---
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 .
```