Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation.

Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation.

Impairment in task-specific modulation of muscle coordination correlates with the severity of hand impairment following stroke.

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 impairment in the ability to modulate muscle coordination patterns across tasks and its correlation with hand impairment.

Patient training for functional use of pattern recognition-controlled prostheses.

Pattern recognition control systems have the potential to provide better, more reliable myoelectric prosthesis control for individuals with an upper-limb amputation. However, proper patient training is essential. We begin user training by teaching the concepts of pattern recognition control and progress to teaching how to control, use, and maintain prostheses with one or many degrees of freedom.

Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses.

Despite high classification accuracies (~95%) of myoelectric control systems based on pattern recognition, how well offline measures translate to real-time closed-loop control is unclear. Recently, a real-time virtual test analyzed how well subjects completed arm motions using a multiple-degree of freedom (DOF) classifier. Although this test provided real-time performance metrics, the required task was oversimplified: motion speeds were normalized and unintended movements were ignored.

A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control.

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.

Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.

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 using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels.

Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.

In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study.

A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control.

Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of approximately 95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance.

Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms.

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.

Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

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) signals on the surface of the skin that can be measured and used to control prosthetic arms.

Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery.

Objectives: To fit and evaluate the control of a complex prosthesis for a shoulder disarticulation-level amputee with targeted muscle reinnervation.

Design: One participant who had targeted muscle reinnervation surgery was fitted with an advanced prosthesis and his use of this device was compared with the device that he used in the home setting.

Setting: The experiments were completed within a laboratory setting.

Improved myoelectric prosthesis control using targeted reinnervation surgery: a case series.

Targeted reinnervation is a surgical technique developed to increase the number of myoelectric input sites available to control an upper-limb prosthesis. Because signals from the nerves related to specific movements are used to control those missing degrees-of-freedom, the control of a prosthesis using this procedure is more physiologically appropriate compared to conventional control. This procedure has successfully been performed on three people with a shoulder disarticulation level amputation and three people with a transhumeral level amputation.

Redirection of cutaneous sensation from the hand to the chest skin of human amputees with targeted reinnervation.

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.

Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study.

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 woman with a left arm amputation at the humeral neck.