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Recent Advances in Biomechanics of Human Movement and Its Clinical Applications: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2925

Special Issue Editors

Dr. Weijie Wang

E-Mail Website
Guest Editor
Department of Orthopedic and Trauma Surgery, University of Dundee, Dundee DD1 9SY, UK
Interests: biomechanics; motion medicine; musculoskeletal modelling; computer simulation; gait analysis; electromyograph; artificial intelligence; clinical application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dong Ming

E-Mail Website
Guest Editor
Medical College, Tianjin University, Tianjin 300072, China
Interests: neural engineering; rehabilitation engineering; biomedical instrumentation, and signal/image processing; brain–computer interface; functional electrical stimulation; gait analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of motion capture technology, computer modeling, and bioinformation, biomechanics has achieved both academic and theoretical milestones, as well as having been involved in the advancement of useful tools for clinical assessment and treatment. However, these results are far from being applicable to clinical practice. This welcomes new challenges for researchers: (1) how to build a bridge between theoretical achievements and practical applications, (2) how to develop stronger means for clinical practice, (3) what could be suitable for clinical practice, etc. Therefore, this Special Issue aims to present innovative ideas and experimental results in the field of biomechanics alongside their applications to clinical practice; in other words, it will bring ideas, tools, and theory into practical use.

Areas relevant to this Special Issue include, but are not limited to, theoretical analyses of biomechanics with clinical cases, data-intensive applications, novel algorithms and applications, computational science, artificial intelligence, machine learning, deep learning, wearable sensor application, medical instruments, and other interesting and useful topics.

This Special Issue will publish high-quality, original research papers emphasizing clinical application in the following areas:

  • Biomechanics;
  • Motion medicine;
  • Artificial intelligence, machine learning, and deep learning;
  • Computational modeling;
  • Instrumentation;
  • Motion pattern recognition;
  • Bio-force measurement and analysis;
  • Bio-signal measurement and analysis;
  • Bio-image measurement and analysis.

Dr. Weijie Wang
Prof. Dr. Dong Ming
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. Applied Sciences 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 2400 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

  • gait
  • artificial intelligence
  • machine learning and deep learning
  • electromyograph (EMG)
  • electroencephalogram (EEG)
  • strength
  • rehabilitation
  • physiotherapy
  • stroke
  • cerebral palsy
  • motion capture
  • sensor
  • joint and bone
  • muscle
  • artificial implant
  • algorithms

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Related Special Issue

Published Papers (4 papers)

