\n",
       "      <td>-12.140320</td>\n",
       "      <td>-73.670944</td>\n",
       "      <td>-12.269012</td>\n",
       "      <td>5.741394</td>\n",
       "      <td>-20.408253</td>\n",
       "      <td>-26.628906</td>\n",
       "      <td>19.429688</td>\n",
       "      <td>-61.925949</td>\n",
       "      <td>-36.830482</td>\n",
       "      <td>8.192657</td>\n",
       "      <td>-84.433563</td>\n",
       "      <td>-41.600212</td>\n",
       "      <td>-7.619537</td>\n",
       "      <td>-91.437363</td>\n",
       "      <td>9.305349</td>\n",
       "      <td>5.784424</td>\n",
       "      <td>-23.192884</td>\n",
       "      <td>-1.900085</td>\n",
       "      <td>17.207031</td>\n",
       "      <td>-62.730690</td>\n",
       "      <td>-11.677986</td>\n",
       "      <td>6.098206</td>\n",
       "      <td>-84.647568</td>\n",
       "      <td>-17.221012</td>\n",
       "      <td>-9.363892</td>\n",
       "      <td>-91.600197</td>\n",
       "      <td>29.291636</td>\n",
       "      <td>1.544373</td>\n",
       "      <td>-23.824600</td>\n",
       "      <td>22.835503</td>\n",
       "      <td>5.419800</td>\n",
       "      <td>-56.729340</td>\n",
       "      <td>17.429138</td>\n",
       "      <td>2.168213</td>\n",
       "      <td>-74.302849</td>\n",
       "      <td>11.652407</td>\n",
       "      <td>-4.481842</td>\n",
       "      <td>-88.201462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>-0.017444</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>54.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>26.647976</td>\n",
       "      <td>-0.541893</td>\n",
       "      <td>44.900626</td>\n",
       "      <td>2.603665</td>\n",
       "      <td>-11.599823</td>\n",
       "      <td>57.431037</td>\n",
       "      <td>-39.666034</td>\n",
       "      <td>-14.301682</td>\n",
       "      <td>35.652349</td>\n",
       "      <td>-70.089661</td>\n",
       "      <td>-14.468178</td>\n",
       "      <td>24.132261</td>\n",
       "      <td>-90.578381</td>\n",
       "      <td>-15.055008</td>\n",
       "      <td>15.274604</td>\n",
       "      <td>-31.203072</td>\n",
       "      <td>3.832390</td>\n",
       "      <td>-12.509948</td>\n",
       "      <td>-47.597781</td>\n",
       "      <td>14.169205</td>\n",
       "      <td>-48.651957</td>\n",
       "      <td>-55.505336</td>\n",
       "      <td>3.798073</td>\n",
       "      <td>-67.685915</td>\n",
       "      <td>-57.910363</td>\n",
       "      <td>-10.747452</td>\n",
       "      <td>-74.662590</td>\n",
       "      <td>-11.808361</td>\n",
       "      <td>6.275238</td>\n",
       "      <td>-20.517360</td>\n",
       "      <td>-25.454881</td>\n",
       "      <td>20.207542</td>\n",
       "      <td>-62.191739</td>\n",
       "      <td>-35.418246</td>\n",
       "      <td>9.399536</td>\n",
       "      <td>-85.011818</td>\n",
       "      <td>-40.310819</td>\n",
       "      <td>-6.143776</td>\n",
       "      <td>-92.508541</td>\n",
       "      <td>9.811148</td>\n",
       "      <td>6.081985</td>\n",
       "      <td>-22.903098</td>\n",
       "      <td>-0.553401</td>\n",
       "      <td>17.576523</td>\n",
       "      <td>-62.645160</td>\n",
       "      <td>-9.981847</td>\n",
       "      <td>6.514847</td>\n",
       "      <td>-84.736057</td>\n",
       "      <td>-15.500547</td>\n",
       "      <td>-8.887718</td>\n",
       "      <td>-91.833954</td>\n",
       "      <td>29.747767</td>\n",
       "      <td>1.589165</td>\n",
       "      <td>-23.245819</td>\n",
       "      <td>24.140824</td>\n",
       "      <td>6.374512</td>\n",
       "      <td>-56.173798</td>\n",
       "      <td>19.047623</td>\n",
       "      <td>3.444153</td>\n",
       "      <td>-73.894844</td>\n",
       "      <td>13.431637</td>\n",
       "      <td>-3.077950</td>\n",
       "      <td>-87.919249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>0.003166</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>50.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>25.490219</td>\n",
       "      <td>-3.977844</td>\n",
       "      <td>45.407623</td>\n",
       "      <td>0.888691</td>\n",
       "      <td>-15.207642</td>\n",
       "      <td>56.645126</td>\n",
       "      <td>-40.799980</td>\n",
       "      <td>-12.787781</td>\n",
       "      <td>33.687691</td>\n",
       "      <td>-70.586067</td>\n",
       "      <td>-9.926239</td>\n",
       "      <td>20.879833</td>\n",
       "      <td>-91.010010</td>\n",
       "      <td>-7.984314</td>\n",
       "      <td>12.033489</td>\n",
       "      <td>-30.784788</td>\n",
       "      <td>5.359344</td>\n",
       "      <td>-12.988110</td>\n",
       "      <td>-46.170105</td>\n",
       "      <td>17.185913</td>\n",
       "      <td>-49.120426</td>\n",
       "      <td>-53.684822</td>\n",
       "      <td>7.380035</td>\n",
       "      <td>-68.608513</td>\n",
       "      <td>-56.010406</td>\n",
       "      <td>-6.984161</td>\n",
       "      <td>-75.977188</td>\n",
       "      <td>-11.141672</td>\n",
       "      <td>7.823792</td>\n",
       "      <td>-20.362720</td>\n",
       "      <td>-23.704641</td>\n",
       "      <td>23.463989</td>\n",
       "      <td>-61.773483</td>\n",
       "      <td>-33.332466</td>\n",
       "      <td>13.942474</td>\n",
       "      <td>-85.301781</td>\n",
       "      <td>-38.390213</td>\n",
       "      <td>-0.982178</td>\n",
       "      <td>-93.875229</td>\n",
       "      <td>10.523100</td>\n",
       "      <td>7.254333</td>\n",
       "      <td>-22.239563</td>\n",
       "      <td>1.277081</td>\n",
       "      <td>20.302490</td>\n",
       "      <td>-61.781082</td>\n",
       "      <td>-7.774546</td>\n",
       "      <td>10.311188</td>\n",
       "      <td>-84.531990</td>\n",
       "      <td>-13.392775</td>\n",
       "      <td>-4.599304</td>\n",
       "      <td>-92.547852</td>\n",
       "      <td>30.355955</td>\n",
       "      <td>2.308136</td>\n",
       "      <td>-22.387161</td>\n",
       "      <td>26.408840</td>\n",
       "      <td>10.257629</td>\n",
       "      <td>-54.954762</td>\n",
       "      <td>21.679298</td>\n",
       "      <td>8.039429</td>\n",
       "      <td>-72.880737</td>\n",
       "      <td>16.065914</td>\n",
       "      <td>1.318481</td>\n",
       "      <td>-86.812164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>0.010059</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>40.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>25.153648</td>\n",
       "      <td>-5.140533</td>\n",
       "      <td>45.478523</td>\n",
       "      <td>0.395868</td>\n",
       "      <td>-16.426270</td>\n",
       "      <td>56.309441</td>\n",
       "      <td>-41.075424</td>\n",
       "      <td>-11.990051</td>\n",
       "      <td>33.259583</td>\n",
       "      <td>-70.632256</td>\n",
       "      <td>-7.888000</td>\n",
       "      <td>20.259293</td>\n",
       "      <td>-91.043556</td>\n",
       "      <td>-4.950928</td>\n",
       "      <td>11.661678</td>\n",
       "      <td>-30.656673</td>\n",
       "      <td>5.864960</td>\n",
       "      <td>-13.072382</td>\n",
       "      <td>-45.760315</td>\n",
       "      <td>17.912903</td>\n",
       "      <td>-49.250374</td>\n",
       "      <td>-53.120193</td>\n",
       "      <td>8.142944</td>\n",
       "      <td>-68.815437</td>\n",
       "      <td>-55.381058</td>\n",
       "      <td>-6.207825</td>\n",
       "      <td>-76.230270</td>\n",
       "      <td>-10.946850</td>\n",
       "      <td>8.341492</td>\n",
       "      <td>-20.262655</td>\n",
       "      <td>-23.150558</td>\n",
       "      <td>24.345184</td>\n",
       "      <td>-61.641949</td>\n",
       "      <td>-32.622368</td>\n",
       "      <td>15.204285</td>\n",
       "      <td>-85.383560</td>\n",
       "      <td>-37.708450</td>\n",
       "      <td>0.511047</td>\n",
       "      <td>-94.331711</td>\n",
