Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68612
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Diploma in Artificial Intelligence and Machine Learning
Zeal Polytechnic
Diploma in Artificial Intelligence and Machine Learning
Zeal Polytechnic
Diploma in Artificial Intelligence and Machine Learning
Zeal Polytechnic
Diploma in Artificial Intelligence and Machine Learning
Zeal Polytechnic
Artificial Intelligence and Machine Learning
Zeal Polytechnic
AI Gym Assistant is a comprehensive, web-based intelligent fitness support platform designed to make workout guidance accessible, interactive, and trackable for individuals across all proficiency levels. The system integrates a voice-enabled conversational AI fitness coach, real-time exercise pose estimation, adaptive workout recommendations, Body Mass Index (BMI) tracking, dietary planning with exclusion filters, and visual progress analytics into a unified application. To further expand traditional exercise modalities, this paper introduces a proposed, leveraging suspended aerial gymnastics, decompression movement physics, and specialized spatial keypoint tracking. Developed using Python Flask backend framework and client-side JavaScript execution, core pose estimation and spatial tracking functions execute directly in the web browser via MediaPipe Pose, ensuring low-latency landmark extraction while strictly maintaining user privacy. Joint-angle calculation algorithms coupled with a Finite State Machine (FSM) handle movement phase transitions and accurate repetition counting. Conversational coaching is driven by the Google Gemini API with an automated local keyword fallback mechanism during network dropouts. Session records and physical parameters are stored in SQLite, while Chart.js provides visual analytical reporting. This paper delivers a thorough literature survey, architecture design,, mathematical model, and evaluation plan across 5 structured pages
KEYWORDS
This survey reviewed the techniques needed for a browser-based fitness assistant and described how they were combined in AI Gym Assistant. Lightweight pose estimation allows form tracking on modest hardware without uploading video; angle thresholds with a state machine give transparent repetition counting; language models make a coach flexible but need safeguards and a fallback; and rule-based scoring keeps recommendations explainable. The prototype is not clinically validated and no accuracy claims are made. Its value lies in showing how these parts can be integrated responsibly, and the evaluation plan sets out how its effectiveness can be measured next.
[1] Y. J. Oh , J. Zhang, M.-L . Fang , and Y. Fukuoka, “A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss,” vol. 18, [2] A. Toshev and C. Szegedy, “DeepPose: Human pose estimation via deep neural networks,” in Proc. IEEE Conf. Computer. Vis. Pattern Recognit. (CVPR), 2014, pp. 1653–1660. [3] Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, “OpenPose: Realtime multi-person 2D pose estimation using part affinity fields,” IEEE Trans. Pattern Anal.Mach. Intell., vol. 43. [4] V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang ,and M. Grundmann, “BlazePose : On-device real-time body pose tracking.[5] D. Smilkov et al., “TensorFlow.js: Machine learning for the web and beyond,” [6] A . Jaiswal, G . Chauhan, and N. Srivastava, “Using learnable physics for real-time exercise form recommendations,” in Proc. [7] World Wide Web Consortium (W3C), “Web Speech API,” W3C Community Group Report. Available: https://webaudio.github.io/web-speech-api. [8] Gemini Team, Google, “Gemini: A family of highly capable multimodal models,” [9] Z. Ji et al., “Survey of hallucination in natural language generation,” [10] W3C Community Group, “Web Speech API,” Draft Community Group Report. [Online]. Available: https://webaudio.github.io/web-speech-api/.
Publication Date
10/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...