Update README.md

README.md

@@ -1,12 +1,71 @@
-We provide preprocessed datasets on Hugging Face based on the original datasets from [SSL-Wearables](https://github.com/OxWearables/ssl-wearables). 
+---
+license: cc-by-4.0
+task_categories:
+- Self-Supervised Learning
+- Human-Activity-Recognition
+- Text-to-Signal Generation
+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)).
 
-Download the datasets using the script below:
-```bash
-hf download sha-ce/SensorData --repo-type dataset --local-dir .
-```
+## 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)
 
----
-license: cc-by-4.0
----
+### 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 .
+```