A real-time, 3-D musculoskeletal model for dynamic simulation of arm movements

Abstract

Neuroprostheses can be used to restore movement of the upper limb in individuals with high-level spinal cord injury. Development and evaluation of command and control schemes for such devices typically require real-time, "patient-in-the-loop" experimentation. A real-time, 3-D, musculoskeletal model of the upper limb has been developed for use in a simulation environment to allow such testing to be carried out noninvasively. The model provides real-time feedback of human arm dynamics that can be displayed to the user in a virtual reality environment. The model has a 3-DOF glenohumeral joint as well as elbow flexion/extension and pronation/supination and contains 22 muscles of the shoulder and elbow divided into multiple elements. The model is able to run in real time on modest desktop hardware and demonstrates that a large-scale, 3-D model can be made to run in real time. This is a prerequisite for a real-time, whole-arm model that will form part of a dynamic arm simulator for use in the development, testing, and user training of neural prosthesis systems.

Fig. 1

Part A shows activation of the elbow flexor muscles (at 4 s), co-contraction of the flexors and extensors (at 10 s) and activation of the flexors, extensors and pronators (at 14 s). Part B shows flexion of the elbow to 140° combined with full supination, extension back to 100° followed by pronation to 160°. Finally, deactivation of all muscles allows the model to return to its initial position of 5° flexion and 70° pronation.

Fig. 2

Part A shows the activation of the middle and anterior parts of the deltoid muscles, and Part B the model motions in response to those activations. Rotator cuff activity was held at the baseline level throughout the movement, and Part C shows the increase in the value of GH_stab to over 1, indicating potential dislocation of the joint.

Fig. 3

Activation of the middle and anterior parts of the deltoid muscles, with increased stimulation of the rotator cuff muscles are shown in Part A. The resulting model motions are shown in Part B. Note the increased stability of the gleno-humeral joint, shown by the stability value of < 0.5 in Part C.

Fig. 4

Stability of the simulation with two different solvers at their minimum step sizes. Part A shows that the model response to the given input is extremely similar with the two solvers. Part B shows the increased stability of the Runge-Kutta 4^th order solver over the Euler 1^st order.