AI-Based Footwork Pattern Detection and Its Impact on Response Time: An A-B Single-Case Study

Sudirman; Muhammad Qasash Hasyim; Muhammad Rizky Alfarizi

doi:10.55081/jphr.v6i1.5217

Sudirman Universitas Negeri Makassar
Muhammad Qasash Hasyim Universitas Negeri Makassar
Muhammad Rizky Alfarizi Universitas Negeri Makassar

DOI: https://doi.org/10.55081/jphr.v6i1.5217

Keywords: artificial intelligence, footwork detection, response time, single-case design, athletic performance, biomechanics

Abstract

Footwork patterns play a critical role in athletic performance, particularly in sports requiring rapid directional changes and agility. This single-case A-B experimental design study investigated the effectiveness of artificial intelligence-based footwork pattern detection systems in improving response time among athletes. The study involved one elite athlete participant engaged in a controlled environment over 12 weeks, comprising 6 weeks of baseline (A) assessment and 6 weeks of intervention (B) with AI-supported real-time feedback. Response time was measured using photographic timing systems, while footwork patterns were analyzed through computer vision algorithms. Results indicated a mean reduction in response time of 8.7% during the intervention phase compared to baseline, with visual analysis suggesting clinically meaningful improvement in pattern consistency. The findings suggest that AI-based footwork detection systems may serve as effective tools for enhancing athletic performance and response time through real-time biomechanical feedback. However, the single-case design limits generalizability, indicating the need for larger-scale studies to corroborate these findings. This research contributes to the growing literature on artificial intelligence applications in sports science and performance optimization.

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