Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Abstract

Abstract-The myoelectric controller (MEC) remains a technological bottleneck in the development of multifunctional prosthetic hands. Current MECs require physiologically inappropriate commands to indicate intent and lack effectiveness in a clinical setting. Postural control schemes use surface electromyography signals to drive a cursor in a continuous twodimensional domain that is then transformed into a hand posture. Here, we present a novel algorithm for a postural controller and test the efficacy of the system during two experiments with 11 total subjects. In the first experiment, we found that performance increased when a velocity cursor-control technique versus a position cursor-control technique was used. Also, performance did not change when using 3, 4, or 12 surface electrodes. In the second experiment, subjects commanded a six degree-of-freedom virtual hand into seven functional postures without training, with completion rates of 82 +/-4%, movement times of 3.5 +/-0.2 s, and path efficiencies of 45 +/ -3%. Subjects retained the ability to use the postural controller at a high level across days after a single 1 h training session. Our results substantiate the novel algorithm for a postural controller as a robust and advantageous design for a MEC of multifunction prosthetic hands.

Learning, Generalization, and Scalability of Abstract Myoelectric Control

“…Our third experiment illustrated that this method can translate to additional control sites and targets. The results of Experiment 3 largely corroborate the findings of previous learning-based myoelectric interfaces that utilized the centre-out task [12]- [15], [17], [19]. These experiments demonstrate that simultaneous proportional control with multiple muscles can be achieved without the use of statistical learning algorithms.…”

“…Human machine interfaces, including myoelectric control, are novel by default and have therefore acted as a catalyst to study how novel motor tasks are learned [12], [16], [23], [34]- [39]. Evidence suggests new mappings can be generated from scratch [20] and they can be arbitrary and non-intuitive [12], [15], although they do not have to be [17], [39], [40].…”

“…A common component linking many of these methods is the presentation of muscle activity as feedback in a non-representational multidimensional space, such as a centre-out task, within which users must learn to control their muscle activity. The driving hypothesis is that once the skill is acquired in the non-representational space, arbitrary mappings may be made to functional outputs, such as proportional control of prosthetic digits [13] or selection of hand postures [17]. Because these methods are characteristically non-biomimetic, we refer to the approach as "abstract decoding" [18].…”

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

“…moving the cursor near to the positive x-axis (±30°) would activate lateral grip, negative x-axis (±30°) would be thumbs up, etc. [49]. The subject then needed to explicitly modulate individual muscle activations using direct control to reach these predefined areas.…”

Comparison of hand and forearm muscle pairs in controlling of a novel myoelectric interface

“…It would be very interesting to quantify if the size to the two middle targets would need to be adjusted automatically or adaptively according to users' performance [29]. Finally, it would be informative to test whether a cursor velocity controller [30], in which quiescent muscle activity leads to a stationary cursor, is more appropriate than our cursor position control.…”