Development of a myoelectric control scheme based on a time delayed neural network

Smith, Alan; Nanda, Pooja; Brown, Edward E.

doi:10.1109/iembs.2009.5332846

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…First, it does not rely on repeated isometric contractions and can be used to identify instantaneous muscle contractions based on natural motion. Second, it can recognize the motion of 2 DOFs at the same time (Smith et al, 2009 ). TDNN processing of combined with sEMG and kinematics data could show excellent performance in the prediction of simultaneous motion of shoulder joint and elbow joint (Kwon and Kim, 2011 ; Blana et al, 2016 ; Day et al, 2020 ).…”

Section: Pattern Recognition-based Semgmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

102

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Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

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Section: Pattern Recognition-based Semgmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

102

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“…The system has been tested and evaluated for functionality and robustness. So far we have used it to collect joint angle and EMG data to make preliminary correlations between an EMG signal and the angle of motion during arm reaching movements in the sagittal plane [13]. The velocity/acceleration of the arm during any movement is also expected to be correlated with the EMG signal.…”

Section: Discussionmentioning

confidence: 99%

Design and development of an upper extremity motion capture system for a rehabilitation robot

Nanda

Smith

Gebregiorgis

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Human robot interaction is a new and rapidly growing field and its application in the realm of rehabilitation and physical care is a major focus area of research worldwide. This paper discusses the development and implementation of a wireless motion capture system for the human arm which can be used for physical therapy or real-time control of a robotic arm, among many other potential applications. The system is comprised of a mechanical brace with rotary potentiometers inserted at the different joints to capture position data. It also contains surface electrodes which acquire electromyographic signals through the CleveMed BioRadio device. The brace interfaces with a software subsystem which displays real time data signals. The software includes a 3D arm model which imitates the actual movement of a subject's arm under testing. This project began as part of the Rochester Institute of Technology's Undergraduate Multidisciplinary Senior Design curriculum and has been integrated into the overall research objectives of the Biomechatronic Learning Laboratory.

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“…The upper limb prosthesis controlled by electromyography is refer to Myo electric control that is non-invasively recording via. surface electrodes built inside the socket of the upper limb prosthetic (like prosthetic for transradial amputees) (Smith et al, 2009). Transradial or below elbow amputation is partial removal of the forearm below the elbow joint which affects the hand function by hand loss (amputation) (Morgan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Improving EMG Control of Upper Limb Prostheses with Myo Armband Device for Transradial Amputees

Kadhim¹,

Abbas²,

Hussain³

2020

J. Eng. Appl. Sci.

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The study improve sEMG signals, especially, for the transradial amputees, it investigated the classification accuracy of surface Electromyography (sEMG) with low-cost Myo armband device. Seventeen specific viable gestures used in daily-life were compared between two groups, the first one was represented by six transradial amputees and the second one was represented by six able-bodied subjects. In addition, the measured signals from forearm muscles were utilized to drive 3D printed prosthetic hand controlled by Pattern Recognition (PR) technique. In order to analyze the acquired sEMG signals, the PR system employed which consists of three main parts: segmentation, feature extraction and classification. The LDA classifier was employed in order to obtain optimum accuracy of the system. The classification accuracy of Myo armband showed high performance where the achieved classification accuracy for abled bodies subjects was more than (94.85%) with LDA classifier and for transradial amputees subjects (88.2%) with LDA classifier. Also, it was acceptable for 3D printed hand controller for 7 movements in study case of transradial amputees. The results may conclude that low cost Myo armband can be applicable for controlling 3D printed prosthetic hand to distinguish hand movements.

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Development of a myoelectric control scheme based on a time delayed neural network

Cited by 13 publications

References 14 publications

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Design and development of an upper extremity motion capture system for a rehabilitation robot

An Investigation of Improving EMG Control of Upper Limb Prostheses with Myo Armband Device for Transradial Amputees

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