From Data to Autonomy: Integrating Demographic Factors and AI Models for Expert-Free Exercise Coaching

Abstract

This study investigates the performance of three deep learning architectures-LSTM with Attention, GRU with Attention, and Transformer-in the context of real-time, self-guided exercise classification, using coordinate data collected from 103 participants via a dual-camera system. Each model was evaluated over ten randomized runs to ensure robustness and statistical validity. The GRU + Attention and LSTM + Attention models demonstrated consistently high test accuracy (mean approximate to 98.9%), while the Transformer model yielded significantly lower accuracy (mean approximate to 96.6%) with greater variance. Paired t-tests confirmed that the difference between LSTM and GRU models was not statistically significant (p = 0.9249), while both models significantly outperformed the Transformer architecture (p < 0.01). In addition, participant-specific features, such as athletic experience and BMI, were found to affect classification accuracy. These findings support the feasibility of AI-based feedback systems in enhancing unsupervised training, offering a scalable solution to bridge the gap between expert supervision and autonomous physical practice.