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Application of Acoustic Sensing in Myography Signals
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Application of Acoustic Sensing in Myography Signals

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (26 January 2024) | Viewed by 4604

Special Issue Editors

Prof. Dr. Kosai Raoof

E-Mail Website
Guest Editor
Laboratoire LAUM CNRS UMR 6613, Université de Maine, 72085 Le Mans, France
Interests: wireless communication systems; medium access control (MAC) protocols; wireless sensor network energy consumption
Dr. Youssef Serrestou

E-Mail Website
Guest Editor
Laboratoire LAUM CNRS UMR 6613, Université de Maine, 72085 Le Mans, France
Interests: wireless communication systems; medium access control (MAC) protocols; wireless sensor network energy consumption

Special Issue Information

Dear Colleagues,

Mechanomyography (MMG), in particular the acoustic myogram (AMG), is a non-invasive technique used for recording the sound signal produced by muscles. It can be used to explore the neurophysiological and morphological characteristics of muscles and thus highlight fatigue or even the modification of these characteristics of the latter.

Acoustic myography (AMG) enables a detailed and accurate measurement of those muscles involved in a particular movement, and is independent of electrical signals between the nerve and muscle, measuring solely muscle contractions, unlike surface electromyography (sEMG). With modern amplifiers and digital sound recording systems, measurements during physical activity both inside and outside a laboratory setting are now possible and accurate.

This Special Issue aims to promote innovative studies based on the application of sensors and acoustic myograms in several fields, such as clinics, sports, robotics, and industry; the implementation of innovative methodologies for data analysis; the design of innovative sensors; and the publication of open databases for motion analysis.

Prof. Dr. Kosai Raoof
Dr. Youssef Serrestou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • acoustic myography
  • mechanomyography
  • movement analysis and control
  • fatigue detection
  • pattern recognition
  • human machine interface
  • deep learning
  • wearable sensors
  • artificial intelligence
  • experimental biomechanics
  • muscle sound
  • AMG
  • sEMG
  • hybrid MMG-EMG
  • non-invasive
  • microphones for myography
  • muscle activities

Published Papers (2 papers)

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Research

16 pages, 3662 KiB  
Article
Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression
by Hui-Bin Li, Xiao-Rong Guan, Zhong Li, Kai-Fan Zou and Long He
Sensors 2023, 23(10), 4934; https://doi.org/10.3390/s23104934 - 20 May 2023
Viewed by 1735
Abstract
In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an [...] Read more.
In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R2 of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer’s motion intentions in human–robot collaboration control. Full article
(This article belongs to the Special Issue Application of Acoustic Sensing in Myography Signals)
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16 pages, 2175 KiB  
Article
Evaluation of Electric Muscle Stimulation Method for Haptic Augmented Reality
by Takaya Ishimaru and Satoshi Saga
Sensors 2023, 23(4), 1796; https://doi.org/10.3390/s23041796 - 5 Feb 2023
Viewed by 2239
Abstract
Currently, visual Augmented Reality (AR) technology is widespread among the public. Similarly, haptic AR technology is also widely practiced in the academic field. However, conventional haptic AR devices are not suitable for interacting with real objects. These devices are often held by the [...] Read more.
Currently, visual Augmented Reality (AR) technology is widespread among the public. Similarly, haptic AR technology is also widely practiced in the academic field. However, conventional haptic AR devices are not suitable for interacting with real objects. These devices are often held by the users, and they contact the real object via the devices. Thus, they prevent direct contact between the user and real objects. To solve this problem, we proposed employing Electrical Muscle Stimulation (EMS) technology. EMS technology does not interfere with the interaction between the user and the real object, and the user can wear the device. First, we examined proper stimulus waveforms for EMS, in addition to pulse waveforms. At the same time, we examined the appropriate frequency and pulse width. The waveforms that we used this time were a sawtooth wave, a reverse sawtooth wave, and a sine wave. Second, to clarify the characteristic of the force presented by the EMS, we measured the relationship between the input voltage and the force presented and obtained the point of subjective equality using the constant method. Subsequently, we presented the bump sensation using EMS to the participants and verified its effectiveness by comparing it with the existing methods. Full article
(This article belongs to the Special Issue Application of Acoustic Sensing in Myography Signals)
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