Neural machine interfaces for controlling multifunctional powered upper-limb prostheses

Abstract

This article investigates various neural machine interfaces for voluntary control of externally powered upper-limb prostheses. Epidemiology of upper limb amputation, as well as prescription and follow-up studies of externally powered upper-limb prostheses are discussed. The use of electromyographic interfaces and peripheral nerve interfaces for prosthetic control, as well as brain machine interfaces suitable for prosthetic control, are examined in detail along with available clinical results. In addition, studies on interfaces using muscle acoustic and mechanical properties and the problem of interfacing sensory information to the nervous system are discussed.

Feasibility of controlling prosthetic hand using sonomyography signal in real time: Preliminary study

“…The signal processing for electromagnetic effects and sensor attachments are still challenging when collecting the MK signal [7]. The MMG can be affected by many factors such as muscle temperature, skinfold thickness, socket movement, and external mechanical noise [4,10,34]. These factors, together with challenges in lowfrequency noise elimination, can reduce the stability and reliability of the MMG signal [4,7].…”

“…The MMG can be affected by many factors such as muscle temperature, skinfold thickness, socket movement, and external mechanical noise [4,10,34]. These factors, together with challenges in lowfrequency noise elimination, can reduce the stability and reliability of the MMG signal [4,7]. SMG reflects the structural and morphological features of skeletal muscle, and the morphological changes are easily extracted from ultrasound images with little conscious effort.…”

“…Over the past decade, researchers across the world have expended a great deal of effort to improve the control accuracy of electromyography (EMG)-based prostheses [1][2][3][4]. However, some inherent limitations have not yet been overcome [3][4].…”

“…However, some inherent limitations have not yet been overcome [3][4]. For example, it is believed that considerable attention is required to control EMG-based prostheses, often resulting in fatigue [4][5]. It is also difficult for SEMG to detect the activities of deep muscles because the deep muscle EMG may be attenuated and/or mixed with the superficial muscle EMG at the skin surface [4,[6][7].…”

“…For example, it is believed that considerable attention is required to control EMG-based prostheses, often resulting in fatigue [4][5]. It is also difficult for SEMG to detect the activities of deep muscles because the deep muscle EMG may be attenuated and/or mixed with the superficial muscle EMG at the skin surface [4,[6][7]. Furthermore, the lack of proprioceptive feedback is one of the major disadvantages for SEMG-based control [2,[3][4][5].…”