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Research

33 pages, 8517 KiB  
Article
Approximate and Sample Entropy of the Center of Pressure During Unperturbed Tandem Standing: Effect of Altering the Tolerance Window
by Jayla Wesley, Samhita Rhodes, David W. Zeitler and Gordon Alderink
Appl. Sci. 2025, 15(2), 576; https://doi.org/10.3390/app15020576 - 9 Jan 2025
Viewed by 295
Abstract
Approximate entropy (ApEn) and sample entropy (SampEn) are statistical indices designed to quantify the regularity or predictability of time-series data. Although ApEn has been a prominent choice in analyzing non-linear data, it is currently unclear which method and parameter selection combination is optimal [...] Read more.
Approximate entropy (ApEn) and sample entropy (SampEn) are statistical indices designed to quantify the regularity or predictability of time-series data. Although ApEn has been a prominent choice in analyzing non-linear data, it is currently unclear which method and parameter selection combination is optimal for its application in biomechanics. This research aimed to examine the differences between ApEn and SampEn related to center-of-pressure (COP) data during tandem standing balance tasks, while also changing the tolerance window, r. Six participants completed five, 30 s trials, feet-together and tandem standing with eyes open and eyes closed. COP data (fs = 60 Hz, downsampled from 1200 Hz) from ground reaction force platforms were collected. ApEn and SampEn were calculated using a constant vector length, i.e., m = 2, but differing values of r (tolerance window). For each of the participants, four separate one-way analysis of variance analyses (ANOVA) were conducted for ApEn and SampEn along the anterior–posterior (AP) and medial–lateral (ML) axes. Dunnett’s intervals were applied to the one-way ANOVA analyses to determine which tandem conditions differed significantly from the baseline condition. ApEn and SampEn provided comparable results in the predictability of patterns for different stability conditions, with increasing instability, i.e., tandem eyes closed postures, being associated with greater unpredictability. The selection of r had a relatively consistent effect on mean ApEn and SampEn values across r = 0.15 × SD to r = 0.25 × SD, where both entropy methods tended to decrease as r increased. Mean SampEn values were generally lower than ApEn values. The results suggest that both ApEn and SampEn indices demonstrated relative consistency and were equally effective in quantifying the level of the center-of-pressure signal regularity during quiet tandem standing postural balance tests. Full article
(This article belongs to the Special Issue Recent Advances in Biomechanics of Human Movement and Its Clinical Applications: 2nd Edition)
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15 pages, 4637 KiB  
Article
The Reverse Lunge: A Descriptive Electromyographic Study
by Barbara J. Hoogenboom, Madison Ferguson, Zac Krauss and Stephanie Tran
Appl. Sci. 2024, 14(24), 11480; https://doi.org/10.3390/app142411480 - 10 Dec 2024
Viewed by 519
Abstract
Limited studies exist examining the reverse lunge. The purpose of this study was to describe the activation of the rectus femoris (RF), biceps femoris (BF), gluteus medius (GMed), and gluteus maximus (GMax) of both limbs during a bodyweight reverse lunge movement. A secondary [...] Read more.
Limited studies exist examining the reverse lunge. The purpose of this study was to describe the activation of the rectus femoris (RF), biceps femoris (BF), gluteus medius (GMed), and gluteus maximus (GMax) of both limbs during a bodyweight reverse lunge movement. A secondary purpose was to describe the phases of the stationary (non-moving) and lead (moving) limbs during the reverse lunge. Surface electromyography (EMG) was used to record the activity of the target muscles in 20 healthy adults (10 male, 10 female; aged 22–25). Root mean squared values for mean maximum and average percent activation normalized to maximum voluntary isometric contraction (MVIC) activation were calculated. Descriptive terminology was created to describe the phases of the lunge for both limbs. The mean maximum percentage of muscle activation for the RF and BF was greater in the lead limb, while GMed and GMax activations were greater in the stationary limb. Only the lead limb RF and stationary limb GMed reached a strengthening stimulus in mean maximum percentage measurements. Clinically, it may be important to consider when each muscle is maximally active and at what percentage of its MVIC to properly prescribe the reverse lunge in a safe manner. Full article
(This article belongs to the Special Issue Recent Advances in Biomechanics of Human Movement and Its Clinical Applications: 2nd Edition)
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16 pages, 3319 KiB  
Article
Voluntary Muscle Contraction Pattern in Cerebral Palsy by Reducing Guidance Force in Robot-Assisted Gait Training: A Proof of Concept Focused on a Single-Participant Study
by Suncheol Kwon, Sora Park, Ji Hye Jung and Hyun Kyung Kim
Appl. Sci. 2024, 14(23), 11119; https://doi.org/10.3390/app142311119 - 28 Nov 2024
Viewed by 500
Abstract
This study aimed to investigate if voluntary participation in robot-assisted gait training leads to more concentrated muscle activity patterns and clinical measure improvements. A single-participant research design study was conducted with a gradual reduction in robotic assistance during robot-assisted gait training. A child [...] Read more.
This study aimed to investigate if voluntary participation in robot-assisted gait training leads to more concentrated muscle activity patterns and clinical measure improvements. A single-participant research design study was conducted with a gradual reduction in robotic assistance during robot-assisted gait training. A child with cerebral palsy participated in 20 robot-assisted gait training sessions and two assessment sessions across 99 days. The assistive force of the Lokomat gradually reduced during repeated training. The effects of reduced assistive force on muscle activity patterns were quantitatively analyzed using a clustering algorithm and electromyography. Improvements in overall gait quality and muscle strength were measured after robot-assisted gait training. The results also showed that the number of clustered representative patterns doubled and muscle activation patterns increased when the assistance decreased by 20%, whereas full robot assistance might have hindered active participation. Since assistive force modulation can be a key in robotic rehabilitation, the proposed protocol, involving gradual assistive force reduction, demonstrates promising efficacy and allows for in-depth analysis. Therefore, further randomized clinical trials based on this study can be possible for children with cerebral palsy. Full article
(This article belongs to the Special Issue Recent Advances in Biomechanics of Human Movement and Its Clinical Applications: 2nd Edition)
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14 pages, 1458 KiB  
Article
Data-Driven Stroke Classification Utilizing Electromyographic Muscle Features and Machine Learning Techniques
by Jaehyuk Lee, Youngjun Kim and Eunchan Kim
Appl. Sci. 2024, 14(18), 8430; https://doi.org/10.3390/app14188430 - 19 Sep 2024
Cited by 1 | Viewed by 894
Abstract
Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application [...] Read more.
Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models. Methods: EMG data were collected from the legs of 120 healthy individuals and 120 stroke patients during gait. Four statistical features were extracted from 16 EMG signals and trained on seven ML-based models. The accuracy of the validation and test datasets was also examined. Results: The model with the best performance was Random Forest. Among the 16 EMG signals, the average and maximum values of the muscle activities involved in knee extension (i.e., vastus medialis and rectus femoris) contributed significantly to the predictions. Conclusion: The results of this study confirmed that the simple processing and feature extraction of EMG signals effectively contributed to the accuracy of ML-based models. Routine use of EMG data collected in clinical environments is expected to provide benefits in terms of stroke prevention and rehabilitation. Full article
(This article belongs to the Special Issue Recent Advances in Biomechanics of Human Movement and Its Clinical Applications: 2nd Edition)
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