File size: 2,712 Bytes
69043a7 a77e084 69043a7 04e1818 69043a7 04e1818 69043a7 04e1818 69043a7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: cc-by-4.0
task_categories:
- time-series-forecasting
- tabular-classification
- feature-extraction
tags:
- sensors
- time-series
- human-activity-recognition
- wearable
- imu
- accelerometer
pretty_name: SensorData
size_categories:
- 100M<n<1G
---
# SensorData for Human Activity Recognition (HAR)
This repository contains pre-processed sensor datasets based on the original datasets from [SSL-Wearables](https://github.com/OxWearables/ssl-wearables).
Please refer to the SSL-Wearables repository for the original data sources ([here](https://zenodo.org/records/6574265#.YovCMi8w1qs)).
## Dataset Description
The collection includes several widely used datasets in the field of ubiquitous computing and wearable sensing.
These datasets have been pre-processed to align with the experimental settings of recent state-of-the-art methods.
- **Repository:** [sha-ce/SensorData](https://huggingface.co/datasets/sha-ce/SensorData)
- **Paper:** (paper url)
- **Point of Contact:** (Name/Email)
### Supported Tasks
- **Human Activity Recognition (HAR):** Classification of human activities based on wearable sensor data (accelerometer).
- **Text-to-Signal Generation:** Evaluating generative models on sensor data.
## Included Datasets
Based on the benchmarks, this repository includes data from the following sources:
1. **ADL (MotionSense/MobiAct):** Activities of Daily Living recorded from smartphone sensors.
2. **Opportunity:** Activities of daily living recorded in a sensor-rich environment, focusing on gestures and object interactions.
3. **PAMAP2:** Physical Activity Monitoring dataset containing data from 9 subjects performing 18 different physical activities.
4. **RealWorld (HAR):** Acceleration data collected from smartphones/wearables in realistic settings.
5. **WISDM:** Wireless Sensor Data Mining dataset containing accelerometer data for activity recognition.
## Dataset Structure
### Data Format
The data is likely stored in `.npy`, or `.npz` formats.
- **Input Features:** Multi-channel time-series data (e.g., 3-axis accelerometer).
- **Labels:** Integer class labels corresponding to specific activities (e.g., Walking, Running, Sitting).
### Usage
You can download the files directly or use the Hugging Face `huggingface_hub` library to download specific datasets.
```python
from huggingface_hub import hf_hub_download
import numpy as np
# Example: Downloading PAMAP2 data
file_path = hf_hub_download(repo_id="sha-ce/SensorData", filename="pamap2.npz") # Adjust filename
data = np.load(file_path)
print(data.files)
# Output might be: ['x_train', 'y_train', 'x_test', 'y_test']
```
or
```bash
hf download sha-ce/SensorData --repo-type dataset --local-dir .
``` |