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      Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

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          Abstract

          The efferent control chain for an upper-limb myoelectric prosthesis can be separated into 3 key areas: signal generation, signal acquisition, and device response. Data were collected from twenty trans-radial myoelectric prosthesis users using their own clinically prescribed devices, to establish the relative impact of these potential control factors on user performance (user functionality and everyday prosthesis usage). By identifying the key factor(s), we can guide future developments to ensure clinical impact. Skill in generating muscle signals was assessed via reaction times and signal tracking. To assess the predictability of signal acquisition, we inspected reaction time spread and undesired hand activations. As a measure of device response, we recorded the electromechanical delay between electrode stimulation and the onset of hand movement. Results suggest abstract measures of skill in controlling muscle signals are poorly correlated with performance. Undesired activations of the hand or incorrect responses were correlated with almost all kinematics and gaze measures suggesting unpredictability is a key factor. Significant correlations were also found between several measures of performance and the electromechanical delay; however, unexpectedly, longer electromechanical delays correlated with better performance. Future research should focus on exploring causes of unpredictability, their relative impacts on performance and interventions to address this.

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          Most cited references26

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          The optimal controller delay for myoelectric prostheses.

          A tradeoff exists when considering the delay created by multifunctional prosthesis controllers. Large controller delays maximize the amount of time available for EMG signal collection and analysis (and thus maximize classification accuracy); however, large delays also degrade prosthesis performance by decreasing the responsiveness of the prosthesis. To elucidate an "optimal controller delay" twenty able-bodied subjects performed the Box and Block Test using a device called PHABS (prosthetic hand for able bodied subjects). Tests were conducted with seven different levels of controller delay ranging from nearly 0-300 ms and with two different artificial hand speeds. Based on repeted measures ANOVA analysis and a linear mixed effects model, the optimal controller delay was found to range between approximately 100 ms for fast prehensors and 125 ms for slower prehensors. Furthermore, the linear mixed effects model shows that there is a linear degradation in performance with increasing delay.
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            Upper-limb prosthetics: critical factors in device abandonment.

            To investigate the roles of predisposing characteristics, established need, and enabling resources in upper-limb prosthesis use and abandonment. A self-administered, anonymous survey was designed to explore these factors. The questionnaire was available online and in paper format and was distributed through healthcare providers, community support groups, and one prosthesis manufacturer. Two hundred forty-two participants of all ages and levels of upper-limb absence completed the survey. Of participants, 20% had abandoned prosthesis use. Predisposing factors, namely, origin of limb absence, gender, bilateral limb absence, and, most importantly, level of limb absence, proved influential in the decision not to wear prostheses. Enabling resources such as the availability of health care, cost, and quality of training did not weigh heavily on prosthesis rejection, with the exception of the fitting time frame and the involvement of clients in the prosthesis selection. Conversely, the state of available technology was a highly censured factor in abandonment, specifically in the areas of comfort and function. Perceived need emerged as a predominant factor in prosthesis use. Future research should focus on continued development of more comfortable and functional prostheses, particularly for individuals with high-level or bilateral limb absence. Improved follow-up, repair, and information services, together with active involvement of clients in the selection of prostheses meeting their specific goals and needs, is recommended.
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              Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor.

              This study aimed to implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method.
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                Author and article information

                Contributors
                a.e.a.chadwell1@salford.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 February 2021
                8 February 2021
                2021
                : 11
                : 3300
                Affiliations
                [1 ]GRID grid.8752.8, ISNI 0000 0004 0460 5971, Centre for Health Sciences Research, , University of Salford, ; Salford, UK
                [2 ]GRID grid.8752.8, ISNI 0000 0004 0460 5971, Salford Business School, , University of Salford, ; Salford, UK
                Author information
                http://orcid.org/0000-0002-9101-5202
                http://orcid.org/0000-0003-2164-3892
                Article
                82764
                10.1038/s41598-021-82764-6
                7870859
                33558547
                941bfc4e-b308-4712-9411-29871d4e67ce
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 September 2020
                : 17 December 2020
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                trauma,health care,medical research
                Uncategorized
                trauma, health care, medical research

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