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Sensors and Technologies in Skeletal Muscle Disorder

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

Deadline for manuscript submissions: closed (10 September 2022) | Viewed by 23479

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Special Issue Editor

Dr. Taco J. Blokhuis

E-Mail Website
Guest Editor

Maastricht University, P.O. Box 5800, 6202AZMaastricht, the Netherlands
Interests: rehabilitation after trauma; muscle mass; fracture healing; bone repair

Special Issue Information

Dear Colleagues,

Sarcopenia is a progressive skeletal muscle disorder with loss of muscle mass, strength, and function. It is common, and is related to poor clinical outcome and increased mortality. Adequate quantification or visualization of muscle mass is needed in order to identify individuals with skeletal muscle disorders. Although a wide array of tools is available, varying investigational settings and lack of homogeneity of populations influence the definition of a gold standard. In this Special Issue, upcoming techniques for the quantification or visualization of muscle mass are described. The scope of this Special Issue includes new sensors, new technologies, and new techniques to measure muscle tissue or body composition. Manuscripts covering these techniques in a wide variety of settings, ranging from clinical to laboratory settings, are welcomed.

Dr. Taco J. Blokhuis
Guest Editor

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.

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Keywords

sarcopenia
muscle mass
cachexia

Published Papers (6 papers)

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Research

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19 pages, 4188 KiB

Open AccessArticle

A Custom-Made Electronic Dynamometer for Evaluation of Peak Ankle Torque after COVID-19

by Iulia Iovanca Dragoi, Florina Georgeta Popescu, Teodor Petrita, Florin Alexa, Romulus Fabian Tatu, Cosmina Ioana Bondor, Carmen Tatu, Frank L. Bowling, Neil D. Reeves and Mihai Ionac

Sensors 2022, 22(5), 2073; https://doi.org/10.3390/s22052073 - 07 Mar 2022

Cited by 2 | Viewed by 2398

Abstract

The negative effects of SARS-CoV-2 infection on the musculoskeletal system include symptoms of fatigue and sarcopenia. The aim of this study is to assess the impact of COVID-19 on foot muscle strength and evaluate the reproducibility of peak ankle torque measurements in time by using a custom-made electronic dynamometer. In this observational cohort study, we compare two groups of four participants, one exposed to COVID-19 throughout measurements and one unexposed. Peak ankle torque was measured using a portable custom-made electronic dynamometer. Ankle plantar flexor and dorsiflexor muscle strength was captured for both feet at different ankle angles prior and post COVID-19. Average peak torque demonstrated no significant statistical differences between initial and final moment for both groups (p = 0.945). An increase of 4.8%, p = 0.746 was obtained in the group with COVID-19 and a decrease of 1.3%, p = 0.953 was obtained in the group without COVID-19. Multivariate analysis demonstrated no significant differences between the two groups (p = 0.797). There was a very good test–retest reproducibility between the measurements in initial and final moments (ICC = 0.78, p < 0.001). In conclusion, peak torque variability is similar in both COVID-19 and non-COVID-19 groups and the custom-made electronic dynamometer is a reproducible method for repetitive ankle peak torque measurements. Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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22 pages, 6061 KiB

Open AccessArticle

MAS: Standalone Microwave Resonator to Assess Muscle Quality

by Viktor Mattsson, Leanne L. G. C. Ackermans, Bappaditya Mandal, Mauricio D. Perez, Maud A. M. Vesseur, Paul Meaney, Jan A. Ten Bosch, Taco J. Blokhuis and Robin Augustine

Sensors 2021, 21(16), 5485; https://doi.org/10.3390/s21165485 - 14 Aug 2021

Cited by 13 | Viewed by 3172

Abstract

Microwave-based sensing for tissue analysis is recently gaining interest due to advantages such as non-ionizing radiation and non-invasiveness. We have developed a set of transmission sensors for microwave-based real-time sensing to quantify muscle mass and quality. In connection, we verified the sensors by 3D simulations, tested them in a laboratory on a homogeneous three-layer tissue model, and collected pilot clinical data in 20 patients and 25 healthy volunteers. This report focuses on initial sensor designs for the Muscle Analyzer System (MAS), their simulation, laboratory trials and clinical trials followed by developing three new sensors and their performance comparison. In the clinical studies, correlation studies were done to compare MAS performance with other clinical standards, specifically the skeletal muscle index, for muscle mass quantification. The results showed limited signal penetration depth for the Split Ring Resonator (SRR) sensor. New sensors were designed incorporating Substrate Integrated Waveguides (SIW) and a bandstop filter to overcome this problem. The sensors were validated through 3D simulations in which they showed increased penetration depth through tissue when compared to the SRR. The second-generation sensors offer higher penetration depth which will improve clinical data collection and validation. The bandstop filter is fabricated and studied in a group of volunteers, showing more reliable data that warrants further continuation of this development. Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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13 pages, 2428 KiB

