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In this study, we evaluated classification performance of electromyography (EMG) four time-domain features and autoregressive model features and their combination in identifying 11 classes of arm and hand movements in both able-bodied subjects and amputees. Our results showed that using three time-domain features could achieve similar classification(More)
Inherent limitations of the surface myoelectric signal, such as the lack of recording sites in high-level amputations, and the sensitivity to placement and impedance effects, confound its wider application in powered prostheses. Since a functionally topographic distribution (somatotopic organization) of nerve fascicles exists within the peripheral nerves,(More)
A computational model linking stochastic neural innervation processes and functional neuromuscular excitation is developed to investigate peripheral nerve interface based limb prostheses. A means of classifying the virtual nerve data is presented by using both a time domain feature set and a spike detection algorithm. Some intrinsic parameters in recording(More)
BACKGROUND Measurement uncertainty (MU) characterizes the dispersion of the quantity values attributed to a measurand. Although this concept was introduced to medical laboratories some years ago, not all medical researchers are familiar with it. Therefore, the evaluation and expression of MU must be highlighted. In this paper, the evaluation of MU is(More)
Most previous studies of electromyography (EMG) pattern recognition control of multifunctional myoelectric prostheses adopted a conventional sampling rate that is commonly used in EMG research fields. However, it is unknown whether using a lower sampling rate in EMG acquisition still preserves sufficient neural control information for accurate(More)
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