Functional Assessment of a Myoelectric Postural Controller and Multi-Functional Prosthetic Hand by Persons With Trans-Radial Limb Loss

Abstract

The functional assessment of myoelectric control algorithms by persons with amputation promotes the overarching goal of the field of prosthetic limb design: to replace what was lost. However, many studies use experimental paradigms with virtual interfaces and able-bodied subjects that do not capture the challenges of a clinical implementation with an amputee population. A myoelectric control system must be robust to variable physiology, loading effects of the prosthesis on the limb, and limb position effects during dynamic tasks. Here persons with transradial limb loss performed activities of daily living using a postural controller and multi-functional prosthetic hand in order to verify that the postural controller was robust to these clinical challenges. The Southampton Hand Assessment Procedure was performed by persons with limb loss and able-bodied subjects. The results indicate that persons with limb loss and able-limbed subjects achieved the same performance and therefore that the clinical challenges were overcome. Persons with limb loss achieved 55% of physiological hand function on average. Also, the postural controller is compared to other state of the art myoelectric controllers and prosthetic hands previously tested. This work confirms that the postural controller is potentially a clinically-viable method to control myoelectric multi-functional prosthetic hands.

A Classification Method for Myoelectric Control of Hand Prostheses Inspired by Muscle Coordination

“…Content may change prior to final publication. There is an interesting body of work [47]- [50] exploiting the coordinated activation of muscles in a different way, in particular, by applying a linear transformation on the EMG signals acquired from different muscles for controlling a cursor in 2D space. For example, [47] and [48] represented each muscle as a vector acting along uniformly-spaced directions of action, while the position of a 2D cursor was determined by summing up these vectors, whose magnitude was equal to the amplitude of the EMG signal.…”

Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD

“…There are two methods to measure EMG signals: invasive and noninvasive. For invasive methods, it uses needle electrodes while a noninvasive method uses electrodes above the skin surface of the patient's body [7].…”

“…EMG signals have a wide range of applications in biomedical engineering and it is one of the vital biological parameters, prosthetic devices, and rehabilitation devices [9]. It is a bio-potential signal acquired through the muscle fiber body by electrodes to analyze muscle activity [9] and these signals measure the electrical activity during contraction and relaxation phase of the muscle fiber [7].…”

Multi-modal prosthetic fingertip sensor with proximity, contact, and force localization capabilities

“…Now, advanced feedforward control algorithms which enable more intuitive use of the prosthesis are becoming clinically relevant through many commercial partners. [2][3][4] These myoelectric control algorithms provide improved control of the multi-functional prosthesis over the standard direct control techniques used previously. 3 Now, the largest deficiency in our field is the lack of feedback systems to provide sensory restoration for the user.…”