Myoelectric control systems for hand rehabilitation device: a review

被引：0

作者：

Anam, Khairul ^{[1
]}

Rosyadi, Ahmad Adib ^{[2
]}

Sujanarko, Bambang ^{[1
]}

Al-Jumaily, Adel ^{[3
]}

机构：

[1] Univ Jember, Dept Elect Engn, Jember, Indonesia

[2] Univ Jember, Dept Mech Engn, Jember, Indonesia

[3] Univ Technol Sydney, Sch Biomed Engn, Sydney, NSW, Australia

来源：

2017 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTER SCIENCE AND INFORMATICS (EECSI) | 2017年

关键词：

myoelectric control system; hand rehabilitation device; FEATURE REDUCTION; SURFACE EMG; CLASSIFICATION; RECOGNITION; MACHINE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

One of the challenges of the hand rehabilitation device is to create a smooth interaction between the device and user. The smooth interaction can be achieved by considering myoelectric signal generated by human's muscle. Therefore, the so-called myoelectric control system (MCS) has been developed since the 1940s. Various MCS's has been proposed, developed, tested, and implemented in various hand rehabilitation devices for different purposes. This article presents a review of MCS in the existing hand rehabilitation devices. The MCS can be grouped into main groups, the non-pattern recognition and pattern recognition ones. In term of implementation, it can be classified as MCS for prosthetic and exoskeleton hand. Main challenges for MCS today is the robustness issue that hampers the implementation of MCS on the clinical application.

引用

页码：100 / 105

页数：6

共 45 条

[1] Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography [J].

Al-Timemy, Ali H. ;

Bugmann, Guido ;

Escudero, Javier ;

Outram, Nicholas .

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2013, 17 (03) :608-618

[2] INDEX FINGER MOTION RECOGNITION USING SELF-ADVISE SUPPORT VECTOR MACHINE [J].

Anam, Khairul ;

Al Jumaily, Adel ;

Maali, Yashar .

INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, 2014, 7 (02) :644-657

[3] Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines [J].

Antuvan, Chris Wilson ;

Bisio, Federica ;

Marini, Francesca ;

Yen, Shih-Cheng ;

Cambria, Erik ;

Masia, Lorenzo .

JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2016, 13

[4]

Barros Pablo, 2014, Artificial Neural Networks and Machine Learning - ICANN 2014. 24th International Conference on Artificial Neural Networks. Proceedings: LNCS 8681, P403, DOI 10.1007/978-3-319-11179-7_51

[5] SRDA: An efficient algorithm for large-scale discriminant analysis [J].