Abstract
Although partial-hand amputees largely retain the ability to use their wrist, it is difficult to preserve wrist motion while using a myoelectric partial-hand prosthesis without severely impacting control performance. Electromyogram (EMG) pattern recognition is a well-studied control method; however, EMG from wrist motion can obscure myoelectric finger control signals. Thus, to accommodate wrist motion and to provide high classification accuracy and minimize system latency, we developed a training protocol and a classifier that switches between long and short EMG analysis window lengths. Seventeen non-amputee and two partial-hand amputee subjects participated in a study to determine the effects of including EMG from different arm and hand locations during static and/or dynamic wrist motion in the classifier training data. We evaluated several real-time classification techniques to determine which control scheme yielded the highest performance in virtual real-time tasks using a three-way ANOVA. We found significant interaction between analysis window length and the number of grasps available. Including static and dynamic wrist motion and intrinsic hand muscle EMG with extrinsic muscle EMG significantly reduced pattern recognition classification error by 35%. Classification delay or majority voting techniques significantly improved real-time task completion rates (17%), selection (23%), and completion (11%) times, and selection attempts (15%) for non-amputee subjects, and the dual window classifier significantly reduced the time (8%) and average number of attempts required to complete grasp selections (14%) made in various wrist positions. Amputee subjects demonstrated improved task timeout rates, and made fewer grasp selection attempts, with classification delay or majority voting techniques. Thus, the proposed techniques show promise for improving control of partial-hand prostheses and more effectively restoring function to individuals using these devices.
Highlights
As of 2005, there were an estimated 455,000 individuals in the United States living with partial-hand amputations (ZieglerGraham et al, 2008), with over 14,500 new cases occurring each year (Dillingham et al, 2002)
Touch-sensitive resistors and conventional myoelectric control strategies that rely on EMG signal amplitude have been used to control partial-hand prosthesis movements
Though conventional control using intrinsic hand muscle EMG is not compromised by wrist motion, users must operate an unintuitive switching mechanism to control more than one prosthetic function
Summary
As of 2005, there were an estimated 455,000 individuals in the United States living with partial-hand amputations (ZieglerGraham et al, 2008), with over 14,500 new cases occurring each year (Dillingham et al, 2002). Powered myoelectric prostheses, controlled with surface electromyographic (EMG) signals have been used clinically by higher-level upper-limb amputees since the 1970s (Feeny and Hagaeus, 1970; Yamada et al, 1983; Parker and Scott, 1986), but have only recently become available to partial-hand amputees (Weir and Grahn, 2000; Gow, 2014). These prostheses can provide functional hand-grasps not available in earlier myoelectric prostheses, though they are still restricted by inadequate control methods (Phillips et al, 2012). EMG from intrinsic muscles can be very challenging to comfortably and robustly capture using electrodes mounted within the patient’s socket
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