Task discrimination for non-weight-bearing movements using muscle synergies

Afzal, Taimoor; Iqbal, Kamran; White, Gannon; Wright, Andrew B.

doi:10.1109/embc.2015.7318403

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…More importantly, such movements must be natural, not the result of reaction forces. MPR for the non-weight-bearing condition has been attempted in offline ( 21 ) and real-time studies ( 22 ). Notably, Hargrove et al demonstrated the discrimination of eight leg movements (knee flexion/extension, ankle plantarflexion/dorsiflexion, hip rotation medial/lateral, and tibial rotation medial/lateral) in both non-amputee and amputee subjects by recording surface electromyography (sEMG) signals with bipolar electrodes placed over nine residual thigh muscles ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain

et al. 2017

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Phantom motor execution (PME), facilitated by myoelectric pattern recognition (MPR) and virtual reality (VR), is positioned to be a viable option to treat phantom limb pain (PLP). A recent clinical trial using PME on upper-limb amputees with chronic intractable PLP yielded promising results. However, further work in the area of signal acquisition is needed if such technology is to be used on subjects with lower-limb amputation. We propose two alternative electrode configurations to conventional, bipolar, targeted recordings for acquiring surface electromyography. We evaluated their performance in a real-time MPR task for non-weight-bearing, lower-limb movements. We found that monopolar recordings using a circumferential electrode of conductive fabric, performed similarly to classical bipolar recordings, but were easier to use in a clinical setting. In addition, we present the first case study of a lower-limb amputee with chronic, intractable PLP treated with PME. The patient’s Pain Rating Index dropped by 22 points (from 32 to 10, 68%) after 23 PME sessions. These results represent a methodological advancement and a positive proof-of-concept of PME in lower limbs. Further work remains to be conducted for a high-evidence level clinical validation of PME as a treatment of PLP in lower-limb amputees.

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Section: Introductionmentioning

confidence: 99%

Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain

et al. 2017

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“…Duplicates (527), title (320), non-English (15), Patent (10), non-human (4), rehabilitation (35), exoskeleton (22), other bio signals (18), other reasons (81) This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.…”

Section: Exclusionmentioning

confidence: 99%

“…in the reviewed studies were of variable format and the research participants were asked to perform movements with their phantom (individuals with amputation) or intact (able bodied research participants) limb. In some studies the research participants were asked to mimic pre-programmed motion trajectories [30]- [34] or to perform isolated single joint movements (one degree of freedom (DOF)) [35]- [37]. In other studies the participants controlled a virtual object in a 2D or 3D space [11], [38]- [41].…”

Section: Exclusionmentioning

confidence: 99%

Electromyography-based control of lower limb prostheses: a systematic review

Ahkami¹,

Ahmed²,

Thesleff³

et al. 2022

Preprint

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Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate <a>EMG </a>signals as part of the control strategy have increased in the last decade. In this review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched from July-September 2020: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,327 papers were initially assessed and 117 were finally included in this review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated large variations were found across studies between the different control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

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Application of control strategies and machine learning techniques in prosthetic knee: a systematic review

Mohanty

Sabut

2022

Adv. in Comp. Int.

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This systematic review focuses on control strategies and machine learning techniques used in prosthetic knees for restoring mobility of individuals with trans-femoral amputations. Review and classification of control strategies that determine how these prosthetic knees interact with the user and gait strategy inspired algorithms for phase identification, locomotion mode, and motion intention recognition were studied. Relevant studies were identified using electronic databases such as PubMed, EMBASE, SCOPUS, and the Cochrane Controlled Trials Register (Rehabilitation and Related Therapies) up to April 2021. Abstracts were screened and inclusion and exclusion criteria were applied. Out of 278 potentially relevant studies, 65 articles were included. The specific variables on control approach, control modes, gait control, hardware level, machine learning algorithm, and measured signals mechanism were extracted and added to summary table. The results indicate that advanced methods for adapting position or torque depiction and automatic detection of terrains or gait modes are more commonly utilized, but they are largely limited to laboratory environments. It is concluded that a correct combination of control strategies and machine learning techniques will enable the improvement of prosthetic performance and enhance the standard of amputee's lives.

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Task discrimination for non-weight-bearing movements using muscle synergies

Cited by 8 publications

References 11 publications

Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain

Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain

Electromyography-based control of lower limb prostheses: a systematic review

Application of control strategies and machine learning techniques in prosthetic knee: a systematic review

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