A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems

Abstract

Upper extremity neuroprostheses use functional electrical stimulation (FES) to restore arm motor function to individuals with cervical level spinal cord injury. For the design and testing of these systems, a biomechanical model of the shoulder and elbow has been developed, to be used as a substitute for the human arm. It can be used to design and evaluate specific implementations of FES systems, as well as FES controllers. The model can be customized to simulate a variety of pathological conditions. For example, by adjusting the maximum force the muscles can produce, the model can be used to simulate an individual with tetraplegia and to explore the effects of FES of different muscle sets. The model comprises six bones, five joints, nine degrees of freedom, and 29 shoulder and arm muscles. It was developed using commercial, graphics-based modeling and simulation packages that are easily accessible to other researchers and can be readily interfaced to other analysis packages. It can be used for both forward-dynamic (inputs: muscle activation and external load; outputs: motions) and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations. Our model was verified by comparing the model calculated muscle activations to electromyographic signals recorded from shoulder and arm muscles of five subjects. As an example of its application to neuroprosthesis design, the model was used to demonstrate the importance of rotator cuff muscle stimulation when aiming to restore humeral elevation. It is concluded that this model is a useful tool in the development and implementation of upper extremity neuroprosthetic systems.

Figure 1

Experimental Setup. Sets of LED were fixed over the thorax, humerus and forearm of the subjects, and the three-dimensional position of these segments was recorded using three cameras. The positions of the clavicle and scapula were estimated using a regression model based on the scapulohumeral rhythm. Surface and percutaneous electrodes measured the EMG signals from selected shoulder and arm muscles.

Figure 2

EMG recording, processing, and comparison to model-predicted activations. For example, the EMG and model-predicted activations of middle deltoid muscle during a repeated humeral abduction movement are illustrated. Raw EMG (A), processed (rectified, low pass filtered at 4Hz and normalized) EMG (B), model-predicted activation (C), processed EMG (black line) and model-predicted activation (grey line) (D). For part D, the vertical axis for the EMG is on the left, and for the activation is on the right.

Figure 3

Serratus anterior (A), supraspinatus (B), and infraspinatus (C) signals during a repeated humeral elevation movement. For all muscles, the processed EMG signal is the black line, and the model-predicted activation is the grey line. The vertical axes for the EMG signals are on the left, and for the model-predicted activations are on the right.

Figure 4

Biceps (A) and triceps (B) signals during a repeated elbow flexion/extension movement. The processed EMG signal is the black line, and the model-predicted activation is the grey line. Co-contraction of the biceps and triceps is recorded in the EMG signals, but is not predicted by the model.

Figure 5

Cross-correlation between EMG and model-predicted muscle activations, averaged across tasks. Every row corresponds to one muscle, and every column to one subject. The vertical scale of every plot ranges from 0 to 1.

Figure 6

Maximum humeral elevation achieved in four cases: (a) able-bodied (b) C3 SCI (c) C3 SCI with FES of serratus anterior and deltoids (d) C3 SCI with FES of serratus anterior, deltoids, infraspinatus, supraspinatus and subscapularis.