metadata

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: 14440
      num_examples: 120
  download_size: 11081
  dataset_size: 14440
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:

from datasets import load_dataset

dataset = load_dataset("Tarakeshwaran/Hackathon-Dataset_Round_2_test")
print(dataset)