Electromyography-Based Hand Gesture Recognition System for Upper Limb Amputees

Pancholi, Sidharth; Joshi, Amit M.

doi:10.1109/lsens.2019.2898257

Cited by 60 publications

(28 citation statements)

References 10 publications

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“…Which is amplified in the following stages where this offset becomes very noticeable. Table III are comparable to other studies [12,13,14] and thus reaffirm the potential viability of the system. www.ijacsa.thesai.org…”

Section: Literature Of Over Fifty Research Articles Was Revised Fromsupporting

confidence: 88%

Multi-Channel Muscle Armband Implementation: Electronic Circuit Validation and Considerations towards Medical Device Regulation Assessment

Loli¹,

Regina²,

Agueda³

et al. 2020

IJACSA

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Multi-channel muscle arrays are commonly used as sensors in bionic prosthetic devices offering an innovative solution to recover motion in transradial amputees. This study presents preliminary assessments towards validation of a muscle armband for usage in transradial users. Analog and digital components were designed based on medical agencies' recommendations to assess future compliance with Latin American medical device regulations. The study follows two approaches, an exploratory and pre-experimental design. Design was validated and confronted among research literature and medical device regulations. For validation, a pre-experimental design was guided by a quantitative paradigm. Muscle signal was assessed before and after the condition circuit for up to four muscle signals in real time. The present study considers both the conditioning muscle signal circuit and the embedded logic implementation to record signals from the designed muscle armband. Results show that the proposed device allows to record noninvasive signals with a frequency from 20-500 Hz.

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Section: Literature Of Over Fifty Research Articles Was Revised Fromsupporting

confidence: 88%

Multi-Channel Muscle Armband Implementation: Electronic Circuit Validation and Considerations towards Medical Device Regulation Assessment

Loli¹,

Regina²,

Agueda³

et al. 2020

IJACSA

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show abstract

“…However, the SVM algorithm requires a great deal of training and can not incorporate domain knowledge [41]. A gesture detection and recognition system by decoding EMG is presented with four time-domain features and an LDA classifier [42]. The hand gesture recognition for the real-time interface of the LDA as a classifier is designed in [43].…”

Section: Related Workmentioning

confidence: 99%

A novel autonomous learning framework to enhance sEMG-based hand gesture recognition using depth information

Zhou

Zhang

et al. 2021

Biomedical Signal Processing and Control

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Hand gesture recognition using Surface Electromyography (sEMG) has been one of the most efficient motion analysis techniques in human-computer interaction in the last few decades. In particular, multichannel sEMG techniques have achieved stable performance in hand gesture recognition. However, the general solution of collecting and labeling large data manually leads to time-consuming implementation. A novel learning method is therefore needed to facilitate efficient data collection and preprocessing. In this paper, a novel autonomous learning framework is proposed to integrate the benefits of both depth vision and EMG signals, which automatically label the class of collected EMG data using depth information. It then utilizes a multiple layer neural network (MNN) classifier to achieve real-time recognition of the hand gestures using only the sEMG. The overall framework is demonstrated in an augmented reality application by the recognition of ten hand gestures using the Myo armband and an HTC VIVE PRO. The results show prominent

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“…These hands can perform various hand gestures and grip motions in a manner that is similar 2 of 13 to the human hand owing to their additional DOFs. However, because it is difficult to control multi-DOF myoelectric hand prostheses with the existing control method [8,9], many researchers utilize a pattern-recognition-based control method that can perform various hand movements using the EMG signals collected from the residual muscle at the amputation site [10][11][12][13][14][15][16][17][18][19][20][21]. The techniques employed incorporate various types of EMG pattern feature extractions and classifiers to achieve a classification accuracy of more than 95% for pattern recognition (PR) control of the multi-DOF myoelectric hand prosthesis [22].…”

Section: Introductionmentioning

confidence: 99%

Development of Multifunctional Myoelectric Hand Prosthesis System with Easy and Effective Mode Change Control Method Based on the Thumb Position and State

Jung

Kim

et al. 2021

Applied Sciences

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Commercial multi-degrees-of-freedom (multi-DOF) myoelectric hand prostheses can perform various hand gestures and grip motions using multiple DOFs. However, as most upper limb amputees have less than two electromyogram (EMG) signals generated at the amputation site, it is difficult to control various hand gestures and grip motions using multi-DOF myoelectric hand prostheses. This paper proposes a multifunctional myoelectric hand prosthesis system that uses only two EMG sensors while improving the convenience of upper limb amputees in everyday life. The proposed system comprises a six-DOF myoelectric hand prosthesis and an easy and effective control algorithm that enables upper limb amputees to perform various hand gestures and grip motions. More specifically, the hand prosthesis has a multi-DOF five-finger mechanism and a small controller that can be mounted inside the hand, allowing it to perform various hand gestures and grip motions. The control algorithm facilitates four grip motions and four gesture motions using the adduction and abduction positions of the thumb, the flexion and extension state of the thumb, and three EMG signals (co-contraction, flexion, and extension) generated using the two EMG sensors. Experimental results indicate that the proposed system is a versatile, flexible, and effective hand prosthesis system for upper limb amputees.

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Electromyography-Based Hand Gesture Recognition System for Upper Limb Amputees

Cited by 60 publications

References 10 publications

Multi-Channel Muscle Armband Implementation: Electronic Circuit Validation and Considerations towards Medical Device Regulation Assessment

Multi-Channel Muscle Armband Implementation: Electronic Circuit Validation and Considerations towards Medical Device Regulation Assessment

A novel autonomous learning framework to enhance sEMG-based hand gesture recognition using depth information

Development of Multifunctional Myoelectric Hand Prosthesis System with Easy and Effective Mode Change Control Method Based on the Thumb Position and State

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