To download the datasets used in this project:
Processed datasets for atomic activities (Optional):
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The IMU dataset (ESC-50 + Kitchen20): https://drive.google.com/drive/folders/1K65vnkCIdqmULeFmgJvmjzneuZey6LAq?usp=drive_link.
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The audio dataset (WISDM): https://drive.google.com/drive/folders/1WOlDvCEP5QiqrrmTEWn4F-eJHV0Drjtn?usp=sharing.
Synthetic complex event dataset:
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The complex event dataset is synthesized using the atomic activity datasets above. Here is the definition of the complex events:
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Description of the dataset:
Consider a (
$data$ ,$label$ ) sample pair from the dataset:-
$data$ is a time series of embedding vectors, with dimension$60 \times 128$ . It contains 60 timestamps of embedding vectors, and each 128-dim embedding vector encodes a 5-second window of fused IMU and audio sensor data. Hence,$data$ corresponds to a 5-minute sensor data. -
$label$ is a time series of complex event labels, with dimension$60 \times 1$ . It contains 60 timestamps of complex event labels, with 0 referring to$e_0$ , 1 referring to$e_1$ , 2 referring to$e_2$ , and 3 referring to$e_3$ . Example: if at timestamp$t$ , the complex event label is$2$ , then it means complex event$e_2$ is detected to "happen" in previous$0 - t$ timestamps. Note that by "happen" we refer to the exact time when the complete event pattern of complex event$e_2$ is observed.
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To use the complex event dataset, simply download training data:
ce5min_train_data_{num_samples}.npy,ce5min_train_labels_{num_samples}.npy,ae2ce5min_train_data_{num_samples}.npy,ae2ce5min_train_labels_{num_samples}.npy, and test data:ce5min_test_data.npy,ce5min_test_labels.npy,ae2ce5min_test_data.npy,ae2ce5min_test_labels.npyfrom https://drive.google.com/drive/folders/1tATqHHPHJO9_f0fI-n4gBZtRVd1QPbX4?usp=drive_link. Herenum_samplesis the number of samples synthesized in the complex event dataset, you can choose values from4000,6000,8000, and10000.