Exercise Tracking And User Identification System For Fitness Environments

DOI

10.2991/978-94-6463-740-3_19How to use a DOI?

Keywords

Exercise Tracking; Wearable Device; Re-Identification

Abstract

In modern fitness environments, accurate tracking of user activity and identification poses significant challenges due to the dynamic nature of gym settings. This project introduces an innovative system that uses RFID for entry/exit tracking and OSNet-based deep learning for robust, real-time re-identification of a person. The system incorporates a custom-designed wearable device with IMU sensors and machine learning algorithms to monitor and analyze exercises with high accuracy and minimal user intervention. A key design principle of this system is seamless usability, prioritizing minimal disruption to the user’s workout flow. Once checked in via RFID, users are passively tracked across stations without requiring further action, allowing them to focus solely on their exercise routines. Each station operates independently, using a sequence of motion detection, re-identification, and exercise-specific analysis, while multi-threading enables simultaneous monitoring across multiple stations. The system employs a dual-modality approach, combining data from IMU sensors on a wrist-worn device with video-based pose estimation to accurately track user movements and exercise form. This complementary setup ensures robustness, compensating for limitations in each modality—for example, addressing occlusions in video or stationary poses undetectable by IMU. Together, these data streams allow for precise repetition counting and detailed movement analysis, making the system adaptable to diverse exercise types and enhancing the accuracy and reliability of real-time exercise monitoring.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rana Gürsoy
AU  - Furkan Yüceyalçın
AU  - Muhammed Ali Soydaş
AU  - Hüseyin Üvet
PY  - 2025
DA  - 2025/06/25
TI  - Exercise Tracking And User Identification System For Fitness Environments
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 215
EP  - 224
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_19
DO  - 10.2991/978-94-6463-740-3_19
ID  - Gürsoy2025
ER  -