Brain-machine and muscle-machine bio-sensing methods for gesture intent acquisition in upper-limb prosthesis control: a review

Abstract

This paper presents a review of a number of bio-sensing methods for gesture intent signal acquisition in control tasks for upper-limb prosthesis. The paper specifically provides a breakdown of the control task in myoelectric prosthesis, and in addition, highlights and describes the importance of the acquisition of a high-quality bio-signal. The paper also describes commonly used invasive and non-invasive brain and muscle machine interfaces such as electroencephalography, electrocorticography, electroneurography, surface electromyography, sonomyography, mechanomyography, near infra-red, force sensitive resistance/pressure, and magnetoencephalography. Each modality is reviewed based on its operating principle and limitations in gesture recognition, followed by respective advantages and disadvantages. Also described within this paper, are multimodal sensing approaches, which involve data fusion of information from various sensing modalities for an enhanced neuromuscular bio-sensing source. Using a semi-systematic review methodology, we are able to derive a novel tabular approach towards contrasting the various strengths and weaknesses of the reviewed bio-sensing methods towards gesture recognition in a prosthesis interface. This would allow for a streamlined method of down selection of an appropriate bio-sensor given specific prosthesis design criteria and requirements. The paper concludes by highlighting a number of research areas that require more work for strides to be made towards improving and enhancing the connection between man and machine as it concerns upper-limb prosthesis. Such areas include classifier augmentation for gesture recognition, filtering techniques for sensor disturbance rejection, feeling of tactile sensations with an artificial limb.

Keywords:

• Biosensing
• cybernetics
• man-machine
• prosthesis control
• upper limb amputee

Acknowledgements

The author would like to thank Dr Eliza Condrea for providing feedback on the manuscript, Mike Fraser for the suggestion to carry out the work done as part of Table 1 and Kerr Editing Services (https://www.kerrediting.com/) for proofreading the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).