Preliminary Evaluation of the Effect of Mechanotactile Feedback Location on Myoelectric Prosthesis Performance Using a Sensorized Prosthetic Hand

Wells, Eric D.; Shehata, Ahmed W.; Dawson, Michael R.; Carey, Jason P.; Hebert, Jacqueline S.

doi:10.3390/s22103892

Cited by 7 publications

(5 citation statements)

References 56 publications

(68 reference statements)

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“…The MAPP's previously-published design [67] was altered to improve wearer comfort in our study-the distal ring was made to resemble the oval shape of a wrist and the hand brace was elongated so that the distal ring would sit more proximally on the wearer's wrist. A 3D-printed robotic hand [68] participant's hand. Wrist rotation capabilities were also added to the device.…”

Section: Simulated Prosthesis and Donning Proceduresmentioning

confidence: 99%

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Williams,

Shehata,

Cheng

et al. 2023

Preprint

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Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop user-specific prosthesis controllers that can predict a person’s intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users’ intentions; the timing of specific wrist and hand control functions; and users’ opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details—spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the “limb position effect”. We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

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Section: Simulated Prosthesis and Donning Proceduresmentioning

confidence: 99%

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Williams,

Shehata,

Cheng

et al. 2023

Preprint

Self Cite

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“…Although, the remaining three participants did not comment on that notion. Wells et al ( 2022 ) reported similar findings, where the mechanotactile feedback resulted in an increased time to finish a task. The respective authors believe this effect to be due to the added focus on the task with feedback.…”

Section: Discussionmentioning

confidence: 72%

FeetBack–Redirecting touch sensation from a prosthetic hand to the human foot

Morand¹,

Brusa²,

Schnüriger³

et al. 2022

Front. Neurosci.

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IntroductionAdding sensory feedback to myoelectric prosthetic hands was shown to enhance the user experience in terms of controllability and device embodiment. Often this is realized non-invasively by adding devices, such as actuators or electrodes, within the prosthetic shaft to deliver the desired feedback. However, adding a feedback system in the socket adds more weight, steals valuable space, and may interfere with myoelectric signals. To circumvent said drawbacks we tested for the first time if force feedback from a prosthetic hand could be redirected to another similarly sensitive part of the body: the foot.MethodsWe developed a vibrotactile insole that vibrates depending on the sensed force on the prosthetic fingers. This self-controlled clinical pilot trial included four experienced users of myoelectric prostheses. The participants solved two types of tasks with the artificial hands: 1) sorting objects depending on their plasticity with the feedback insole but without audio-visual feedback, and 2) manipulating fragile, heavy, and delicate objects with and without the feedback insole. The sorting task was evaluated with Goodman-Kruskal's gamma for ranked correlation. The manipulation tasks were assessed by the success rate.ResultsThe results from the sorting task with vibrotactile feedback showed a substantial positive effect. The success rates for manipulation tasks with fragile and heavy objects were high under both conditions (feedback on or off, respectively). The manipulation task with delicate objects revealed inferior success with feedback in three of four participants.ConclusionWe introduced a novel approach to touch sensation in myoelectric prostheses. The results for the sorting task and the manipulation tasks diverged. This is likely linked to the availability of various feedback sources. Our results for redirected feedback to the feet fall in line with previous similar studies that applied feedback to the residual arm.Clinical trial registrationName: Sensor Glove and Non-Invasive Vibrotactile Feedback Insole to Improve Hand Prostheses Functions and Embodiment (FeetBack). Date of registration: 23 April 2019. Date the first participant was enrolled: 3 September 2021. ClinicalTrials.gov Identifier: NCT03924310.

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“…The MAPP’s previously-published design [30] was altered to improve wearer comfort in our study—the distal ring was made to resemble the oval shape of a wrist and the hand brace was elongated so that the distal ring would sit more proximally on the wearer’s wrist. A non-proprietary 3D-printed robotic hand [31] was affixed to the MAPP beneath the participant’s hand. Wrist rotation capabilities were also added to the device.…”

Section: Methodsmentioning

confidence: 99%

Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect

Williams,

Shehata,

Cheng

et al. 2024

Preprint

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Although state-of-the-art myoelectric prostheses offer persons with upper limb amputation extensive movement capabilities, users have not been afforded a reliable means to control common movements required in daily living. Many proposed prosthesis controllers use pattern recognition, a method that learns and recognizes patterns of electromyographic (EMG) signals produced by the user's residual limb muscles to predict and execute device movements. Such control becomes unreliable in high limb positions---a problem known as the limb position effect. Pattern recognition often uses a classification algorithm; simple to implement, but limits user-initiated control to only one device movement at a time, at a single speed. To combat position-related control deficiencies and classification controller constraints, we developed and tested two recurrent convolutional neural network (RCNN) pattern recognition-based solutions: (1) an RCNN classification controller that uses EMG plus positional inertial measurement unit (IMU) signals to offer one-speed, sequential movement control; and (2) an RCNN regression controller that uses the same data capture technique to offer simultaneous control of multiple movements and device movement velocity. We assessed both RCNN controllers by comparing them to a commonly used linear discriminant analysis classification controller (LDA-Baseline). Participants without upper limb impairment were recruited to perform multipositional tasks while wearing a simulated prosthesis. Both RCNN classification and regression controllers showed improved functional task performance over LDA-Baseline, in 11 and 38 out of 115 metrics, respectively. This work contributes an RCNN regression-based controller that provides accurate, simultaneous, and proportional movements to EMG-based technologies including prostheses, exoskeletons, and even muscle-activated video games.

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Preliminary Evaluation of the Effect of Mechanotactile Feedback Location on Myoelectric Prosthesis Performance Using a Sensorized Prosthetic Hand

Cited by 7 publications

References 56 publications

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

FeetBack–Redirecting touch sensation from a prosthetic hand to the human foot

Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect

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