Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.
ABSTRACT 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. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.
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ABSTRACT: A tradeoff exists when considering the delay created by multifunctional prosthesis controllers. Large controller delays maximize the amount of time available for EMG signal collection and analysis (and thus maximize classification accuracy); however, large delays also degrade prosthesis performance by decreasing the responsiveness of the prosthesis. To elucidate an "optimal controller delay" twenty able-bodied subjects performed the Box and Block Test using a device called PHABS (prosthetic hand for able bodied subjects). Tests were conducted with seven different levels of controller delay ranging from nearly 0-300 ms and with two different artificial hand speeds. Based on repeted measures ANOVA analysis and a linear mixed effects model, the optimal controller delay was found to range between approximately 100 ms for fast prehensors and 125 ms for slower prehensors. Furthermore, the linear mixed effects model shows that there is a linear degradation in performance with increasing delayIEEE Transactions on Neural Systems and Rehabilitation Engineering 04/2007; · 3.26 Impact Factor
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ABSTRACT: The paper discusses results of on-line tests on amputees and hemiplegics of multifunctional prostheses and orthoses control by identifying the parameters of single-site temporal EMG signal signatures. The results relate to tests on above-elbow amputees, on shoulder-disarticulation amputees (including a congenital disarticulation amputee) and on hemiplegics, varying from 5 to 50 years of age. The system employed is based on an 8-bit Intel 8080 microprocessor, when computation is in double precision, to obtain an effective 16-bit work-length. The system employs a sequential least-squares algorithm to identify a 4-parameter auto-regressive time-series model of the EMG signal, and a Bayesian rule discrimination algorithm.Journal of Biomedical Engineering 02/1982; 4(1):17-22.
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ABSTRACT: This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.IEEE Transactions on Biomedical Engineering 02/1993; 40(1):82-94. · 2.35 Impact Factor