Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control

Resnik, Linda; Huang, He; Winslow, Anna T.; Crouch, Dustin L.; Zhang, Fan; Wolk, Nancy

doi:10.1186/s12984-018-0361-3

Cited by 132 publications

(148 citation statements)

References 45 publications

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“…To assess the cognitive load related to Thumb use, a numerical cognition task was performed twice, on both the first and the last training sessions (40)(41)(42). Participants were asked to perform a cooperation task, building a Jenga tower (described above), while simultaneously presented with a set of low and high pitch auditory tones played from a laptop.…”

Section: Numerical Cognitionmentioning

confidence: 99%

Neurocognitive consequences of hand augmentation

Kieliba

Clode

Maimon-Mor

et al. 2020

Preprint

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From hand tools to cyborgs, humans have long been fascinated by the opportunities afforded by augmenting ourselves. Here, we studied how motor augmentation with an extra robotic thumb (the Third Thumb) impacts the biological hand representation in the brains of ablebodied people. Participants were tested on a variety of behavioural and neuroimaging tests designed to interrogate the augmented hand's representation before and after 5-days of semi-intensive training. Training improved the Thumb's motor control, dexterity and handrobot coordination, even when cognitive load was increased or when vision was occluded, and resulted in increased sense of embodiment over the robotic Thumb. Thumb usage also weakened natural kinematic hand synergies. Importantly, brain decoding of the augmented hand's motor representation demonstrated mild collapsing of the canonical hand structure following training, suggesting that motor augmentation may disrupt the biological hand representation. Together, our findings unveil critical neurocognitive considerations for designing human body augmentation.

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Section: Numerical Cognitionmentioning

confidence: 99%

Neurocognitive consequences of hand augmentation

Kieliba

Clode

Maimon-Mor

et al. 2020

Preprint

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“…As a result, rest image frames have a higher intra-class dissimilarity compared to other motion classes. However, when rest is excluded, our approach achieves motion distinguishability that is comparable to current myoelectric pattern recognition (PR) systems performing similar motions 48,49 . However, sonomyography was able to achieve this classification accuracy with less than an hour of training, compared to several weeks of training needed for PR to achieve comparable classification accuracy 48 .…”

Section: Discussionmentioning

confidence: 69%

Proprioceptive Sonomyographic Control: A novel method of intuitive proportional control of multiple degrees of freedom for upper-extremity amputees

Dhawan

Mukherjee

Patwardhan

et al. 2018

Preprint

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Technological advances in multi-articulated prosthetic hands have outpaced the methods available to amputees to intuitively control these devices. Amputees often cite difficulty of use as a key contributing factor for abandoning their prosthesis, creating a pressing need for improved control technology. A major challenge of traditional myoelectric control strategies using surface electromyography electrodes has been the difficulty in achieving intuitive and robust proportional control of multiple degrees of freedom. In this paper, we describe a new control method, proprioceptive sonomyographic control that overcomes several limitations of myoelectric control. In sonomyography, muscle mechanical deformation is sensed using ultrasound, as compared to electrical activation, and therefore the resulting control signals can directly control the position of the end effector. Compared to myoelectric control which controls the velocity of the end-effector device, sonomyographic control is more congruent with residual proprioception in the residual limb. We tested our approach with 5 upper-extremity amputees and able-bodied subjects using a virtual target achievement and holding task. Amputees and able-bodied participants demonstrated the ability to achieve positional control for 5 degrees of freedom with an hour of training. Our results demonstrate the potential of proprioceptive sonomyographic control for intuitive dexterous control of multiarticulated prostheses.

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“…It is not surprising that thumb control is a challenge in powered prosthetics {ref} or in the related process of decoding motor intent from electromyography {ref}. The sophisticated algorithms for the control of relatively simpler artificial limbs have yet to reach the robustness and accuracy of their biological counterparts (Crouch and Huang, 2016;Dantas et al, 2019;Resnik et al, 2018). One biomimetic solution is the modelbased prosthetic control mimicking the presence of MS computations within neural computations of planning and execution pathways (Lillicrap and Scott, 2013;Shadmehr et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

Hardesty

Sobinov

et al. 2020

Preprint

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The development of realistic musculoskeletal models is a fundamental goal for the theoretical progress in sensorimotor control and its engineering applications, e.g., in the biomimetic control of artificial limbs. Yet, accurate models require extensive experimental measures to validate structural and functional parameters describing muscle state over the full physiological range of motion. However, available experimental measurements of, for example, muscle moment arms are sparse and often disparate. Validation of these models is not trivial because of the highly complex anatomy and behavior of human limbs. In this study, we developed a method to validate and scale kinematic muscle parameters using posture-dependent moment arm profiles, isometric force measurements, and a computational detection of assembly errors. We used a previously published model with 18 degrees of freedom (DOFs) and 32 musculotendon actuators with force generated from a Hill-type muscle model. The muscle path from origin to insertion with wrapping geometry was used to model the muscle lengths and moment arms. We simulated moment arm profiles across the full physiological range of motion and compared them to an assembled dataset of published and merged experimental profiles. The muscle paths were adjusted using custom metrics based on root-mean-square and correlation coefficient of the difference between simulated and experimental values. Since the available measurements were sparse and the examination of high-dimensional muscles is challenging, we developed analyses to identify common failures, i.e., moment arm functional flipping due to the sign reversal in simulated moments and the imbalance of force generation between antagonistic groups in postural extremes. The validated model was used to evaluate the expected errors in torque generation with the assumption of constant instead of the posture-dependent moment arms at the wrist flexion-extension DOF, which is the critical joint in our model with the largest number of crossing muscles. We found that there was a reduction of joint torques by about 35% in the extreme quartiles of the wrist DOF. The use of realistic musculoskeletal models is essential for the reconstruction of hand dynamics. These models may improve the understanding of muscle actions and help in the design and control of artificial limbs in future applications. New & NoteworthyRealistic models of human limbs are a development goal required for the understanding of motor control and its applications in biomedical fields. However, developing accurate models is restrained by the lack of accurate and reliable musculoskeletal measurements in humans. Here, we have overcome this challenge by using multi-stage validation of simulated structures using both experimental data and the identification of structural failures in the high-dimensional muscle paths. We demonstrate that the rigorous structural and functional validation method is essential for the understanding of force generation at the wrist.

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Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control

Cited by 132 publications

References 45 publications

Neurocognitive consequences of hand augmentation

Neurocognitive consequences of hand augmentation

Proprioceptive Sonomyographic Control: A novel method of intuitive proportional control of multiple degrees of freedom for upper-extremity amputees

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

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