Load-Aware Calibration of EMG-Driven Musculoskeletal Models for Accurate and Generalizable Joint Torque Estimation

TL;DR

This study tackles the limitation of EMG-driven MSK models that calibrate parameters under a single load, proposing a load-aware calibration framework tested on elbow flexion-extension under 0, 2, and 4 kg loads. It benchmarks three calibration strategies (load-specific, global, cross-load) and three optimization methods (SA, PSO, PSO-PS), showing that load-specific calibration yields the best joint torque predictions and that PSO-based methods provide more consistent, physiologically plausible parameter estimates. A sensitivity analysis identifies a small subset of parameters—notably activation dynamics and musculotendon geometry—that dominate torque prediction accuracy, guiding more efficient calibration. The framework enables robust, subject-specific modeling across contextually different yet visually similar tasks, with implications for clinical, ergonomic, and assistive applications.

Abstract

Accurate EMG-driven musculoskeletal (MSK) modeling is critical for biomechanics, rehabilitation, and assistive technology. However, most models calibrate parameters under a single load, ignoring the fact that tasks with similar kinematics may differ in mechanical demand. This study introduces a load-aware calibration framework to improve joint torque prediction accuracy and generalizability. Surface EMG and joint kinematics were recorded from eleven participants during elbow flexion-extension under 0, 2, and 4\,kg loads. We evaluated three calibration strategies (load-specific, global, cross-load) and three optimization frameworks (simulated annealing (SA), particle swarm optimization (PSO), and hybrid PSO-pattern search (PSO-PS)). Results indicate that load-specific calibration significantly improves performance, with lower RMSE and higher correlation ($r > 0.75$). Parameters related to muscle force, fiber length, and activation dynamics showed high load sensitivity. PSO-based methods yielded more consistent and physiologically plausible estimates than simulated annealing. The proposed framework enables MSK models to distinguish between visually similar but mechanically distinct tasks, supporting robust subject-specific modeling for clinical and real-world applications.

Figures (12)

• Figure 1: Experimental data recording setup and protocol. (a) Surface EMG electrodes were placed on four upper-arm muscles (biceps long head, biceps short head, triceps long head, and triceps lateral head), and reflective markers were attached for joint angle measurement. Elbow flexion-extension movements were performed by participants while holding dumbbells of 0, 2, or 4 kg. EMG and joint kinematic data were acquired synchronously using a Noraxon Ultium wireless system and NiNOX camera, respectively. (b) The experimental protocol comprised alternating phases of preparation, elbow flexion-extension task, and rest, with visual and auditory cues provided via a display and speaker.
• Figure 2: Main framework. This modeling setup was designed to isolate parameter responses to load variations while ensuring consistency in movement kinematics.
• Figure 3: Optimization method comparison for musculoskeletal calibration. Mean $\pm$ SD optimization errors for simulated annealing (SA), particle swarm optimization (PSO), and hybrid PSO-pattern search (PSO-PS) across all subjects and loading conditions (0, 2, 4 kg, and global). PSO-PS consistently yields the lowest calibration error, highlighting its robustness for EMG-driven modeling under variable loads.
• Figure 4: Predicted vs. experimental elbow joint torque for subject S01. Mean (solid) and SD (shaded) for model-predicted and experimental torque across trials under all loads (0 kg: blue, 2 kg: red, 4 kg: green). Model predictions closely match experimental torque across conditions.
• Figure 5: Calibration strategy comparison for joint torque estimation across loading conditions. Mean $\pm$ SD bar plots of (a) RMSE and (b) Pearson correlation ($r$) for five strategies—load-specific, fixed-global, fixed-0kg, fixed-2kg, and fixed-4kg—across all subjects and loads (0, 2, 4 kg). The top strategy (lowest RMSE, highest $r$) is marked with a black border/yellow triangle; the second-best with a gray border/blue circle.