       "      <td>10.725384</td>\n",
       "      <td>7.651855</td>\n",
       "      <td>-22.008507</td>\n",
       "      <td>1.703369</td>\n",
       "      <td>20.909332</td>\n",
       "      <td>-61.532143</td>\n",
       "      <td>-7.254799</td>\n",
       "      <td>11.117615</td>\n",
       "      <td>-84.406494</td>\n",
       "      <td>-12.890709</td>\n",
       "      <td>-3.660126</td>\n",
       "      <td>-92.652481</td>\n",
       "      <td>30.522303</td>\n",
       "      <td>2.563080</td>\n",
       "      <td>-22.131792</td>\n",
       "      <td>27.211058</td>\n",
       "      <td>11.719635</td>\n",
       "      <td>-54.452309</td>\n",
       "      <td>22.549606</td>\n",
       "      <td>9.592224</td>\n",
       "      <td>-72.407104</td>\n",
       "      <td>16.854794</td>\n",
       "      <td>2.514252</td>\n",
       "      <td>-86.127060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>-0.013824</td>\n",
       "      <td>1.571862e+09</td>\n",
       "      <td>45.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>24.743472</td>\n",
       "      <td>-6.742635</td>\n",
       "      <td>45.491520</td>\n",
       "      <td>-0.110102</td>\n",
       "      <td>-18.394918</td>\n",
       "      <td>55.696049</td>\n",
       "      <td>-41.305864</td>\n",
       "      <td>-12.705556</td>\n",
       "      <td>32.434175</td>\n",
       "      <td>-70.632487</td>\n",
       "      <td>-7.905243</td>\n",
       "      <td>19.158581</td>\n",
       "      <td>-90.982437</td>\n",
       "      <td>-4.420380</td>\n",
       "      <td>10.624847</td>\n",
       "      <td>-30.537937</td>\n",
       "      <td>6.328888</td>\n",
       "      <td>-13.131980</td>\n",
       "      <td>-45.344407</td>\n",
       "      <td>18.750071</td>\n",
       "      <td>-49.306426</td>\n",
       "      <td>-52.401997</td>\n",
       "      <td>9.165934</td>\n",
       "      <td>-69.073558</td>\n",
       "      <td>-54.474183</td>\n",
       "      <td>-5.081034</td>\n",
       "      <td>-76.738945</td>\n",
       "      <td>-10.764413</td>\n",
       "      <td>9.051208</td>\n",
       "      <td>-20.053380</td>\n",
       "      <td>-22.729432</td>\n",
       "      <td>25.211934</td>\n",
       "      <td>-61.441198</td>\n",
       "      <td>-31.890605</td>\n",
       "      <td>16.455200</td>\n",
       "      <td>-85.447795</td>\n",
       "      <td>-36.853535</td>\n",
       "      <td>2.071289</td>\n",
       "      <td>-94.948532</td>\n",
       "      <td>10.922589</td>\n",
       "      <td>8.417521</td>\n",
       "      <td>-21.627883</td>\n",
       "      <td>2.164737</td>\n",
       "      <td>22.093313</td>\n",
       "      <td>-61.068199</td>\n",
       "      <td>-6.513943</td>\n",
       "      <td>12.694092</td>\n",
       "      <td>-84.212934</td>\n",
       "      <td>-12.022625</td>\n",
       "      <td>-1.847493</td>\n",
       "      <td>-92.948497</td>\n",
       "      <td>30.719985</td>\n",
       "      <td>3.331126</td>\n",
       "      <td>-21.752429</td>\n",
       "      <td>28.355796</td>\n",
       "      <td>14.152852</td>\n",
       "      <td>-53.633718</td>\n",
       "      <td>23.850754</td>\n",
       "      <td>12.003235</td>\n",
       "      <td>-71.625646</td>\n",
       "      <td>18.137719</td>\n",
       "      <td>4.223226</td>\n",
       "      <td>-84.948919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Leap timestamp  timestamp diff  emg timestamp   ch1    ch2  \\\n",
       "0           0    1.571862e+09        0.006472   1.571862e+09  56.0  152.0   \n",
       "1           1    1.571862e+09       -0.017444   1.571862e+09  54.0  164.0   \n",
       "2           2    1.571862e+09        0.003166   1.571862e+09  50.0  161.0   \n",
       "3           3    1.571862e+09        0.010059   1.571862e+09  40.0  170.0   \n",
       "4           4    1.571862e+09       -0.013824   1.571862e+09  45.0  172.0   \n",
       "\n",
       "     ch3    ch4   ch5   ch6   ch7    ch8    Wrist x   Wrist y    Wrist z  \\\n",
       "0  318.0   58.0  30.0  26.0  37.0  168.0  27.481928  0.635315  44.404579   \n",
       "1  301.0  148.0  43.0  29.0  36.0  169.0  26.647976 -0.541893  44.900626   \n",
       "2  401.0  184.0  49.0  32.0  37.0  162.0  25.490219 -3.977844  45.407623   \n",
       "3  451.0  206.0  52.0  32.0  28.0   82.0  25.153648 -5.140533  45.478523   \n",
       "4  480.0  212.0  53.0  36.0  41.0  102.0  24.743472 -6.742635  45.491520   \n",
       "\n",
       "   Thumb Proximal x  Thumb Proximal y  Thumb Proximal z  Thumb Intermediate x  \\\n",
       "0          3.819370        -10.501190         57.583588            -39.021576   \n",
       "1          2.603665        -11.599823         57.431037            -39.666034   \n",
       "2          0.888691        -15.207642         56.645126            -40.799980   \n",
       "3          0.395868        -16.426270         56.309441            -41.075424   \n",
       "4         -0.110102        -18.394918         55.696049            -41.305864   \n",
       "\n",
       "   Thumb Intermediate y  Thumb Intermediate z  Thumb Distal x  Thumb Distal y  \\\n",
       "0            -14.662689             37.134148      -69.938087      -15.536011   \n",
       "1            -14.301682             35.652349      -70.089661      -14.468178   \n",
       "2            -12.787781             33.687691      -70.586067       -9.926239   \n",
       "3            -11.990051             33.259583      -70.632256       -7.888000   \n",
       "4            -12.705556             32.434175      -70.632487       -7.905243   \n",
       "\n",
       "   Thumb Distal z  Thumb Tip x  Thumb Tip y  Thumb Tip z  Index Proximal x  \\\n",
       "0       26.992786   -90.684303   -16.765839    18.792263        -31.480103   \n",
       "1       24.132261   -90.578381   -15.055008    15.274604        -31.203072   \n",
       "2       20.879833   -91.010010    -7.984314    12.033489        -30.784788   \n",
       "3       20.259293   -91.043556    -4.950928    11.661678        -30.656673   \n",
       "4       19.158581   -90.982437    -4.420380    10.624847        -30.537937   \n",
       "\n",
       "   Index Proximal y  Index Proximal z  Index Intermediate x  \\\n",
       "0          3.195557        -11.996174            -48.617424   \n",
       "1          3.832390        -12.509948            -47.597781   \n",
       "2          5.359344        -12.988110            -46.170105   \n",
       "3          5.864960        -13.072382            -45.760315   \n",
       "4          6.328888        -13.131980            -45.344407   \n",
       "\n",
       "   Index Intermediate y  Index Intermediate z  Index Distal x  Index Distal y  \\\n",
       "0             12.979187            -47.950493      -56.789734        2.440277   \n",
       "1             14.169205            -48.651957      -55.505336        3.798073   \n",
       "2             17.185913            -49.120426      -53.684822        7.380035   \n",
       "3             17.912903            -49.250374      -53.120193        8.142944   \n",
       "4             18.750071            -49.306426      -52.401997        9.165934   \n",
       "\n",
       "   Index Distal z  Index Tip x  Index Tip y  Index Tip z  Middle Proximal x  \\\n",
       "0      -66.780174   -59.230820   -12.140320   -73.670944         -12.269012   \n",
       "1      -67.685915   -57.910363   -10.747452   -74.662590         -11.808361   \n",
       "2      -68.608513   -56.010406    -6.984161   -75.977188         -11.141672   \n",
       "3      -68.815437   -55.381058    -6.207825   -76.230270         -10.946850   \n",
       "4      -69.073558   -54.474183    -5.081034   -76.738945         -10.764413   \n",
       "\n",
       "   Middle Proximal y  Middle Proximal z  Middle Intermediate x  \\\n",
       "0           5.741394         -20.408253             -26.628906   \n",
