Physiologically Constrained Musculoskeletal Neural Network for Multi-DoF Joint Kinematics Estimation from Partially Observed sEMG
2026-06-05 • Robotics
Robotics
AI summaryⓘ
The authors developed a new neural network called MSK-NN to estimate wrist joint movements using muscle signals, even when some muscle data is missing. Their method combines muscle activation estimation with a biomechanical model into one system that learns without needing detailed internal muscle measurements. By training with losses based on joint movements, muscle coordination, and anatomy, MSK-NN predicts joint angles more accurately than other methods, especially during unpredictable motions. They also showed that MSK-NN produces muscle activity estimates that match real physiological data and stay within normal muscle behavior ranges.
surface electromyography (sEMG)degrees of freedom (DoF)musculoskeletal modelingneural networksconvolutional neural network (CNN)joint kinematicsmuscle activationloss functionbiomechanicsmuscle synergy
Authors
Wending Heng, Mingming Zhang, Glen Cooper, Zhenhong Li
Abstract
This paper investigates multi-degrees of freedom (DoF) joint kinematics estimation under partially observed surface electromyography (sEMG), where only a subset of task-relevant muscles can be measured due to anatomical inaccessibility or sensor constraints. A novel musculoskeletal neural network (MSK-NN) is proposed to estimate multi-DoF joint angles while simultaneously inferring activations for both measured and unmeasured muscles. MSK-NN consists of a CNN-based muscle activation estimator and an embedded MSK forward dynamics module, forming a fully differentiable architecture. Unlike existing hybrid neural frameworks that require additional biomechanical labels (e.g., muscle-tendon forces, joint torques), MSK-NN is trained without direct supervision of internal biomechanical variables. A composite physics-physiology loss is designed by incorporating a joint kinematics loss, a data-driven muscle synergy loss, and an anatomy-guided trend loss. The proposed method is evaluated on two-DoF wrist kinematics estimation across three rhythmic motions with unconstrained speed and amplitude, and one random motion. Compared with CNN, Bi-LSTM, CNN-LSTM, and PET baselines, MSK-NN achieves lower normalized root mean square error (NRMSE) and higher coefficient of determination (R2), especially for the random motion. More importantly, the optimized MSK parameters remain within physiological limits, and the estimated activation of an input-excluded muscle exhibits strong temporal agreement with its recorded sEMG envelope, demonstrating the capability of musculoskeletal (MSK)-NN to recover physiologically plausible activations.