Minimum biomechanical energy expenditure predicts upper-limb motor strategies in individuals with limb loss

Traditional models of upper-limb motion represent observed motor behaviors as the solution to an optimization problem defined over a cost function. However, these traditional formulations are computationally expensive and it is unclear if they extend to individuals with non-standard anatomy (such as those with upper-limb loss). Goal: We propose an optimal path planning framework that leverages musculoskeletal modeling to generate motor strategies during unconstrained, upper-limb movement. Methods: We validate this framework against upper-limb trajectories measured from a 3D target acquisition task and compare performance against multiple models of upper-limb motion previously presented in literature. Results: When compared to measured upper-limb trajectories, the proposed method generates upper-limb paths with significantly less geometric error than alternative methods (p < 0.001). Significance: Our approach provides a method for upper-limb motion planning that is easily adaptable to non-standard anatomies and computationally efficient enough for prosthesis control applications. Conclusions: The proposed path planning framework provides accurate motor strategy prediction for individuals both with and without upper-limb loss.