Citation

Lew, Kai Liang and Muniandy, Kedaresa and Lee, Chia Shyan (2026) Integrating Real-Time Pose Estimation in Block-Based Programming Environments Through Novel Architectural Patterns. International Journal on Robotics Automation and Sciences, 8 (1). p. 44. ISSN 2682-860X

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Abstract

This technical demonstration study develops an automated motion analysis system via MitApp Inventor. It is a high-level block-based visual programming language. The system also utilises a pose estimation library for computer vision tasks, which is implemented within the application. The system addresses the growing need for accessible motion tracking by eliminating dependency on additional hardware and providing real-time movement classification capabilities. The user interface, as well as the block diagram of the application, are designed and developed using MIT App Inventor. The basic working principle of how the application operates is that users can perform movements that are automatically tracked and classified. MitApp Inventor allows users to design and develop via a computer or a laptop. Once created, the application can be viewed in an Android / iOS emulator as well as on the user's device. In terms of motion tracking performance, Posenet has been chosen as the only library that the MitApp Inventor supports. The Posenet model is suitable for detecting and tracking key body points of a human body in real-time. The system features four different arm exercises, including left-arm bicep curls, right-arm bicep curls, lateral raises, and military presses. These exercises are designed to detect the angles of the body's joints when a user performs them. Testing with 10 participants, who performed 25 repetitions of each exercise, totalling 1,000 pose classifications, demonstrated the system's effectiveness. The Posenet achieved high accuracy in movement recognition, with precision and recall values of 0.94 and 0.94 for left arm curls, 0.932 and 0.932 for right arm curls, and 0.96 and 0.96 for both lateral raises and military push exercises, demonstrating its effectiveness in precise motion classification. The system achieved an overall accuracy of 94.8% while providing immediate feedback for movement form correction, offering a viable approach for automated motion analysis applicable to human-robot interaction, motion capture systems and industrial safety monitoring.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, automated motion analysis
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Jul 2026 03:43
Last Modified:	09 Jul 2026 03:43
URII:	http://shdl.mmu.edu.my/id/eprint/16341

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