Abstract
There is an increasing need to extend the control possibilities of upper limb amputees over their prosthetics, especially given the development of devices with numerous active joints. One way of feeding pattern recognition myoelectric control is to rely on the myoelectric activities of the residual limb associated with phantom limb movements (PLM). This study aimed to describe the types, characteristics, potential influencing factors and trainability of upper limb PLM. Seventy-six below- and above-elbow amputees with major amputation underwent a semi-directed interview about their phantom limb. Amputation level, elapsed time since amputation, chronic pain and use of prostheses of upper limb PLM were extracted from the interviews. Thirteen different PLM were found involving the hand, wrist and elbow. Seventy-six percent of the patients were able to produce at least one type of PLM; most of them could execute several. Amputation level, elapsed time since amputation, chronic pain and use of myoelectric prostheses were not found to influence PLM. Five above-elbow amputees participated in a PLM training program and consequently increased both endurance and speed of their PLM. These results clearly encourage further research on PLM-associated muscle activation patterns for future PLM-based modes of prostheses control.
Highlights
Given the development of new biomimetic devices with numerous active joints, there is an increased need to extend the control possibilities of upper limb amputees over their prosthetics[1]
One way of feeding pattern-recognition myoelectric control is to rely on the EMG activities of the residual limb associated with phantom limb movement (PLM) execution
Despite the development of more natural prosthetic control approaches based on PLM use/decoding[13,19,22,23], little is known from an epidemiological point of view
Summary
Given the development of new biomimetic devices with numerous active joints (e.g. recent polydigital hands), there is an increased need to extend the control possibilities of upper limb amputees over their prosthetics[1]. Despite the potential possibilities offered by new biomimetic prostheses such as whole robotic arms [DEKA Luke ARM]3 or polydigital hands, their control is still complex (far from intuitive) and offers few functional degrees of freedom[1] To overcome these limitations, pattern-recognition approaches were developed in the late 60 s/70s4–6, with the aim of decoding more precisely the myoelectric signals and to recognize more classes of contractions from the EMG signals and to control more classes of motions. The associated muscle activity varies with the type of executed PLM9,11,13 even for different finger movements in above-elbow amputees[13] This pattern-recognition approach has been extensively studied www.nature.com/scientificreports/. Despite the development of more natural prosthetic control approaches based on PLM use/decoding[13,19,22,23], little is known from an epidemiological point of view
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