       "1           6.275238         -20.517360             -25.454881   \n",
       "2           7.823792         -20.362720             -23.704641   \n",
       "3           8.341492         -20.262655             -23.150558   \n",
       "4           9.051208         -20.053380             -22.729432   \n",
       "\n",
       "   Middle Intermediate y  Middle Intermediate z  Middle Distal x  \\\n",
       "0              19.429688             -61.925949       -36.830482   \n",
       "1              20.207542             -62.191739       -35.418246   \n",
       "2              23.463989             -61.773483       -33.332466   \n",
       "3              24.345184             -61.641949       -32.622368   \n",
       "4              25.211934             -61.441198       -31.890605   \n",
       "\n",
       "   Middle Distal y  Middle Distal z  Middle Tip x  Middle Tip y  Middle Tip z  \\\n",
       "0         8.192657       -84.433563    -41.600212     -7.619537    -91.437363   \n",
       "1         9.399536       -85.011818    -40.310819     -6.143776    -92.508541   \n",
       "2        13.942474       -85.301781    -38.390213     -0.982178    -93.875229   \n",
       "3        15.204285       -85.383560    -37.708450      0.511047    -94.331711   \n",
       "4        16.455200       -85.447795    -36.853535      2.071289    -94.948532   \n",
       "\n",
       "   Ring Proximal x  Ring Proximal y  Ring Proximal z  Ring Intermediate x  \\\n",
       "0         9.305349         5.784424       -23.192884            -1.900085   \n",
       "1         9.811148         6.081985       -22.903098            -0.553401   \n",
       "2        10.523100         7.254333       -22.239563             1.277081   \n",
       "3        10.725384         7.651855       -22.008507             1.703369   \n",
       "4        10.922589         8.417521       -21.627883             2.164737   \n",
       "\n",
       "   Ring Intermediate y  Ring Intermediate z  Ring Distal x  Ring Distal y  \\\n",
       "0            17.207031           -62.730690     -11.677986       6.098206   \n",
       "1            17.576523           -62.645160      -9.981847       6.514847   \n",
       "2            20.302490           -61.781082      -7.774546      10.311188   \n",
       "3            20.909332           -61.532143      -7.254799      11.117615   \n",
       "4            22.093313           -61.068199      -6.513943      12.694092   \n",
       "\n",
       "   Ring Distal z  Ring Tip x  Ring Tip y  Ring Tip z  Pinky Proximal x  \\\n",
       "0     -84.647568  -17.221012   -9.363892  -91.600197         29.291636   \n",
       "1     -84.736057  -15.500547   -8.887718  -91.833954         29.747767   \n",
       "2     -84.531990  -13.392775   -4.599304  -92.547852         30.355955   \n",
       "3     -84.406494  -12.890709   -3.660126  -92.652481         30.522303   \n",
       "4     -84.212934  -12.022625   -1.847493  -92.948497         30.719985   \n",
       "\n",
       "   Pinky Proximal y  Pinky Proximal z  Pinky Intermediate x  \\\n",
       "0          1.544373        -23.824600             22.835503   \n",
       "1          1.589165        -23.245819             24.140824   \n",
       "2          2.308136        -22.387161             26.408840   \n",
       "3          2.563080        -22.131792             27.211058   \n",
       "4          3.331126        -21.752429             28.355796   \n",
       "\n",
       "   Pinky Intermediate y  Pinky Intermediate z  Pinky Distal x  Pinky Distal y  \\\n",
       "0              5.419800            -56.729340       17.429138        2.168213   \n",
       "1              6.374512            -56.173798       19.047623        3.444153   \n",
       "2             10.257629            -54.954762       21.679298        8.039429   \n",
       "3             11.719635            -54.452309       22.549606        9.592224   \n",
       "4             14.152852            -53.633718       23.850754       12.003235   \n",
       "\n",
       "   Pinky Distal z  Pinky Tip x  Pinky Tip y  Pinky Tip z  \n",
       "0      -74.302849    11.652407    -4.481842   -88.201462  \n",
       "1      -73.894844    13.431637    -3.077950   -87.919249  \n",
       "2      -72.880737    16.065914     1.318481   -86.812164  \n",
       "3      -72.407104    16.854794     2.514252   -86.127060  \n",
       "4      -71.625646    18.137719     4.223226   -84.948919  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(labels=[\"Leap timestamp\", \"timestamp diff\", \"emg timestamp\"], axis=1)\n",
    "df = df.drop(df.columns[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ch1</th>\n",
       "      <th>ch2</th>\n",
       "      <th>ch3</th>\n",
       "      <th>ch4</th>\n",
       "      <th>ch5</th>\n",
       "      <th>ch6</th>\n",
       "      <th>ch7</th>\n",
       "      <th>ch8</th>\n",
       "      <th>Wrist x</th>\n",
       "      <th>Wrist y</th>\n",
       "      <th>Wrist z</th>\n",
       "      <th>Thumb Proximal x</th>\n",
       "      <th>Thumb Proximal y</th>\n",
       "      <th>Thumb Proximal z</th>\n",
       "      <th>Thumb Intermediate x</th>\n",
       "      <th>Thumb Intermediate y</th>\n",
       "      <th>Thumb Intermediate z</th>\n",
       "      <th>Thumb Distal x</th>\n",
       "      <th>Thumb Distal y</th>\n",
       "      <th>Thumb Distal z</th>\n",
       "      <th>Thumb Tip x</th>\n",
       "      <th>Thumb Tip y</th>\n",
       "      <th>Thumb Tip z</th>\n",
       "      <th>Index Proximal x</th>\n",
       "      <th>Index Proximal y</th>\n",
       "      <th>Index Proximal z</th>\n",
       "      <th>Index Intermediate x</th>\n",
       "      <th>Index Intermediate y</th>\n",
       "      <th>Index Intermediate z</th>\n",
       "      <th>Index Distal x</th>\n",
       "      <th>Index Distal y</th>\n",
       "      <th>Index Distal z</th>\n",
       "      <th>Index Tip x</th>\n",
       "      <th>Index Tip y</th>\n",
       "      <th>Index Tip z</th>\n",
       "      <th>Middle Proximal x</th>\n",
       "      <th>Middle Proximal y</th>\n",
       "      <th>Middle Proximal z</th>\n",
       "      <th>Middle Intermediate x</th>\n",
       "      <th>Middle Intermediate y</th>\n",
       "      <th>Middle Intermediate z</th>\n",
       "      <th>Middle Distal x</th>\n",
       "      <th>Middle Distal y</th>\n",
       "      <th>Middle Distal z</th>\n",
       "      <th>Middle Tip x</th>\n",
       "      <th>Middle Tip y</th>\n",
       "      <th>Middle Tip z</th>\n",
       "      <th>Ring Proximal x</th>\n",
       "      <th>Ring Proximal y</th>\n",
       "      <th>Ring Proximal z</th>\n",
       "      <th>Ring Intermediate x</th>\n",
       "      <th>Ring Intermediate y</th>\n",
       "      <th>Ring Intermediate z</th>\n",
       "      <th>Ring Distal x</th>\n",
       "      <th>Ring Distal y</th>\n",
       "      <th>Ring Distal z</th>\n",
       "      <th>Ring Tip x</th>\n",
       "      <th>Ring Tip y</th>\n",
       "      <th>Ring Tip z</th>\n",
       "      <th>Pinky Proximal x</th>\n",
       "      <th>Pinky Proximal y</th>\n",
       "      <th>Pinky Proximal z</th>\n",
       "      <th>Pinky Intermediate x</th>\n",
       "      <th>Pinky Intermediate y</th>\n",
       "      <th>Pinky Intermediate z</th>\n",
       "      <th>Pinky Distal x</th>\n",
       "      <th>Pinky Distal y</th>\n",
       "      <th>Pinky Distal z</th>\n",
       "      <th>Pinky Tip x</th>\n",
       "      <th>Pinky Tip y</th>\n",
       "      <th>Pinky Tip z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "      <td>410705.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>69.830650</td>\n",
       "      <td>194.624456</td>\n",
       "      <td>327.642768</td>\n",
       "      <td>71.728214</td>\n",
       "      <td>44.296870</td>\n",
       "      <td>87.210112</td>\n",
       "      <td>73.443849</td>\n",
       "      <td>95.129351</td>\n",
       "      <td>13.433740</td>\n",
