README.md

---
dataset_info:
  features:
  - name: F1
    dtype: int64
  - name: F2
    dtype: int64
  - name: F3
    dtype: int64
  - name: F4
    dtype: int64
  - name: F5
    dtype: int64
  - name: Acc_Fin_x
    dtype: int64
  - name: Acc_Fin_y
    dtype: int64
  - name: Acc_Fin_z
    dtype: int64
  - name: Acc_Palm_x
    dtype: int64
  - name: Acc_Palm_y
    dtype: int64
  - name: Acc_Palm_z
    dtype: int64
  - name: Acc_Arm_x
    dtype: int64
  - name: Acc_Arm_y
    dtype: int64
  - name: Acc_Arm_z
    dtype: int64
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 9086450
    num_examples: 75687
  download_size: 1481697
  dataset_size: 9086450
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Sensor-Based Motion Data Dataset  

## Description  
This dataset contains **sensor-based motion data** collected from multiple files, each representing different recording sessions. It captures acceleration readings from various body parts, making it valuable for **human activity recognition, biomechanics analysis, and motion classification**.  

## Dataset Details  

### **Columns:**  
- **F1, F2, F3, F4, F5** – Feature values representing signal intensities or raw sensor readings.  
- **Acc_Fin_x, Acc_Fin_y, Acc_Fin_z** – Accelerometer readings from the **fingers** in **x, y,** and **z** directions.  
- **Acc_Palm_x, Acc_Palm_y, Acc_Palm_z** – Accelerometer readings from the **palm** in **x, y,** and **z** directions.  
- **Acc_Arm_x, Acc_Arm_y, Acc_Arm_z** – Accelerometer readings from the **arm** in **x, y,** and **z** directions.  

### **Notes:**  
- The dataset consists of **multiple files**, each containing sensor readings over time.  
- Values are likely recorded at **a fixed sampling rate**, making the dataset useful for **time-series analysis**.  
- The dataset can be applied to **motion recognition, gesture classification,** and **biomechanical research**.  

## Use Cases  
- **Human activity recognition** – Classify different hand and arm movements.  
- **Gesture-based interface development** – Use motion data for interactive systems.  
- **Sports and rehabilitation analytics** – Analyze motion patterns for performance and recovery tracking.  
- **Machine learning applications** – Train models for predictive motion analysis.  

## How to Use  
You can load the dataset using the `datasets` library:  
```python
from datasets import load_dataset

dataset = load_dataset("Tarakeshwaran/Hackathon-Dataset_Round_2")
print(dataset)