Benchmark and Validation of State-of-the-art Muscle Recruitment Strategies in Shoulder Modelling

Abstract

Abstract Shoulder muscle forces estimated via modelling are typically indirectly validated against measurements of glenohumeral joint reaction forces (GHJ-RF). This validation study benchmarks the outcomes of several muscle recruitment strategies against public GHJ-RF measurements. Public kinematics, electromyography, and GHJ-RF data from a selected male participant executing a 2.4 kg weight shoulder abduction task up to 92° GHJ elevation were obtained. The Delft Shoulder and Elbow Model was scaled to the participant. Muscle recruitment was solved by 1) minimizing muscle activations squared (SO), 2) accounting for dynamic muscle properties (CMC) and 3) constraining muscle excitations to corresponding surface electromyography measurements (CEINMS). Moreover, the spectrum of admissible GHJ-RF in the model was determined via Markov Chain Monte-Carlo stochastic sampling. The experimental GHJ-RF was compared to the resultant GHJ-RF of the different muscle recruitment strategies as well as the admissible stochastic range. Admissible GHJ-RF spanned 21 to 659% of body weight (%BW), excluding the experimental GHJ-RF up to 40 degrees of humeral elevation. Joint force RMSE were between 23 (CMC) and 27%BW (CEINMS). At high elevation angles, CMC (11%BW) and CEINMS (14%BW) performed better than SO (25%BW). A guide has been proposed to best select muscle recruitment strategies. Overall, CMC and CEINMS were the two most accurate methods in terms of predicted GHJ-RF, especially at high elevation angles. SO performed best at low elevation angles. In addition, stochastic muscle sampling provided critical information on the shoulder model capabilities and the consistency between model and experimental data.