       "      <td>-9.918895</td>\n",
       "      <td>49.406393</td>\n",
       "      <td>-11.376644</td>\n",
       "      <td>-25.605264</td>\n",
       "      <td>51.338970</td>\n",
       "      <td>-44.872333</td>\n",
       "      <td>-24.645973</td>\n",
       "      <td>18.824727</td>\n",
       "      <td>-61.431829</td>\n",
       "      <td>-24.770864</td>\n",
       "      <td>-7.855166</td>\n",
       "      <td>-77.766978</td>\n",
       "      <td>-23.818628</td>\n",
       "      <td>-20.041394</td>\n",
       "      <td>-27.111190</td>\n",
       "      <td>4.681878</td>\n",
       "      <td>-20.149181</td>\n",
       "      <td>-31.168010</td>\n",
       "      <td>2.216711</td>\n",
       "      <td>-56.533035</td>\n",
       "      <td>-31.400229</td>\n",
       "      <td>-3.194320</td>\n",
       "      <td>-73.436094</td>\n",
       "      <td>-30.786220</td>\n",
       "      <td>-7.652179</td>\n",
       "      <td>-83.590116</td>\n",
       "      <td>-6.693581</td>\n",
       "      <td>10.463009</td>\n",
       "      <td>-21.165028</td>\n",
       "      <td>-6.515101</td>\n",
       "      <td>11.520539</td>\n",
       "      <td>-63.206372</td>\n",
       "      <td>-5.261173</td>\n",
       "      <td>7.249119</td>\n",
       "      <td>-84.549727</td>\n",
       "      <td>-3.946629</td>\n",
       "      <td>3.396960</td>\n",
       "      <td>-96.981908</td>\n",
       "      <td>14.726588</td>\n",
       "      <td>12.407608</td>\n",
       "      <td>-16.884533</td>\n",
       "      <td>16.900805</td>\n",
       "      <td>11.061808</td>\n",
       "      <td>-56.082492</td>\n",
       "      <td>18.437839</td>\n",
       "      <td>4.973580</td>\n",
       "      <td>-76.538139</td>\n",
       "      <td>19.668363</td>\n",
       "      <td>-0.382871</td>\n",
       "      <td>-88.293062</td>\n",
       "      <td>34.624458</td>\n",
       "      <td>9.507856</td>\n",
       "      <td>-12.152019</td>\n",
       "      <td>44.629017</td>\n",
       "      <td>11.456136</td>\n",
       "      <td>-40.051041</td>\n",
       "      <td>49.475038</td>\n",
       "      <td>9.713231</td>\n",
       "      <td>-53.828391</td>\n",
       "      <td>53.471617</td>\n",
       "      <td>7.140140</td>\n",
       "      <td>-64.891697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>52.362277</td>\n",
       "      <td>117.678324</td>\n",
       "      <td>202.985681</td>\n",
       "      <td>44.448922</td>\n",
       "      <td>36.061338</td>\n",
       "      <td>141.084668</td>\n",
       "      <td>73.084319</td>\n",
       "      <td>68.073366</td>\n",
       "      <td>11.101877</td>\n",
       "      <td>9.608523</td>\n",
       "      <td>5.131227</td>\n",
       "      <td>12.637440</td>\n",
       "      <td>11.062260</td>\n",
       "      <td>5.787927</td>\n",
       "      <td>11.626375</td>\n",
       "      <td>7.278401</td>\n",
       "      <td>10.328375</td>\n",
       "      <td>15.079041</td>\n",
       "      <td>12.786336</td>\n",
       "      <td>15.241664</td>\n",
       "      <td>21.838932</td>\n",
       "      <td>17.859002</td>\n",
       "      <td>19.772263</td>\n",
       "      <td>4.206371</td>\n",
       "      <td>4.495053</td>\n",
       "      <td>6.532463</td>\n",
       "      <td>13.169467</td>\n",
       "      <td>20.142673</td>\n",
       "      <td>11.303510</td>\n",
       "      <td>17.535305</td>\n",
       "      <td>30.724830</td>\n",
       "      <td>20.110927</td>\n",
       "      <td>20.610897</td>\n",
       "      <td>36.878668</td>\n",
       "      <td>29.311201</td>\n",
       "      <td>4.934637</td>\n",
       "      <td>4.187734</td>\n",
       "      <td>2.722228</td>\n",
       "      <td>14.825448</td>\n",
       "      <td>20.637099</td>\n",
       "      <td>8.213241</td>\n",
       "      <td>19.314485</td>\n",
       "      <td>33.083783</td>\n",
       "      <td>18.344533</td>\n",
       "      <td>21.852345</td>\n",
       "      <td>39.955083</td>\n",
       "      <td>27.991373</td>\n",
       "      <td>4.647071</td>\n",
       "      <td>4.449165</td>\n",
       "      <td>4.145501</td>\n",
       "      <td>14.127276</td>\n",
       "      <td>19.079015</td>\n",
       "      <td>6.454849</td>\n",
       "      <td>19.054060</td>\n",
       "      <td>31.085150</td>\n",
       "      <td>15.175452</td>\n",
       "      <td>21.861600</td>\n",
       "      <td>38.290924</td>\n",
       "      <td>24.522839</td>\n",
       "      <td>4.250847</td>\n",
       "      <td>5.660208</td>\n",
       "      <td>7.977864</td>\n",
       "      <td>13.918032</td>\n",
       "      <td>17.930563</td>\n",
       "      <td>10.334027</td>\n",
       "      <td>19.450786</td>\n",
       "      <td>26.805241</td>\n",
       "      <td>12.623608</td>\n",
       "      <td>24.040729</td>\n",
       "      <td>34.119182</td>\n",
       "      <td>17.605140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18.788593</td>\n",
       "      <td>-47.219574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-45.582393</td>\n",
       "      <td>-60.541901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-64.980680</td>\n",
       "      <td>-66.381989</td>\n",
       "      <td>-11.942673</td>\n",
       "      <td>-85.509727</td>\n",
       "      <td>-81.291138</td>\n",
       "      <td>-44.110306</td>\n",
       "      <td>-107.789253</td>\n",
       "      <td>-91.676575</td>\n",
       "      <td>-66.343353</td>\n",
       "      <td>-35.445511</td>\n",
       "      <td>-20.730328</td>\n",
       "      <td>-32.883482</td>\n",
       "      <td>-71.428963</td>\n",
       "      <td>-58.541824</td>\n",
       "      <td>-73.250750</td>\n",
       "      <td>-92.259822</td>\n",
       "      <td>-77.042549</td>\n",
       "      <td>-96.800034</td>\n",
       "      <td>-107.166021</td>\n",
       "      <td>-91.046356</td>\n",
       "      <td>-113.517113</td>\n",
       "      <td>-22.855350</td>\n",
       "      <td>-12.155167</td>\n",
       "      <td>-25.658291</td>\n",
       "      <td>-56.705952</td>\n",
       "      <td>-56.844780</td>\n",
       "      <td>-73.379822</td>\n",
       "      <td>-75.681870</td>\n",
       "      <td>-79.146027</td>\n",
       "      <td>-101.507748</td>\n",
       "      <td>-89.119637</td>\n",
       "      <td>-95.912731</td>\n",
       "      <td>-120.184189</td>\n",
       "      <td>-7.060419</td>\n",
       "      <td>-11.240997</td>\n",
       "      <td>-26.847574</td>\n",
       "      <td>-35.160936</td>\n",
       "      <td>-49.839066</td>\n",
       "      <td>-68.887894</td>\n",
       "      <td>-52.210419</td>\n",
       "      <td>-70.960037</td>\n",
       "      <td>-95.784557</td>\n",
       "      <td>-64.453278</td>\n",
       "      <td>-88.702026</td>\n",
       "      <td>-113.748749</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18.891541</td>\n",
       "      <td>-37.043697</td>\n",
       "      <td>-17.501412</td>\n",
       "      <td>-48.466721</td>\n",
       "      <td>-69.166000</td>\n",
       "      <td>-17.062012</td>\n",
       "      <td>-65.865921</td>\n",
       "      <td>-88.387527</td>\n",
       "      <td>-26.224027</td>\n",
       "      <td>-79.350571</td>\n",
       "      <td>-105.164220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>5.303953</td>\n",
       "      <td>-16.154655</td>\n",
       "      <td>46.889400</td>\n",
       "      <td>-20.589817</td>\n",
       "      <td>-32.824753</td>\n",
       "      <td>48.385406</td>\n",
       "      <td>-53.283127</td>\n",
       "      <td>-29.053731</td>\n",
       "      <td>12.247231</td>\n",
       "      <td>-72.044868</td>\n",
       "      <td>-33.214859</td>\n",
       "      <td>-17.476803</td>\n",
       "      <td>-92.168205</td>\n",
       "      <td>-34.514648</td>\n",
       "      <td>-32.191772</td>\n",
       "      <td>-30.417233</td>\n",
       "      <td>1.752487</td>\n",
