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

Özbalkan U., TURNA Ö. C.

Applied Sciences (Switzerland), cilt.16, sa.1, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/app16010488
  • Dergi Adı: Applied Sciences (Switzerland)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: artificial intelligence in exercise monitoring, deep learning for exercise feedback, human motion tracking, real-time movement classification, self-guided fitness training
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

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 ≈ 98.9%), while the Transformer model yielded significantly lower accuracy (mean ≈ 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.