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BACKGROUND The function of current artificial arms is limited by inadequate control methods. We developed a technique that used nerve transfers to muscle to develop new electromyogram control signals and nerve transfers to skin, to provide a pathway for cutaneous sensory feedback to the missing hand. METHODS We did targeted reinnervation surgery on a(More)
CONTEXT Improving the function of prosthetic arms remains a challenge, because access to the neural-control information for the arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce electromyogram (EMG)(More)
Pattern recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended(More)
Pattern recognition based myoelectric control systems have been well researched; however very few systems have been implemented in a clinical environment. Although classification accuracy or classification error is the metric most often reported to describe how well these control systems perform, very little work research has been conducted to relate this(More)
Amputees cannot feel what they touch with their artificial hands, which severely limits usefulness of those hands. We have developed a technique that transfers remaining arm nerves to residual chest muscles after an amputation. This technique allows some sensory nerves from the amputated limb to reinnervate overlying chest skin. When this reinnervated skin(More)
Significant functional impairment of the hand is commonly observed in stroke survivors. Our previous studies suggested that the inability to modulate muscle coordination patterns according to task requirements may be substantial after stroke, but these limitations have not been examined directly. In this study, we aimed to characterize post-stroke(More)
INTRODUCTION Many clinically available, upper-extremity prosthetic limbs provide myoelectric control of a single device, such as a hand, elbow, or wrist. Most commonly, these systems yield control information from myoelectric signal (MES) amplitude [1] or rate of change of MES [2]. Such systems have been beneficial; however, prosthetic users would no doubt(More)
Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis(More)
Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Non-amputee and amputee subjects(More)
This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected(More)