Improving the upper-limb force feasible set evaluation by muscles maximal isometric force identification and cocontraction factors

Maximal isometric force exertion predicted by the force feasible set formalism: application to handbraking

The aim of this study was to test the capacity of the force feasible set formalism to predict maximal force exertion during isometric handbraking. Maximal force exertion and upper-limb posture were measured with a force sensor embedded in a handbrake and an optoelectronic system, respectively. Eleven subjects participated in the experiment which consisted of exerting the maximal force in isometric conditions considering five hand brake positions relative to the seat H-point. Then, maximal force was predicted by the force feasible set obtained from an upper-limb musculoskeletal model. The root-mean-square (RMS) of the angle between measured and predicted forces was 8.4° while the RMS error (RMSE) for amplitude prediction was 95.4 N. However, predicted, and measured force amplitudes were highly correlated (r = 0.88, p < 0.05, slope = 0.97, intercept = 73.3N) attesting the capacity of the model to predict force exertion according to the subject's posture. The implications in the framework of ergonomics are then discussed. Practitioner summary: Maximal force exertion is of paramount importance in digital human modelling. We used the force feasible set formalism to predict maximal force exertion during handbraking from posture and anthropometric data. The predicted and measured force orientation showed a RMS of 8.4° while amplitude presented a RMSE of 95.4 N with a strong correlation (r = 0.88, p < 0.05, slope 0.97, intercept 77.3 N).

Force feasible set prediction with artificial neural network and musculoskeletal model

Developing tools to predict the force capabilities of the human limbs through the Force Feasible Set (FFS) may be of great interest for robotic assisted rehabilitation and digital human modelling for ergonomics. Indeed, it could help to refine rehabilitation programs for active participation during exercise therapy and to prevent musculoskeletal disorders. In this framework, the purpose of this study is to use artificial neural networks (ANN) to predict the FFS of the upper-limb based on joint centre Cartesian positions and anthropometric data. Seventeen right upper-limb musculoskeletal models based on individual anthropometric data are created. For each musculoskeletal model, the FFS is computed for 8428 different postures. For any combination of force direction and joint positions, ANNs can predict the FFS with high values of coefficient of determination (R² > 0.89) between the true and predicted data.

Evaluation and validation of musculoskeletal force feasible set indices: Application to manual wheelchair propulsion

The aim of this work was to assess handrim wheelchair propulsion effectiveness, related to the applied forces on the handrim, through the force feasible set. For a given posture of the upper-limb, it represents the set of isometric forces that can be applied on the handrim in any direction. The force feasible set was predicted from a musculoskeletal model of the upper-limb and trunk (10 degrees of freedom and 56 muscles). The aim of the first part of the study was to compare the force feasible set prediction and the force currently applied on the handrim. The second part proposes the creation of a new index called "Musculoskeletal Postural Performance Index" (MPPI) derived from the force feasible set and its comparison with the Mechanical Efficiency Force (MEF). These comparisons were conducted at 60, 80, 100, 120 and 140% of the Freely Chosen Frequency at submaximal and maximal conditions on 5 different phases of the push phase. The values of the MPPI were significantly correlated with those of the MEF. During the course of the push phase, the orientation of the force feasible set main axis approached that of the measured force and the force effectiveness evaluated through the MPPI increased.