A sensor-driven Hill-type muscle modeling framework integrating sEMG and pFMG for biceps brachii force estimation

OBJECTIVE: Hill-type muscle models offer physiologically interpretablemuscle force estimation, but their use in wearable and personalized applications is limited by sensing constraints and validation challenges. This study presents a sensor-driven Hill-type muscle modeling framework for estimating biceps muscle force under controlled isometric conditions using wearable signals. APPROACH: A Hill-type muscle model integrating surface electromyography (sEMG) and pressure-based force myography (pFMG) was proposed to estimate biceps brachii force under isometric conditions. To evaluate the mechanical consistency of the model, a purely mechanical elbow model, based on joint geometry, moment arms, external loads, and static equilibrium, was employed. The proposed Hill-type muscle model estimates both passive and total muscle forces across multiple elbow angles, with results evaluated for mechanical consistency against the benchmark mechanical elbow model. MAIN RESULTS: Under passive conditions, pFMG measurements alone enabled estimation of length-dependent passive muscle force. For active conditions, the combined use of sEMG-derived activation and pFMG-derived deformation allowed consistent estimation of total muscle force trends. Strong agreement was observed between the sensor-driven estimates by the Hill-type model and mechanically derived reference values by the elbow model, with high coefficients of determination and low estimation errors. SIGNIFICANCE: By explicitly separating neural activation and geometric deformation within a Hill-type structure, the proposed approach provides a physiologically meaningful and experimentally feasible solution for wearable muscle force estimation, and may offer a potential foundation for future investigation of real-time and subject-specific neuromuscular modeling applications.