       "      <td>-25.074760</td>\n",
       "      <td>-39.611374</td>\n",
       "      <td>-10.477402</td>\n",
       "      <td>-65.477338</td>\n",
       "      <td>-41.510479</td>\n",
       "      <td>-24.806004</td>\n",
       "      <td>-88.591947</td>\n",
       "      <td>-41.272228</td>\n",
       "      <td>-35.928970</td>\n",
       "      <td>-104.791837</td>\n",
       "      <td>-10.184507</td>\n",
       "      <td>8.146667</td>\n",
       "      <td>-23.092530</td>\n",
       "      <td>-16.272113</td>\n",
       "      <td>1.670151</td>\n",
       "      <td>-68.976637</td>\n",
       "      <td>-18.042429</td>\n",
       "      <td>-10.197922</td>\n",
       "      <td>-96.536362</td>\n",
       "      <td>-17.905727</td>\n",
       "      <td>-20.966949</td>\n",
       "      <td>-114.569537</td>\n",
       "      <td>11.824230</td>\n",
       "      <td>10.128174</td>\n",
       "      <td>-19.518894</td>\n",
       "      <td>7.481028</td>\n",
       "      <td>-0.661336</td>\n",
       "      <td>-60.248833</td>\n",
       "      <td>5.463505</td>\n",
       "      <td>-17.096054</td>\n",
       "      <td>-86.111069</td>\n",
       "      <td>4.233183</td>\n",
       "      <td>-30.623734</td>\n",
       "      <td>-103.597824</td>\n",
       "      <td>32.578709</td>\n",
       "      <td>6.126282</td>\n",
       "      <td>-17.356735</td>\n",
       "      <td>37.071983</td>\n",
       "      <td>0.743622</td>\n",
       "      <td>-47.289053</td>\n",
       "      <td>37.879635</td>\n",
       "      <td>-7.458702</td>\n",
       "      <td>-62.019073</td>\n",
       "      <td>37.661507</td>\n",
       "      <td>-16.873043</td>\n",
       "      <td>-76.457085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>11.003603</td>\n",
       "      <td>-10.438423</td>\n",
       "      <td>51.089325</td>\n",
       "      <td>-14.393297</td>\n",
       "      <td>-26.846329</td>\n",
       "      <td>51.974277</td>\n",
       "      <td>-48.050100</td>\n",
       "      <td>-24.186591</td>\n",
       "      <td>18.188843</td>\n",
       "      <td>-66.187424</td>\n",
       "      <td>-22.981628</td>\n",
       "      <td>-10.080578</td>\n",
       "      <td>-85.591614</td>\n",
       "      <td>-20.921448</td>\n",
       "      <td>-23.494066</td>\n",
       "      <td>-26.713697</td>\n",
       "      <td>4.766418</td>\n",
       "      <td>-21.933835</td>\n",
       "      <td>-29.365269</td>\n",
       "      <td>5.060577</td>\n",
       "      <td>-60.440456</td>\n",
       "      <td>-29.321157</td>\n",
       "      <td>1.868774</td>\n",
       "      <td>-82.658916</td>\n",
       "      <td>-28.242832</td>\n",
       "      <td>-2.149460</td>\n",
       "      <td>-98.060318</td>\n",
       "      <td>-5.757006</td>\n",
       "      <td>10.816483</td>\n",
       "      <td>-21.689735</td>\n",
       "      <td>-3.943844</td>\n",
       "      <td>15.231567</td>\n",
       "      <td>-65.868057</td>\n",
       "      <td>-2.491593</td>\n",
       "      <td>13.786426</td>\n",
       "      <td>-92.389818</td>\n",
       "      <td>-1.259846</td>\n",
       "      <td>10.905167</td>\n",
       "      <td>-109.865616</td>\n",
       "      <td>15.951729</td>\n",
       "      <td>12.922211</td>\n",
       "      <td>-16.346840</td>\n",
       "      <td>19.878994</td>\n",
       "      <td>15.531101</td>\n",
       "      <td>-57.141853</td>\n",
       "      <td>21.515116</td>\n",
       "      <td>12.985641</td>\n",
       "      <td>-82.397789</td>\n",
       "      <td>22.062859</td>\n",
       "      <td>8.935730</td>\n",
       "      <td>-99.326035</td>\n",
       "      <td>36.144882</td>\n",
       "      <td>9.758530</td>\n",
       "      <td>-10.091713</td>\n",
       "      <td>47.538086</td>\n",
       "      <td>13.103134</td>\n",
       "      <td>-38.711935</td>\n",
       "      <td>53.119721</td>\n",
       "      <td>13.267067</td>\n",
       "      <td>-53.230655</td>\n",
       "      <td>57.554037</td>\n",
       "      <td>12.190862</td>\n",
       "      <td>-66.381577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>21.007404</td>\n",
       "      <td>-4.331512</td>\n",
       "      <td>53.096467</td>\n",
       "      <td>-2.977227</td>\n",
       "      <td>-19.848022</td>\n",
       "      <td>55.241539</td>\n",
       "      <td>-39.346478</td>\n",
       "      <td>-19.819931</td>\n",
       "      <td>23.961493</td>\n",
       "      <td>-56.279068</td>\n",
       "      <td>-15.676071</td>\n",
       "      <td>-1.889607</td>\n",
       "      <td>-73.255493</td>\n",
       "      <td>-11.246796</td>\n",
       "      <td>-13.354761</td>\n",
       "      <td>-24.231110</td>\n",
       "      <td>7.710999</td>\n",
       "      <td>-16.440654</td>\n",
       "      <td>-20.888549</td>\n",
       "      <td>15.740051</td>\n",
       "      <td>-48.319103</td>\n",
       "      <td>-17.828548</td>\n",
       "      <td>18.006699</td>\n",
       "      <td>-61.762764</td>\n",
       "      <td>-15.246248</td>\n",
       "      <td>17.932541</td>\n",
       "      <td>-69.928131</td>\n",
       "      <td>-3.128247</td>\n",
       "      <td>13.223108</td>\n",
       "      <td>-19.812828</td>\n",
       "      <td>3.724144</td>\n",
       "      <td>24.981990</td>\n",
       "      <td>-59.579098</td>\n",
       "      <td>7.962442</td>\n",
       "      <td>29.397827</td>\n",
       "      <td>-80.972992</td>\n",
       "      <td>10.796032</td>\n",
       "      <td>30.455948</td>\n",
       "      <td>-94.711868</td>\n",
       "      <td>18.105104</td>\n",
       "      <td>15.275167</td>\n",
       "      <td>-14.126438</td>\n",
       "      <td>26.747138</td>\n",
       "      <td>24.869614</td>\n",
       "      <td>-53.152382</td>\n",
       "      <td>31.681546</td>\n",
       "      <td>28.137431</td>\n",
       "      <td>-73.362469</td>\n",
       "      <td>34.938942</td>\n",
       "      <td>28.561951</td>\n",
       "      <td>-86.205826</td>\n",
       "      <td>37.635713</td>\n",
       "      <td>12.965748</td>\n",
       "      <td>-6.368507</td>\n",
       "      <td>55.159328</td>\n",
       "      <td>24.085464</td>\n",
       "      <td>-32.782483</td>\n",
       "      <td>64.440407</td>\n",
       "      <td>29.132584</td>\n",
       "      <td>-46.016613</td>\n",
       "      <td>72.337128</td>\n",
       "      <td>32.429928</td>\n",
       "      <td>-56.287450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>1332.000000</td>\n",
       "      <td>1447.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1079.000000</td>\n",
       "      <td>1063.000000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>1634.000000</td>\n",
       "      <td>49.439873</td>\n",
       "      <td>36.358187</td>\n",
       "      <td>54.706200</td>\n",
       "      <td>37.963276</td>\n",
       "      <td>31.215271</td>\n",
       "      <td>61.437187</td>\n",
       "      <td>10.016212</td>\n",
       "      <td>9.029129</td>\n",
       "      <td>61.060345</td>\n",
       "      <td>18.761269</td>\n",
       "      <td>20.109650</td>\n",
       "      <td>65.461411</td>\n",
       "      <td>32.782349</td>\n",
       "      <td>35.151642</td>\n",
       "      <td>77.677128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.598724</td>\n",
       "      <td>13.875271</td>\n",
       "      <td>4.325998</td>\n",
       "      <td>60.300873</td>\n",
       "      <td>18.866871</td>\n",
       "      <td>17.130154</td>\n",
       "      <td>80.803131</td>\n",
       "      <td>31.765396</td>\n",
       "      <td>26.427204</td>\n",
       "      <td>94.990204</td>\n",
       "      <td>39.135193</td>\n",
       "      <td>9.332176</td>\n",
       "      <td>24.170898</td>\n",
       "      <td>4.815260</td>\n",
       "      <td>35.127131</td>\n",
       "      <td>67.003217</td>\n",
       "      <td>9.368416</td>\n",
       "      <td>50.308007</td>\n",
       "      <td>92.991241</td>\n",
