IM-SportingBehaviors

IM-SportingBehaviors Dataset

Dataset Overview

The IM-SportingBehaviors dataset, developed by researchers at Air University Pakistan, provides detailed motion data from participants engaged in various sports activities. This dataset captures human movement through triaxial accelerometers attached to multiple parts of the body, specifically the knee, wrist, and below-neck areas. The dataset includes motion data from six sports: cycling, badminton, skipping, basketball, football, and table tennis, with participants comprising both professional and amateur athletes aged between 20 and 30 years, weighing between 60 and 100 kilograms.

Dataset Characteristics

• Total Samples: 62,500
• Sports Activities: Cycling, badminton, skipping, basketball, football, and table tennis
• Data Imbalance: The dataset is imbalanced, with the ‘Skipping’ class as the most represented (22.4%) and ‘Football’ as the least represented (12.3%).
• Sensors Used: Triaxial accelerometers providing 9-dimensional data from three sensor placements; however, only 6-dimensional data is utilized in analyses, corresponding to two accelerometers.
• Data Distribution:
• Skipping: 22.4%
• Cycling: 19%
• Table Tennis: 16.8%
• Badminton: 16%
• Basketball: 13.4%
• Football: 12.3%

Motivation and Summary of Content

The IM-SportingBehaviors dataset aims to enhance the understanding of human mobility patterns and biomechanics across a range of sports. By collecting and analyzing motion data through wearable accelerometers, researchers can gain insights into activity-specific movement dynamics and variability in performance between professional and amateur athletes. This dataset's detailed sensor data captures essential aspects of motion and posture, which are crucial for applications in sports science, human-computer interaction, and motion analysis.

Potential Use Cases

The IM-SportingBehaviors dataset is valuable for: 1. Activity Recognition: Developing and validating algorithms to classify sports activities based on sensor data. 2. Performance Analysis: Analyzing movement efficiency and style differences between professional and amateur athletes. 3. Biomechanics Research: Studying human movement patterns, energy expenditure, and motion efficiency in various sports. 4. Wearable Device Optimization: Improving the accuracy of wearable fitness trackers in recognizing and categorizing sports-specific movements. 5. Injury Prevention: Identifying movement patterns or deviations that could contribute to sports-related injuries.

This dataset provides a robust foundation for advancing both theoretical and applied research in fields that depend on accurate motion and activity classification.