Improving anterior deltoid activity in a musculoskeletal shoulder model – an analysis of the torque-feasible space at the sternoclavicular joint

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).

Effects of glenoid inclination and acromion index on humeral head translation and glenoid articular cartilage strain

BACKGROUND

Previous clinical studies have reported associations between glenoid inclination (GI), the acromion index (AI), and the critical shoulder angle (CSA) on the one hand and the occurrence of glenohumeral osteoarthritis and supraspinatus tendon tears on the other hand. The objective of this work was to analyze the correlations and relative importance of these different anatomic parameters.

METHODS

Using a musculoskeletal shoulder model developed from magnetic resonance imaging scans of 1 healthy volunteer, we varied independently GI from 0° to 15° and AI from 0.5 to 0.8. The corresponding CSA varied from 20.9° to 44.1°. We then evaluated humeral head translation and critical strain volume in the glenoid articular cartilage at 60° of abduction in the scapular plane. These values were correlated with GI, AI, and CSA.

RESULTS

Humeral head translation was positively correlated with GI (R = 0.828, P < .0001), AI (R = 0.539, P < .0001), and CSA (R = 0.964, P < .0001). Glenoid articular cartilage strain was also positively correlated with GI (R = 0.489, P = .0004) but negatively with AI (R = -0.860, P < .0001) and CSA (R = -0.285, P < .0473).

CONCLUSIONS

The biomechanical shoulder model is consistent with clinical observations. The prediction strength of CSA is confirmed for humeral head translation and thus presumably for rotator cuff tendon tears, whereas the AI seems more appropriate to evaluate the risk of glenohumeral osteoarthritis caused by excessive articular cartilage strain. As a next step, we should corroborate these theoretical findings with clinical data.