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---
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:
```python
from datasets import load_dataset
dataset = load_dataset("Tarakeshwaran/Hackathon-Dataset_Round_2_test")
print(dataset) |