Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Computer Science and Humanitarian AI
Mitta, Kalyana
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PoseTracker: Accuracy Evaluation of AI-Based Mobile Application for Exercise Posture Feedback Collhins, Billy; Mitta, Kalyana; Gunawan, Christian; Manalu, Sonya Rapinta
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15123

Abstract

In recent years, the rising of public health awareness has increased fitness activities participation. However, improper exercise form remains a significant contributor to injuries, particularly in unsupervised environments. To address this, PoseTracker’s accuracy was evaluated as a native Android application that provides real time feedback on exercise posture through MediaPipe based Human Pose Estimation (HPE) model. The system extracts 33 3D body landmarks, normalizes them to account for body scale, and employs cosine similarity to compare user movements against a reference dataset. Evaluations involving participants aged between 17 to 50 years old and 240 repetitions across four exercises demonstrated high detection accuracy: 88.33% for jumping jacks, 85% for squats, 83.33% for push-ups and 82% for sit ups. While performance can be influenced by environmental factors such as inconsistent lighting, camera positioning and incomplete body visibility, these results highlight the potential for lightweight, AI driven tools to support safe and self-guided fitness routines. Overall, the evaluations indicate that PoseTracker achieves reliable detection accuracy in distinguishing correct and incorrect exercise posture across multiple movement types under realistic conditions. Although performance variability exists due to environmental and system constraints, the accuracy levels observed demonstrate the feasibility of MediaPipe based Human Pose Estimation (HPE) for practical posture assessment in mobile fitness applications.
Co-Authors Collhins, Billy Gunawan, Christian Manalu, Sonya Rapinta