Comparison of Regression Models for Predicting Lower-Limb Joint Angles During Stationary Cycling

Accurate measurement of lower-limb joint kinematics is integral for developing effective rehabilitation programs and understanding musculoskeletal biomechanics. While previous studies have achieved good accuracy in predicting primary joint angles in gait, few studies have explored machine learning (ML) models tailored specifically to assess cycling biomechanics, and comparative evaluations for inter-subject generalization remain sparse. This study addresses these gaps by comparing the performance of three ML models, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Transformer, for predicting lower-limb joint angles during stationary cycling. Data were collected from 15 participants using a motion capture system synchronized with pedal encoders, capturing kinematics under varying saddle positions and cadences. The models were trained to predict pelvis, hip, knee, and ankle joint kinematics using features such as crank angles, saddle positions, and anthropometric data. The Transformer model achieved the lowest prediction errors, with inter-subject root mean square error (RMSE) values ranging from 2.7° ± 0.7° to 4.4° ± 0.4°, but was 8 times more computationally intensive than the CNN. Significant differences in nRMSE were observed among models across joint movements in both intra-subject and inter-subject evaluations. The CNN model offered a favorable trade-off, with slightly higher errors (3.7° ± 0.7° to 6.1° ± 1.3°) but much faster processing times compared to the Transformer model, making it suitable for real-time applications. The SVM model exhibited the highest errors (4.8° ± 1.2° to 7.0° ± 1.4°) and limited predictive performance. This study highlights the potential of ML-based approaches to advance biomechanical cycling analysis, offering insights into joint motions and supporting applications in rehabilitation and performance monitoring.