A Comprehensive Review of Myoelectric Prosthesis Control

Mohebbian, Mohammad Reza; Nosouhi, Marjan; Fazilati, Farzaneh; Esfahani, Zahra Nasr; Amiri, Golnaz; Malekifar, Negar; Yusefi, Fatemeh; Rastegari, Mohsen; Marateb, Hamid Reza

doi:10.48550/arxiv.2112.13192

Cited by 2 publications

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“…Despite the progress made in the past decade in areas such as biocompatible electrodes, surgical paradigms, and mechatronics integration [ 7 ], the development of current myoelectric prosthetic hand control schemes have been hindered by limitations in the field of motion intention recognition, resulting in a lack of intuitive and robust human–machine interfaces. Previous works have reviewed various aspects of research progress, including methods for predicting continuous upper limb movements based on sEMG [ 8 ], the application of deep learning in multi-task human–machine interaction (HMI) based on sEMG [ 9 ], and various performance indices in myoelectric control [ 10 ]. Existing myoelectric control research for prosthetic hand interaction mainly focuses on intention recognition and control strategy, aiming to accurately decode human intention through recognition algorithms and drive the prosthetic hand to execute the intent through control algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Review of Myoelectric Control for Prosthetic Hand Manipulation

et al. 2023

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Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

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Section: Introductionmentioning

confidence: 99%

A Review of Myoelectric Control for Prosthetic Hand Manipulation

et al. 2023

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“…This technique is classified as either non-invasive, including surface electromyography (sEMG), electroencephalography (EEG), forcemyography (FMG), mechanomyography (MMG), magnetoencephalography (MEG), force sensitive resistance (FSR), and magnetomicrometry (MM), with the last one being presently developed in MIT [ 15 ], or invasive, including implanted electromyography (iEMG), myoelectric implantable recording arrays (MIRAs), electroneurography (ENG), electrocorticography (ECoG), brain–chip interfaces (BCHIs), and magnetomicrometry (MM) [ 16 ]. Among all of these techniques, sEMG is the most commonly used method for prosthesis control, which has been studied very extensively [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

Nazari

Zheng

2023

Sensors

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This paper presents a critical review and comparison of the results of recently published studies in the fields of human–machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords “Human Machine Interface”, “Sonomyography”, “Ultrasound”, “Upper Limb Prosthesis”, “Artificial Intelligence”, and “Non-Invasive Sensors” was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human–machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

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A Comprehensive Review of Myoelectric Prosthesis Control

Cited by 2 publications

References 0 publications

A Review of Myoelectric Control for Prosthetic Hand Manipulation

A Review of Myoelectric Control for Prosthetic Hand Manipulation

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

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