       "      <td>28.075127</td>\n",
       "      <td>60.261546</td>\n",
       "      <td>110.145111</td>\n",
       "      <td>38.281386</td>\n",
       "      <td>25.640804</td>\n",
       "      <td>26.513692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.079788</td>\n",
       "      <td>63.983246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.581345</td>\n",
       "      <td>83.103333</td>\n",
       "      <td>16.840763</td>\n",
       "      <td>81.039490</td>\n",
       "      <td>97.762779</td>\n",
       "      <td>31.617199</td>\n",
       "      <td>39.583787</td>\n",
       "      <td>35.336060</td>\n",
       "      <td>12.044017</td>\n",
       "      <td>70.601374</td>\n",
       "      <td>61.324524</td>\n",
       "      <td>3.157291</td>\n",
       "      <td>87.275681</td>\n",
       "      <td>77.416107</td>\n",
       "      <td>13.661100</td>\n",
       "      <td>101.493103</td>\n",
       "      <td>90.416443</td>\n",
       "      <td>29.096052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ch1            ch2            ch3            ch4  \\\n",
       "count  410705.000000  410705.000000  410705.000000  410705.000000   \n",
       "mean       69.830650     194.624456     327.642768      71.728214   \n",
       "std        52.362277     117.678324     202.985681      44.448922   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        41.000000     115.000000     170.000000      44.000000   \n",
       "50%        55.000000     191.000000     335.000000      60.000000   \n",
       "75%        81.000000     260.000000     465.000000      84.000000   \n",
       "max      1373.000000    1332.000000    1447.000000    1197.000000   \n",
       "\n",
       "                 ch5            ch6            ch7            ch8  \\\n",
       "count  410705.000000  410705.000000  410705.000000  410705.000000   \n",
       "mean       44.296870      87.210112      73.443849      95.129351   \n",
       "std        36.061338     141.084668      73.084319      68.073366   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        28.000000      24.000000      29.000000      42.000000   \n",
       "50%        32.000000      27.000000      46.000000      78.000000   \n",
       "75%        40.000000      47.000000      91.000000     130.000000   \n",
       "max      1079.000000    1063.000000    1390.000000    1634.000000   \n",
       "\n",
       "             Wrist x        Wrist y        Wrist z  Thumb Proximal x  \\\n",
       "count  410705.000000  410705.000000  410705.000000     410705.000000   \n",
       "mean       13.433740      -9.918895      49.406393        -11.376644   \n",
       "std        11.101877       9.608523       5.131227         12.637440   \n",
       "min       -18.788593     -47.219574       0.000000        -45.582393   \n",
       "25%         5.303953     -16.154655      46.889400        -20.589817   \n",
       "50%        11.003603     -10.438423      51.089325        -14.393297   \n",
       "75%        21.007404      -4.331512      53.096467         -2.977227   \n",
       "max        49.439873      36.358187      54.706200         37.963276   \n",
       "\n",
       "       Thumb Proximal y  Thumb Proximal z  Thumb Intermediate x  \\\n",
       "count     410705.000000     410705.000000         410705.000000   \n",
       "mean         -25.605264         51.338970            -44.872333   \n",
       "std           11.062260          5.787927             11.626375   \n",
       "min          -60.541901          0.000000            -64.980680   \n",
       "25%          -32.824753         48.385406            -53.283127   \n",
       "50%          -26.846329         51.974277            -48.050100   \n",
       "75%          -19.848022         55.241539            -39.346478   \n",
       "max           31.215271         61.437187             10.016212   \n",
       "\n",
       "       Thumb Intermediate y  Thumb Intermediate z  Thumb Distal x  \\\n",
       "count         410705.000000         410705.000000   410705.000000   \n",
       "mean             -24.645973             18.824727      -61.431829   \n",
       "std                7.278401             10.328375       15.079041   \n",
       "min              -66.381989            -11.942673      -85.509727   \n",
       "25%              -29.053731             12.247231      -72.044868   \n",
       "50%              -24.186591             18.188843      -66.187424   \n",
       "75%              -19.819931             23.961493      -56.279068   \n",
       "max                9.029129             61.060345       18.761269   \n",
       "\n",
       "       Thumb Distal y  Thumb Distal z    Thumb Tip x    Thumb Tip y  \\\n",
       "count   410705.000000   410705.000000  410705.000000  410705.000000   \n",
       "mean       -24.770864       -7.855166     -77.766978     -23.818628   \n",
       "std         12.786336       15.241664      21.838932      17.859002   \n",
       "min        -81.291138      -44.110306    -107.789253     -91.676575   \n",
       "25%        -33.214859      -17.476803     -92.168205     -34.514648   \n",
       "50%        -22.981628      -10.080578     -85.591614     -20.921448   \n",
       "75%        -15.676071       -1.889607     -73.255493     -11.246796   \n",
       "max         20.109650       65.461411      32.782349      35.151642   \n",
       "\n",
       "         Thumb Tip z  Index Proximal x  Index Proximal y  Index Proximal z  \\\n",
       "count  410705.000000     410705.000000     410705.000000     410705.000000   \n",
       "mean      -20.041394        -27.111190          4.681878        -20.149181   \n",
       "std        19.772263          4.206371          4.495053          6.532463   \n",
       "min       -66.343353        -35.445511        -20.730328        -32.883482   \n",
       "25%       -32.191772        -30.417233          1.752487        -25.074760   \n",
       "50%       -23.494066        -26.713697          4.766418        -21.933835   \n",
       "75%       -13.354761        -24.231110          7.710999        -16.440654   \n",
       "max        77.677128          0.000000         24.598724         13.875271   \n",
       "\n",
       "       Index Intermediate x  Index Intermediate y  Index Intermediate z  \\\n",
       "count         410705.000000         410705.000000         410705.000000   \n",
       "mean             -31.168010              2.216711            -56.533035   \n",
       "std               13.169467             20.142673             11.303510   \n",
       "min              -71.428963            -58.541824            -73.250750   \n",
       "25%              -39.611374            -10.477402            -65.477338   \n",
       "50%              -29.365269              5.060577            -60.440456   \n",
       "75%              -20.888549             15.740051            -48.319103   \n",
       "max                4.325998             60.300873             18.866871   \n",
       "\n",
       "       Index Distal x  Index Distal y  Index Distal z    Index Tip x  \\\n",
       "count   410705.000000   410705.000000   410705.000000  410705.000000   \n",
       "mean       -31.400229       -3.194320      -73.436094     -30.786220   \n",
       "std         17.535305       30.724830       20.110927      20.610897   \n",
       "min        -92.259822      -77.042549      -96.800034    -107.166021   \n",
       "25%        -41.510479      -24.806004      -88.591947     -41.272228   \n",
       "50%        -29.321157        1.868774      -82.658916     -28.242832   \n",
       "75%        -17.828548       18.006699      -61.762764     -15.246248   \n",
       "max         17.130154       80.803131       31.765396      26.427204   \n",
       "\n",
       "         Index Tip y    Index Tip z  Middle Proximal x  Middle Proximal y  \\\n",
       "count  410705.000000  410705.000000      410705.000000      410705.000000   \n",
