Update README.md

added dataset description

README.md

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     path: data/train-*
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+# 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)