Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Abstract Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are...

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Autores principales:	Simon Tam, Mounir Boukadoum, Alexandre Campeau-Lecours, Benoit Gosselin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1b087a49ec934ab69feadec146175a67
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spelling	oai:doaj.org-article:1b087a49ec934ab69feadec146175a672021-12-02T16:53:11ZIntuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning10.1038/s41598-021-90688-42045-2322https://doaj.org/article/1b087a49ec934ab69feadec146175a672021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90688-4https://doaj.org/toc/2045-2322Abstract Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.Simon TamMounir BoukadoumAlexandre Campeau-LecoursBenoit GosselinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Simon Tam Mounir Boukadoum Alexandre Campeau-Lecours Benoit Gosselin Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
description	Abstract Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.
format	article
author	Simon Tam Mounir Boukadoum Alexandre Campeau-Lecours Benoit Gosselin
author_facet	Simon Tam Mounir Boukadoum Alexandre Campeau-Lecours Benoit Gosselin
author_sort	Simon Tam
title	Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
title_short	Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
title_full	Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
title_fullStr	Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
title_full_unstemmed	Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
title_sort	intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1b087a49ec934ab69feadec146175a67
work_keys_str_mv	AT simontam intuitiverealtimecontrolstrategyforhighdensitymyoelectrichandprosthesisusingdeepandtransferlearning AT mounirboukadoum intuitiverealtimecontrolstrategyforhighdensitymyoelectrichandprosthesisusingdeepandtransferlearning AT alexandrecampeaulecours intuitiverealtimecontrolstrategyforhighdensitymyoelectrichandprosthesisusingdeepandtransferlearning AT benoitgosselin intuitiverealtimecontrolstrategyforhighdensitymyoelectrichandprosthesisusingdeepandtransferlearning
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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

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