Tarakeshwaran/Hackathon-Dataset_Round_2

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:

from datasets import load_dataset

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