![]() Electromyography (EMG) data from the forearm has been used to predict movement with low, mixed, and high levels of accuracy. Pattern recognition has been implemented with individuals with stroke with varying degrees of success. 90% is significant, as it has been implicated as a transitional value between high functionality and extremely variable levels of functionality of a myoelectric prosthesis based on the user, classifier, and their interaction. Although LDA-based pattern recognition is often focused on controlling distal joints, pattern recognition of shoulder motions of healthy controls has been explored for the purposes of application to the population with amputation and has achieved classification accuracies above 90%. Historically, these devices were controlled using simple amplitude-based thresholds, but recently the use of linear discriminant analysis (LDA) based pattern recognition has proven to be both accurate and computationally efficient and enables intuitive control of a greater number of degrees of freedom. The application of wearable robotic technology has found success in individuals with amputation paving the way for potential use in individuals with stroke. However, design requirements and feasible control techniques that consider the expression of abnormal synergy/loss of independent joint control have not been established. Powered exoskeletons, for both upper- and lower- extremity, are becoming more commonplace and are beginning to emerge as viable sources of rehabilitation and assistance. At a minimum, a wearable device could assist and enable a survivor of stroke by minimizing the effects of abnormal synergy and therefore maximizing their functional work area, better engaging their environment, and/or supporting interventions for their hand, wrist, and elbow. This exoskeleton could provide smart-support possibly leading to long-term improvements in workspace. Therefore, one possible solution to aid these stroke survivors with persistent loss of independent joint control is to support their arm with a wearable exoskeleton. While targeting this impairment with progressive abduction loading therapy has provided small benefit the complete restoration of movement remains elusive. When shoulder effort is reduced, there is a proportional reduction in the expression of loss of independent joint control, enabling access to a greater functional workspace, with full support of the shoulder leading to near maximal reaching range of motion. ![]() The loss of independent joint control resultant from abnormal synergies is thought to be the result of increased utilization of the contralesional corticoreticulospinal tract. In the same manner, shoulder adduction produces involuntary elbow extension, wrist and finger flexion, and forearm pronation and is referred to as the extension synergy. Proximal shoulder abduction effort causes involuntary elbow, wrist, and finger flexion, as well as forearm supination proportional to the amount of shoulder effort and is referred to as the flexion synergy. Commonly these survivors present with abnormal movement patterns referred to as abnormal synergies described as a loss of independent joint control due to coactivation of muscles across multiple joints. ![]() Of these, an estimated 50% result with chronic hemiparesis and up to 80% may have residual upper-extremity impairments. and 16 million people worldwide suffer a stroke each year. This study focuses on the evaluation of an LDA-based classifier to predict individual degrees-of-freedom of the shoulder and elbow joints. However, following stroke, shoulder effort has been shown to have a negative effect on classification accuracy of hand tasks due to the multi-joint torque coupling of abnormal synergy. One control strategy that has proven viable, effective, and computationally efficient in myoelectric prostheses for use in individuals with amputation is linear discriminant analysis (LDA)-based pattern recognition. ![]() However, predicting movement intent from abnormally-coupled torques or EMG signals and subsequent use as a control signal remains elusive. A wearable exoskeleton capable of predicting movement intent could augment abduction effort and therefore reduce the negative effects of distal joint flexion synergy. ![]() The flexion synergy, consisting of involuntary flexion coupling of the paretic elbow, wrist, and fingers, is caused by and proportional to the amount of shoulder abduction effort and limits reaching function. Abnormal synergy is a major stroke-related movement impairment that presents as an unintentional contraction of muscles throughout a limb. ![]()
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