       "mean       -7.652179     -83.590116          -6.693581          10.463009   \n",
       "std        36.878668      29.311201           4.934637           4.187734   \n",
       "min       -91.046356    -113.517113         -22.855350         -12.155167   \n",
       "25%       -35.928970    -104.791837         -10.184507           8.146667   \n",
       "50%        -2.149460     -98.060318          -5.757006          10.816483   \n",
       "75%        17.932541     -69.928131          -3.128247          13.223108   \n",
       "max        94.990204      39.135193           9.332176          24.170898   \n",
       "\n",
       "       Middle Proximal z  Middle Intermediate x  Middle Intermediate y  \\\n",
       "count      410705.000000          410705.000000          410705.000000   \n",
       "mean          -21.165028              -6.515101              11.520539   \n",
       "std             2.722228              14.825448              20.637099   \n",
       "min           -25.658291             -56.705952             -56.844780   \n",
       "25%           -23.092530             -16.272113               1.670151   \n",
       "50%           -21.689735              -3.943844              15.231567   \n",
       "75%           -19.812828               3.724144              24.981990   \n",
       "max             4.815260              35.127131              67.003217   \n",
       "\n",
       "       Middle Intermediate z  Middle Distal x  Middle Distal y  \\\n",
       "count          410705.000000    410705.000000    410705.000000   \n",
       "mean              -63.206372        -5.261173         7.249119   \n",
       "std                 8.213241        19.314485        33.083783   \n",
       "min               -73.379822       -75.681870       -79.146027   \n",
       "25%               -68.976637       -18.042429       -10.197922   \n",
       "50%               -65.868057        -2.491593        13.786426   \n",
       "75%               -59.579098         7.962442        29.397827   \n",
       "max                 9.368416        50.308007        92.991241   \n",
       "\n",
       "       Middle Distal z   Middle Tip x   Middle Tip y   Middle Tip z  \\\n",
       "count    410705.000000  410705.000000  410705.000000  410705.000000   \n",
       "mean        -84.549727      -3.946629       3.396960     -96.981908   \n",
       "std          18.344533      21.852345      39.955083      27.991373   \n",
       "min        -101.507748     -89.119637     -95.912731    -120.184189   \n",
       "25%         -96.536362     -17.905727     -20.966949    -114.569537   \n",
       "50%         -92.389818      -1.259846      10.905167    -109.865616   \n",
       "75%         -80.972992      10.796032      30.455948     -94.711868   \n",
       "max          28.075127      60.261546     110.145111      38.281386   \n",
       "\n",
       "       Ring Proximal x  Ring Proximal y  Ring Proximal z  Ring Intermediate x  \\\n",
       "count    410705.000000    410705.000000    410705.000000        410705.000000   \n",
       "mean         14.726588        12.407608       -16.884533            16.900805   \n",
       "std           4.647071         4.449165         4.145501            14.127276   \n",
       "min          -7.060419       -11.240997       -26.847574           -35.160936   \n",
       "25%          11.824230        10.128174       -19.518894             7.481028   \n",
       "50%          15.951729        12.922211       -16.346840            19.878994   \n",
       "75%          18.105104        15.275167       -14.126438            26.747138   \n",
       "max          25.640804        26.513692         0.000000            54.079788   \n",
       "\n",
       "       Ring Intermediate y  Ring Intermediate z  Ring Distal x  Ring Distal y  \\\n",
       "count        410705.000000        410705.000000  410705.000000  410705.000000   \n",
       "mean             11.061808           -56.082492      18.437839       4.973580   \n",
       "std              19.079015             6.454849      19.054060      31.085150   \n",
       "min             -49.839066           -68.887894     -52.210419     -70.960037   \n",
       "25%              -0.661336           -60.248833       5.463505     -17.096054   \n",
       "50%              15.531101           -57.141853      21.515116      12.985641   \n",
       "75%              24.869614           -53.152382      31.681546      28.137431   \n",
       "max              63.983246             0.000000      70.581345      83.103333   \n",
       "\n",
       "       Ring Distal z     Ring Tip x     Ring Tip y     Ring Tip z  \\\n",
       "count  410705.000000  410705.000000  410705.000000  410705.000000   \n",
       "mean      -76.538139      19.668363      -0.382871     -88.293062   \n",
       "std        15.175452      21.861600      38.290924      24.522839   \n",
       "min       -95.784557     -64.453278     -88.702026    -113.748749   \n",
       "25%       -86.111069       4.233183     -30.623734    -103.597824   \n",
       "50%       -82.397789      22.062859       8.935730     -99.326035   \n",
       "75%       -73.362469      34.938942      28.561951     -86.205826   \n",
       "max        16.840763      81.039490      97.762779      31.617199   \n",
       "\n",
       "       Pinky Proximal x  Pinky Proximal y  Pinky Proximal z  \\\n",
       "count     410705.000000     410705.000000     410705.000000   \n",
       "mean          34.624458          9.507856        -12.152019   \n",
       "std            4.250847          5.660208          7.977864   \n",
       "min            0.000000        -18.891541        -37.043697   \n",
       "25%           32.578709          6.126282        -17.356735   \n",
       "50%           36.144882          9.758530        -10.091713   \n",
       "75%           37.635713         12.965748         -6.368507   \n",
       "max           39.583787         35.336060         12.044017   \n",
       "\n",
       "       Pinky Intermediate x  Pinky Intermediate y  Pinky Intermediate z  \\\n",
       "count         410705.000000         410705.000000         410705.000000   \n",
       "mean              44.629017             11.456136            -40.051041   \n",
       "std               13.918032             17.930563             10.334027   \n",
       "min              -17.501412            -48.466721            -69.166000   \n",
       "25%               37.071983              0.743622            -47.289053   \n",
       "50%               47.538086             13.103134            -38.711935   \n",
       "75%               55.159328             24.085464            -32.782483   \n",
       "max               70.601374             61.324524              3.157291   \n",
       "\n",
       "       Pinky Distal x  Pinky Distal y  Pinky Distal z    Pinky Tip x  \\\n",
       "count   410705.000000   410705.000000   410705.000000  410705.000000   \n",
       "mean        49.475038        9.713231      -53.828391      53.471617   \n",
       "std         19.450786       26.805241       12.623608      24.040729   \n",
       "min        -17.062012      -65.865921      -88.387527     -26.224027   \n",
       "25%         37.879635       -7.458702      -62.019073      37.661507   \n",
       "50%         53.119721       13.267067      -53.230655      57.554037   \n",
       "75%         64.440407       29.132584      -46.016613      72.337128   \n",
       "max         87.275681       77.416107       13.661100     101.493103   \n",
       "\n",
       "         Pinky Tip y    Pinky Tip z  \n",
       "count  410705.000000  410705.000000  \n",
       "mean        7.140140     -64.891697  \n",
       "std        34.119182      17.605140  \n",
       "min       -79.350571    -105.164220  \n",
       "25%       -16.873043     -76.457085  \n",
       "50%        12.190862     -66.381577  \n",
       "75%        32.429928     -56.287450  \n",
