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Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

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Abstract

The development of advanced and effective human–machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.

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Abbreviations

CNS:

Central nervous system

EMG:

Electromyograms

ENG:

Electroneurography

EEG:

Electroencephalograhy

EcoG:

Electrocorticography

iEMG:

Intramuscular EMG

sEMG:

Surface EMG

HD-EMG:

High-density EMG

MU:

Motor unit

DOF:

Degree of freedom

MSC:

Myoelectric signal control

PR:

Pattern recognition

PR-MSC:

PR-based MSC

R-MSC:

Regression-based MSC

E-MSC:

Encoding-based MSC

S-MSC:

Synergy-based MSC

PCA:

Principal components analysis

TMR:

Targeted muscle reinnervation

CA:

Classification accuracy

TPR:

True positives rate

FPR:

False positives rate

GUI:

Graphic user interfaces

SHAP:

Southampton hand assessment procedure

TD/FD:

Time/frequency domain

CWT:

Continuous wavelet transform

TENS:

Transcutaneous electric nerve stimulus

HMI:

Human–machine interface

SVM:

Support vector machine

SVDD:

Support vector domain description

DA:

Domain adaptation

LDA:

Linear discriminant analysis

CMCA:

Common model component analysis

RFID:

Radio frequency identification

IMU:

Inertial measurement unit

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (NO. 51675123, NO.51521003) and China Scholarship Council.

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Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, #3039, HIT Science Park, No. 2 Yikuang Street, Nangang District, Harbin, 150081, China

    Dapeng Yang, Yikun Gu & Hong Liu

  2. Artificial Intelligence Laboratory, Harbin Institute of Technology, Harbin, 150001, China

    Dapeng Yang

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21211, USA

    Nitish V. Thakor

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  1. Dapeng Yang
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  2. Yikun Gu
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  3. Nitish V. Thakor
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  4. Hong Liu
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Correspondence to Dapeng Yang.

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Yang, D., Gu, Y., Thakor, N.V. et al. Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration. Exp Brain Res 237, 291–311 (2019). https://doi.org/10.1007/s00221-018-5441-x

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