Open AccessArticle

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

by Leanne L. G. C. Ackermans, Leroy Volmer, Leonard Wee, Ralph Brecheisen, Patricia Sánchez-González, Alexander P. Seiffert, Enrique J. Gómez, Andre Dekker, Jan A. Ten Bosch, Steven M. W. Olde Damink and Taco J. Blokhuis

Sensors 2021, 21(6), 2083; https://doi.org/10.3390/s21062083 - 16 Mar 2021

Cited by 20 | Viewed by 4139

Abstract

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers. Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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16 pages, 2485 KiB

Open AccessArticle

Intermuscular Coordination in the Power Clean Exercise: Comparison between Olympic Weightlifters and Untrained Individuals—A Preliminary Study

by Paulo D. G. Santos, João R. Vaz, Paulo F. Correia, Maria J. Valamatos, António P. Veloso and Pedro Pezarat-Correia

Sensors 2021, 21(5), 1904; https://doi.org/10.3390/s21051904 - 09 Mar 2021

Cited by 6 | Viewed by 3772

Abstract

Muscle coordination in human movement has been assessed through muscle synergy analysis. In sports science, this procedure has been mainly applied to the comparison between highly trained and unexperienced participants. However, the lack of knowledge regarding strength training exercises led us to study the differences in neural strategies to perform the power clean between weightlifters and untrained individuals. Synergies were extracted from electromyograms of 16 muscles of ten unexperienced participants and seven weightlifters. To evaluate differences, we determined the pairwise correlations for the synergy components and electromyographic profiles. While the shape of activation patterns presented strong correlations across participants of each group, the weightings of each muscle were more variable. The three extracted synergies were shifted in time with the unexperienced group anticipating synergy #1 (−2.46 ± 18.7%; p < 0.001) and #2 (−4.60 ± 5.71%; p < 0.001) and delaying synergy #3 (1.86 ± 17.39%; p = 0.01). Moreover, muscle vectors presented more inter-group variability, changing the composition of synergy #1 and #3. These results may indicate an adaptation in intermuscular coordination with training, and athletes in an initial phase of training should attempt to delay the hip extension (synergy #1), as well as the upper-limb flexion (synergy #2). Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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Other

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33 pages, 5356 KiB

Open AccessSystematic Review

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review

by Ezio Preatoni, Elena Bergamini, Silvia Fantozzi, Lucie I. Giraud, Amaranta S. Orejel Bustos, Giuseppe Vannozzi and Valentina Camomilla

Sensors 2022, 22(9), 3225; https://doi.org/10.3390/s22093225 - 22 Apr 2022

Cited by 14 | Viewed by 6560

Abstract

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features—such as research design, scope, experimental settings, and applied context—were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field. Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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7 pages, 488 KiB

Open AccessBrief Report

Comparison of Two Electronic Physical Performance Batteries by Measurement Time and Sarcopenia Classification

by Chan Mi Park, Hee-Won Jung, Il-Young Jang, Ji Yeon Baek, Seongjun Yoon, Hyunchul Roh and Eunju Lee

Sensors 2021, 21(15), 5147; https://doi.org/10.3390/s21155147 - 29 Jul 2021

Cited by 1 | Viewed by 2316

Abstract

The Short Physical Performance Battery (SPPB) is a widely accepted test for measuring lower extremity function in older adults. However, there are concerns regarding the examination time required to conduct a complete SPPB consisting of three components (walking speed, chair rise, and standing balance tests) in clinical settings. We aimed to assess specific examination times for each component of the electronic Short Physical Performance Battery (eSPPB) and compare the ability of the original three-component examinations (eSPPB) and a faster, two-component examination without a balance test (electronic Quick Physical Performance Battery, eQPPB) to classify sarcopenia. The study was a retrospective, cross-sectional study which included 124 ambulatory outpatients who underwent physical performance examination at a geriatric clinic of a tertiary, academic hospital in Seoul, Korea, between December 2020 and March 2021. For eSPPB, we used a toolkit containing sensors and software (Dyphi, Daejeon, Korea) developed to measure standing balance, walking speed, and chair rise test results. Component-specific time stamps were used to log the raw data. Duration of balance examination, 5 times sit-to-stand test (5XSST), and walking speed examination were calculated. Sarcopenia was determined using the 2019 Asian Working Group for Sarcopenia (AWGS) guideline. The median age was 78 years (interquartile range, IQR: 73,82) and 77 subjects (62.1%) were female. The total mean eSPPB test time was 124.8 ± 29.0 s (balance test time 61.8 ± 12.3 s, 49.5%; gait speed test time 34.3 ± 11.9 s, 27.5%; and 5XSST time 28.7 ± 19.1 s, 23.0%). The total mean eQPPB test time was 63.0 ± 25.4 s. Based on the AWGS criteria, 34 (27.4%) patient’s results were consistent with sarcopenia. C-statistics for classifying sarcopenia were 0.83 for eSPPB and 0.85 for eQPPB (p = 0.264), while eQPPB took 49.5% less measurement time compared with eSPPB. Breakdowns of eSPPB test times were identified. Omitting balance tests may reduce test time without significantly affecting the classifying ability of eSPPB for sarcopenia. Full article

(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)

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