User experience of controlling the DEKA Arm with EMG pattern recognition

Resnik, Linda; Acluche, Frantzy; Klinger, Shana Lieberman

doi:10.1371/journal.pone.0203987

Cited by 31 publications

(41 citation statements)

References 21 publications

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“…It is possible that the weight of the prosthesis impacted EMG-PR users differentially. This finding is supported by data from our qualitative study in which users explained how the device weight led to fatigue and contributed to problems in consistency of controls operation [17]. However qualitative analyses did not provide insight into why EMG-PR users may have been less satisfied with grip and wrist display.…”

Section: Discussionmentioning

confidence: 67%

“…In contrast, there were no differences in usability ratings for TH amputees using EMG-PR or IMUs, but statistically significant differences in satisfaction items related to the full arm system, wires and cables, Batteries Subscale and internal battery life. Prior analyses reported that the EMG-PR system drained the internal battery quickly, and provides rich examples of user complaints about the external wires and cables [17].…”

Section: Discussionmentioning

confidence: 99%

“…The lowest ratings were for saving and deleting calibration data, reproducing consistent patterns during calibration, and wrist calibration. Qualitative analyses described user challenges with learning to use and using the calibration function, which help to explain these ratings [17]. The EMG-PR system could not save a calibration that had worked well and reload it at another time.…”

Section: Discussionmentioning

confidence: 99%

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EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability

Resnik

Acluche

Borgia

et al. 2018

IEEE J. Transl. Eng. Health Med.

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The DEKA Arm has multiple degrees of freedom which historically have been operated primarily by inertial measurement units (IMUs). However, the IMUs are not appropriate for all potential users; new control methods are needed. The purposes of this study were: 1) to describe usability and satisfaction of two controls methods—IMU and myoelectric pattern recognition (EMG-PR) controls—and 2) to compare ratings by control and amputation level. A total of 36 subjects with transradial (TR) or transhumeral (TH) amputation participated in the study. The subjects included 11 EMG-PR users (82% TR) and 25 IMU users (68% TR). The study consisted of in-laboratory training (Part A) and home use (Part B). The subjects were administered the Trinity Amputation and Prosthesis Experience satisfaction scale and other usability and satisfaction measures. Wilcoxon rank-sum tests compared the differences by control type. The differences were compared for those who did and did not want a DEKA Arm. The preferences for features of the DEKA Arm were compared by control type. The comparisons revealed poorer ratings of skill, comfort, and weight among EMG-PR users. The TR amputees using IMUs rated usability more favorably. TH amputees rated usability similarly. The TR amputees using EMG-PR were less satisfied with weight, pinch grip, and wrist display, whereas the TH amputees were less satisfied with the full system, wires/cables, and battery. Usability and satisfaction declined after Part B for EMG-PR users. Overall, we found that the IMU users rated the DEKA Arm and the controls more favorably than the EMG-PR users. The findings indicate that the EMG-PR system we tested was less well accepted than the IMUs for control of the DEKA Arm.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability

Resnik

Acluche

Borgia

et al. 2018

IEEE J. Transl. Eng. Health Med.

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“…However, training daily provides new users with the best control algorithm given the EMG features collected on that day and our experience suggests that over time they will become more stereotyped and thus have improved control. From the start, our training requires less time than pattern recognition algorithms which can require 14-40 h of upfront, inlab training with experienced professionals (Resnik et al, 2017(Resnik et al, , 2018a.…”

Section: Failedmentioning

confidence: 99%

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm

et al. 2020

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This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use-essential information to determine clinical relevance and improve future research for advanced bionic arms.

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“…Sense -Infinite Biomedical Technologies; Myo Plus -Ottobock). Although pattern recognition algorithms enable successful real-time grasp classification (15)(16)(17) and allow for control of a prosthetic hand during real-world functional tasks (18)(19)(20)(21), users and therapists both report that extended periods of training are typically necessary to achieve stable performance (22,23). It is important to note that training to use a prosthesis involves multiple stages-including learning about the underlying functioning of the control system and how to generate the requisite control signals (conceptual training), learning to control a physical prosthesis (control training), and learning to use the prosthesis for completing functional tasks (functional training) (24).…”

mentioning

confidence: 99%

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

Engdahl¹,

Dhawan²,

Bashatah³

et al. 2020

Preprint

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Background: Although surface electromyography is commonly used as a sensing strategy for upper limb prostheses, it remains difficult to reliably decode the recorded signals for controlling multi-articulated hands. Sonomyography, or ultrasound-based sensing of muscle deformation, overcomes some of these issues and allows individuals with upper limb loss to reliably perform multiple motion patterns. The purposes of this study were to determine 1) the effect of training on classification performance with sonomyographic control and 2) the effect of training on the underlying muscle deformation patterns.Methods: A series of motion pattern datasets were collected from five individuals with transradial limb loss. Each dataset contained five ultrasound images corresponding each of the following five motions: power grasp, wrist pronation, key grasp, tripod, point. Participants initially performed the motions for the datasets without receiving feedback on their performance (baseline phase), then with visual and verbal feedback (feedback phase), and finally again without feedback (retention phase). Cross-validation accuracy and metrics describing the consistency and separability of the muscle deformation patters were computed for each dataset. Changes in classification performance over the course of the study were assessed using linear mixed models. Associations between classification performance and the consistency and separability metrics were evaluated using Pearson correlations.Results: The average cross-validation accuracy for each phase was 92% or greater and there was no significant change in cross-validation accuracy throughout training. Misclassifications of one motion as another did not persist systematically across datasets. Few of the correlations were significant, although many were moderate or greater in strength and showed a positive association between accuracy and improved consistency and separability metrics.Conclusions: Participants were able to achieve high classification rates upon their initial exposure to sonomyography and training did not affect their performance. Thus, motion classification using sonomyography may be highly intuitive and is unlikely to require a structured training protocol to gain proficiency.

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User experience of controlling the DEKA Arm with EMG pattern recognition

Cited by 31 publications

References 21 publications

EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability

EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

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