Modelling force-length-activation relationships of wrist and finger extensor muscles

Sensitivity Analysis of Upper Limb Musculoskeletal Models During Isometric and Isokinetic Tasks

Sensitivity coefficients are used to understand how errors in subject-specific musculoskeletal model parameters influence model predictions. Previous sensitivity studies in the lower limb calculated sensitivity using perturbations that do not fully represent the diversity of the population. Hence, the present study performs sensitivity analysis in the upper limb using a large synthetic dataset to capture greater physiological diversity. The large dataset (n = 401 synthetic subjects) was created by adjusting maximum isometric force, optimal fiber length, pennation angle, and bone mass to induce atrophy, hypertrophy, osteoporosis, and osteopetrosis in two upper limb musculoskeletal models. Simulations of three isometric and two isokinetic upper limb tasks were performed using each synthetic subject to predict muscle activations. Sensitivity coefficients were calculated using three different methods (two point, linear regression, and sensitivity functions) to understand how changes in Hill-type parameters influenced predicted muscle activations. The sensitivity coefficient methods were then compared by evaluating how well the coefficients accounted for measurement uncertainty. This was done by using the sensitivity coefficients to predict the range of muscle activations given known errors in measuring musculoskeletal parameters from medical imaging. Sensitivity functions were found to best account for measurement uncertainty. Simulated muscle activations were most sensitive to optimal fiber length and maximum isometric force during upper limb tasks. Importantly, the level of sensitivity was muscle and task dependent. These findings provide a foundation for how large synthetic datasets can be applied to capture physiologically diverse populations and understand how model parameters influence predictions.

Influence of Force-Length Relationship and Task-Specific Constraints on Finger Force-Generating Capacities

Grip strength loss in extended and flexed wrist postures has been explained by reduced force-generating capacities of extrinsic finger flexor resulting from non-optimal length, owing to the force-length relationship. Recent works suggested that other muscles, especially wrist extensors, participate in this grip strength loss. The objective of this study was to clarify the role of the force-length relationship in finger force production. 18 participants performed maximal isometric finger force production during pinch grip (Pinch) and four-finger pressing (Press) tasks in four different wrist postures (extended, flexed, neutral, spontaneous). The maximum finger force (MFF), finger and wrist joint angles, as well as activation of four muscles were determined using dynamometry, motion capture, and electromyography. The force and length of the four muscles were estimated from joint angles and muscle activation using a musculoskeletal model. MFF decreased for flexed wrist during Pinch but remained stable across wrist postures during Press. The results suggested that the loss of pinch grip force in deviated wrist posture is partially related to force-length relationship of finger extensors. In opposition, MFF during Press was not influenced by the modulation of muscle capacities but was probably first limited by mechanical and neural factors related to finger interdependence.

Electromyography-informed musculoskeletal modeling provides new insight into hand tendon forces during tennis forehand

Lateral epicondylitis, also known as tennis elbow, is a major health issue among tennis players. This musculo-skeletal disorder affects hand extensor tendons, results in substantial pain and impairments for sporting and everyday activities and requires several weeks of recovery. Unfortunately, prevention remains limited by the lack of data regarding biomechanical risk factors, especially because in vivo evaluation of hand tendon forces remains challenging. Electromyography-informed musculo-skeletal modeling is a noninvasive approach to provide physiological estimation of tendon forces based on motion capture and electromyography but was never applied to study hand tendon loading during tennis playing. The objective of this study was to develop such electromyography-informed musculo-skeletal model to provide new insight into hand tendon loading in tennis players. The model was tested with three-dimensional kinematics and electromyography data of two players performing forehand drives at two-shot speeds and with three rackets. Muscle forces increased with shot speed but were moderately affected by racket properties. Wrist prime extensors withstood the highest forces, but their relative implication compared to flexors depended on the player-specific grip force and racket motion strategy. When normalizing wrist extensor forces by shot speed and grip strength, up to threefold differences were observed between players, suggesting that gesture technique, for example, grip position or joint motion coordination, could play a role in the overloading of wrist extensor tendons. This study provided a new methodology for in situ analysis of hand biomechanical loadings during tennis gesture and shed a new light on lateral epicondylitis risk factors.

Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model

EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human-machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.