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--- |
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license: cc-by-4.0 |
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task_categories: |
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- time-series-forecasting |
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- tabular-classification |
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- feature-extraction |
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tags: |
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- sensors |
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- time-series |
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- human-activity-recognition |
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- wearable |
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- imu |
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- accelerometer |
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pretty_name: SensorData |
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size_categories: |
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- 100M<n<1G |
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--- |
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# SensorData for Human Activity Recognition (HAR) |
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This repository contains pre-processed sensor datasets based on the original datasets from [SSL-Wearables](https://github.com/OxWearables/ssl-wearables). |
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Please refer to the SSL-Wearables repository for the original data sources ([here](https://zenodo.org/records/6574265#.YovCMi8w1qs)). |
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## Dataset Description |
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The collection includes several widely used datasets in the field of ubiquitous computing and wearable sensing. |
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These datasets have been pre-processed to align with the experimental settings of recent state-of-the-art methods. |
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- **Repository:** [sha-ce/SensorData](https://huggingface.co/datasets/sha-ce/SensorData) |
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- **Paper:** (paper url) |
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- **Point of Contact:** (Name/Email) |
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### Supported Tasks |
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- **Human Activity Recognition (HAR):** Classification of human activities based on wearable sensor data (accelerometer). |
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- **Text-to-Signal Generation:** Evaluating generative models on sensor data. |
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## Included Datasets |
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Based on the benchmarks, this repository includes data from the following sources: |
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1. **ADL (MotionSense/MobiAct):** Activities of Daily Living recorded from smartphone sensors. |
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2. **Opportunity:** Activities of daily living recorded in a sensor-rich environment, focusing on gestures and object interactions. |
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3. **PAMAP2:** Physical Activity Monitoring dataset containing data from 9 subjects performing 18 different physical activities. |
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4. **RealWorld (HAR):** Acceleration data collected from smartphones/wearables in realistic settings. |
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5. **WISDM:** Wireless Sensor Data Mining dataset containing accelerometer data for activity recognition. |
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## Dataset Structure |
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### Data Format |
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The data is likely stored in `.npy`, or `.npz` formats. |
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- **Input Features:** Multi-channel time-series data (e.g., 3-axis accelerometer). |
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- **Labels:** Integer class labels corresponding to specific activities (e.g., Walking, Running, Sitting). |
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### Usage |
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You can download the files directly or use the Hugging Face `huggingface_hub` library to download specific datasets. |
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```python |
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from huggingface_hub import hf_hub_download |
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import numpy as np |
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# Example: Downloading PAMAP2 data |
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file_path = hf_hub_download(repo_id="sha-ce/SensorData", filename="pamap2.npz") # Adjust filename |
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data = np.load(file_path) |
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print(data.files) |
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# Output might be: ['x_train', 'y_train', 'x_test', 'y_test'] |
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``` |
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or |
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```bash |
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hf download sha-ce/SensorData --repo-type dataset --local-dir . |
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``` |
