Abstract
On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency ( ), raw control with raw feedback resulted in stronger internal model development ( ), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.
Highlights
DECADES of advancements in myoelectric signal acquisition and processing have made myoelectric controlled prostheses a promising option for upper limb amputees [1]
Myoelectric signal variability can contribute to inconsistency in prosthesis control that results in unintended prosthesis movements [5]
A novel framework is used to assess internal model strength. This framework models a positioning task as a function of three variables: the sensory uncertainty (R), the control uncertainty (Q), and the internal model uncertainty (Pparam). These three variables interact to affect performance and decision, but as we show in supplementary material, their individual contributions may be extracted by collecting data for a particular set of psychophysical experiments, including 1) a trial-by-trial adaption rate test and 2) a two-alternative forced-choice test to evaluate JND
Summary
DECADES of advancements in myoelectric signal acquisition and processing have made myoelectric controlled prostheses a promising option for upper limb amputees [1]. Precise real-time decoding of movement intent from highly variable myoelectric signals and adequate methods of providing feedback to users remain a challenge [2]–[4]. Myoelectric signal variability can contribute to inconsistency in prosthesis control that results in unintended prosthesis movements [5]. Many research studies have tackled this issue by exploring feature extraction methods to obtain more useful and robust information from noisy myoelectric signals [6]. Time domain and frequency domain features are some of the most referenced of these features and are commonly used in conjunction with pattern recognition algorithms implemented in myoelectric control systems [7], [8]. The current myoelectric control systems can be broadly categorized as on/off control, proportional control, classifier-based control, and regression-based control [9]
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