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

Objective.Hill-type muscle models offer physiologically interpretable muscle 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.