       "max        90.416443      29.096052  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_ar = df.loc[:, 'ch1':'ch8'].values\n",
    "label_ar = df.loc[:, 'Wrist x':].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(410705, 8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_ar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(410705, 63)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_ar.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above evaluation the initial untrained loss is around 2500.\n",
    "Now train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(410674, 32, 8)\n",
      "(410674, 63)\n"
     ]
    }
   ],
   "source": [
    "seq_length = 32\n",
    "\n",
    "def overlap_samples(seq_length, feats, labels):\n",
    "    new_l = labels[seq_length - 1:]\n",
    "    feat_list = [feats[i:i + seq_length] for i in range(feats.shape[0] - seq_length + 1)]\n",
    "    new_f = np.array(feat_list)\n",
    "    return new_f, new_l\n",
    "\n",
    "features, labels = overlap_samples(seq_length, feature_ar, label_ar)\n",
    "print(features.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FC Architecture (Small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 328539 samples, validate on 82135 samples\n",
      "Epoch 1/5\n",
      "328539/328539 [==============================] - 126s 384us/sample - loss: 1086.0460 - val_loss: 837.8746\n",
      "Epoch 2/5\n",
      "328539/328539 [==============================] - 123s 374us/sample - loss: 369.8105 - val_loss: 1024.4610\n",
      "Epoch 3/5\n",
      "328539/328539 [==============================] - 124s 378us/sample - loss: 336.6834 - val_loss: 4527.5117\n",
      "Epoch 4/5\n",
      "328539/328539 [==============================] - 130s 394us/sample - loss: 332.7974 - val_loss: 48775.4029\n",
      "Epoch 5/5\n",
      "328539/328539 [==============================] - 127s 387us/sample - loss: 311.2259 - val_loss: 10696.3956\n"
     ]
    }
   ],
   "source": [
    "model_fc = tf.keras.models.Sequential()\n",
    "model_fc.add(LSTM(64, return_sequences=True, input_shape=(seq_length, 8)))\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(LSTM(64))\n",
    "model_fc.add(BatchNormalization())\n",
    "model_fc.add(Dense(100, input_dim=64))\n",
    "model_fc.add(Activation('relu'))\n",
    "model_fc.add(BatchNormalization())\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(Dense(64, input_dim=64))\n",
    "model_fc.add(Activation('relu'))\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(Dense(63, input_dim=64))\n",
    "\n",
    "model_fc.compile(optimizer='Adam', loss='mse')\n",
    "\n",
    "history = model_fc.fit(features, labels, batch_size=2048, epochs=5, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 32, 64)            18688     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32, 64)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                33024     \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               6500      \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 63)                4095      \n",
      "=================================================================\n",
      "Total params: 69,427\n",
      "Trainable params: 69,099\n",
      "Non-trainable params: 328\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_fc.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Model Error\n",
    "A similar visualizion is now done on this new more restricted model. These errors show 20% - 25% improvement on fingertip y position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x150d4a0f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.legend(['Train', 'Test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model_fc.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = labels - preds\n",
    "abs_error = np.absolute(error)\n",
    "avg_error = np.mean(abs_error, axis=0)\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Prediction Error (mm)')\n",
    "bar = plt.bar(df.columns[8:], avg_error)\n",
    "for i in range(0,63,3):\n",
    "    bar[i].set_color('coral')\n",
    "    bar[i+1].set_color('olivedrab')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Large FC Model Architecture\n",
    "Here a much larger model architecture is implemented to understand if this will improve results on the larger dataset. This dataset is approx 650MB, thus a larger model with proportionally higher parameters is expected to be required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fc = tf.keras.models.Sequential()\n",
    "model_fc.add(LSTM(256, return_sequences=True, input_shape=(seq_length, 8)))\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(LSTM(256, return_sequences=True))\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(LSTM(128))\n",
    "model_fc.add(BatchNormalization())\n",
    "model_fc.add(Dense(512, input_dim=128))\n",
    "model_fc.add(Activation('relu'))\n",
    "model_fc.add(BatchNormalization())\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(Dense(512, input_dim=512))\n",
    "model_fc.add(Activation('relu'))\n",
    "model_fc.add(BatchNormalization())\n",
    "model_fc.add(Dropout(0.5))\n",
    "model_fc.add(Dense(256, input_dim=512))\n",
    "model_fc.add(Activation('relu'))\n",
    "model_fc.add(Dropout(0.3))\n",
    "model_fc.add(Dense(63, input_dim=64))\n",
    "    \n",
    "model_fc.compile(optimizer='Adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 32, 256)           271360    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 32, 256)           0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 32, 256)           525312    \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 32, 256)           0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 128)               197120    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               66048     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 63)                16191     \n",
      "=================================================================\n",
      "Total params: 1,474,623\n",
      "Trainable params: 1,472,319\n",
      "Non-trainable params: 2,304\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_fc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 328539 samples, validate on 82135 samples\n",
      "Epoch 1/4\n",
      "328539/328539 [==============================] - 1013s 3ms/sample - loss: 290.4243 - val_loss: 665.9080\n",
      "Epoch 2/4\n",
      "328539/328539 [==============================] - 985s 3ms/sample - loss: 124.9392 - val_loss: 494.8746\n",
      "Epoch 3/4\n",
      "328539/328539 [==============================] - 1010s 3ms/sample - loss: 103.4529 - val_loss: 472.6404\n",
      "Epoch 4/4\n",
      "328539/328539 [==============================] - 976s 3ms/sample - loss: 93.0157 - val_loss: 506.4118\n"
     ]
    }
   ],
   "source": [
    "history = model_fc.fit(features, labels, batch_size=512, epochs=4, verbose=1, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14aacdeb8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.legend(['Train', 'Test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model_fc.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = labels - preds\n",
    "abs_error = np.absolute(error)\n",
    "avg_error = np.mean(abs_error, axis=0)\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Prediction Error (mm)')\n",
    "bar = plt.bar(df.columns[8:], avg_error)\n",
    "for i in range(0,63,3):\n",
    "    bar[i].set_color('coral')\n",
    "    bar[i+1].set_color('olivedrab')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r2_score(v_true, v_pred):\n",
    "    ssres = np.sum(np.square(v_true - v_pred))\n",
    "    sstot = np.sum(np.square(v_true - np.mean(v_true)))\n",
    "    return 1 - ssres / sstot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9096615419579054"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fc.save('FC_mega_dataset_all_joints_model.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}