AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscl...

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Published inAdvanced science Vol. 11; no. 16; pp. e2305025 - n/a
Main Authors Suo, Jiao, Liu, Yifan, Wang, Jianfei, Chen, Meng, Wang, Keer, Yang, Xiaomeng, Yao, Kuanming, Roy, Vellaisamy A. L., Yu, Xinge, Daoud, Walid A., Liu, Na, Wang, Jianping, Wang, Zuobin, Li, Wen Jung
Format Journal Article
LanguageEnglish
Published Germany John Wiley & Sons, Inc 01.04.2024
John Wiley and Sons Inc
Wiley
Subjects
Adult
Artificial Intelligence
Deformation
Electrodes
Electromyography
Electromyography - instrumentation
Electromyography - methods
Equipment Design
human motion recognition
Humans
Male
Measurement techniques
mechanomyography
Muscle contraction
Muscle function
Muscle, Skeletal - physiology
Myography - instrumentation
Myography - methods
natural human–machine interaction
non‐intrusive muscle activities sensing
Pressure distribution
Sensors
Skin
Vibration
Wearable computers
wearable devices
Wearable Electronic Devices
wearable devices
natural human–machine interaction
non‐intrusive muscle activities sensing
mechanomyography
human motion recognition
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Abstract Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age. A non‐invasive wearable device has been developed, incorporating a soft pressure sensor array that enables simultaneous detection of muscle deformation and mechanomyography. By leveraging machine learning techniques, this device demonstrates its ability to recognize a minimum of ten distinct lower limb motions, showcasing significant potential for future metaverse applications.
AbstractList Abstract Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.
Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.
Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age. A non‐invasive wearable device has been developed, incorporating a soft pressure sensor array that enables simultaneous detection of muscle deformation and mechanomyography. By leveraging machine learning techniques, this device demonstrates its ability to recognize a minimum of ten distinct lower limb motions, showcasing significant potential for future metaverse applications.
Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.
Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.
Author Yu, Xinge
Suo, Jiao
Yao, Kuanming
Wang, Jianping
Wang, Jianfei
Yang, Xiaomeng
Liu, Na
Chen, Meng
Wang, Zuobin
Liu, Yifan
Li, Wen Jung
Wang, Keer
Roy, Vellaisamy A. L.
Daoud, Walid A.
AuthorAffiliation 4 Dept. of Biomedical Engineering City University of Hong Kong Hong Kong 999077 China
2 Dept. of Electrical and Computer Engineering Michigan State University MI 48840 USA
7 Dept. of Computer Science City University of Hong Kong Hong Kong 999077 China
3 The Int. Research Centre for Nano Handling and Manufacturing of China Changchun University of Science and Technology Changchun 130022 China
5 James Watt School of Engineering University of Glasgow Scotland G12 8QQ UK
1 Dept. of Mechanical Engineering City University of Hong Kong Hong Kong 999077 China
6 Sch. of Mechatronic Engineering and Automation Shanghai University Shanghai 200444 China
AuthorAffiliation_xml – name: 5 James Watt School of Engineering University of Glasgow Scotland G12 8QQ UK
– name: 7 Dept. of Computer Science City University of Hong Kong Hong Kong 999077 China
– name: 6 Sch. of Mechatronic Engineering and Automation Shanghai University Shanghai 200444 China
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– name: 3 The Int. Research Centre for Nano Handling and Manufacturing of China Changchun University of Science and Technology Changchun 130022 China
– name: 2 Dept. of Electrical and Computer Engineering Michigan State University MI 48840 USA
– name: 4 Dept. of Biomedical Engineering City University of Hong Kong Hong Kong 999077 China
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38376001$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH
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Issue 16
Keywords wearable devices
natural human–machine interaction
non‐intrusive muscle activities sensing
mechanomyography
human motion recognition
Language English
License Attribution
2023 The Authors. Advanced Science published by Wiley‐VCH GmbH.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Snippet Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for...
Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for...
Abstract Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach...
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StartPage e2305025
SubjectTerms Adult
Artificial Intelligence
Deformation
Electrodes
Electromyography
Electromyography - instrumentation
Electromyography - methods
Equipment Design
human motion recognition
Humans
Male
Measurement techniques
mechanomyography
Muscle contraction
Muscle function
Muscle, Skeletal - physiology
Myography - instrumentation
Myography - methods
natural human–machine interaction
non‐intrusive muscle activities sensing
Pressure distribution
Sensors
Skin
Vibration
Wearable computers
wearable devices
Wearable Electronic Devices
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Title AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fadvs.202305025
https://www.ncbi.nlm.nih.gov/pubmed/38376001
https://www.proquest.com/docview/3046632257
https://www.proquest.com/docview/2928853034
https://pubmed.ncbi.nlm.nih.gov/PMC11040359
https://doaj.org/article/17ba7aaa31824ff9b5b9d62e57cbe836
Volume 11
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