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Bioengineering
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Biomechanics of Human Motion
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Biomechanics of Human Motion

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomechanics and Sports Medicine".

Deadline for manuscript submissions: 20 January 2027 | Viewed by 1747

Editors

Dr. Meizi Wang

E-Mail Website
Guest Editor
Department of Biomedical Engineering (BME), Hong Kong Polytechnic University, Hong Kong, China
Interests: biomechanics of musculoskeletal system; human movement performance
Dr. Xiaoling Hu

E-Mail Website
Guest Editor
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: neural engineering; multi-module robotic system; biomechatronic engineering; biosignal processing; stroke rehabilitation; sports medicine; wearable technology; quantitative measurement for diagnosis and evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding the Biomechanics of Human Motion is crucial for advancing numerous fields, including sports science, rehabilitation, robotics, and ergonomics. This Special Issue aims to bring together innovative research that addresses the complexities of human movement, focusing on musculoskeletal dynamics, movement analysis, and computational modeling. By exploring the underlying principles and applications of human motion, we aim to improve athletic performance, enhance injury prevention strategies, and support the development of assistive technologies such as prosthetics and exoskeletons.

Contributions to this Special Issue will cover a wide range of topics, including biomechanical modeling, motion capture methods, computational simulations, and the application of these techniques in clinical and sports environments. Research on gait analysis, musculoskeletal simulations, and wearable biomechanics sensors will also be highlighted. The goal of this Special Issue is to foster interdisciplinary collaboration and showcase the latest advancements in the Biomechanics of Human Motion.

We invite researchers, clinicians, and engineers from biomechanics, biomedical engineering, sports medicine, and related fields to contribute to this Special Issue. Through this collection, we hope to highlight emerging trends and provide a comprehensive overview of state-of-the-art research in human motion biomechanics.

Dr. Meizi Wang
Dr. Xiaoling Hu
Guest Editors

Dr. Lucia Donno
Guest Editor Assistant
Email: lucia.donno@polimi.it
Homepage: https://www.deib.polimi.it/eng/people/details/1061622
Research interest: bioengineering; movement analysis; clinical biomechanics; rehabilitation; sport biomechanics; musculoskeletal modeling

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • human movement analysis
  • gait analysis
  • sport performance
  • musculoskeletal biomechanics
  • injury prevention
  • wearable biomechanics sensors
  • motion capture techniques

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Published Papers (2 papers)

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Research

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19 pages, 2652 KB  
Article
LLM-Assisted Interpretation of Kinematic Gait Data in Children with Cerebral Palsy: A Pilot Study on Gait Deviation Detection and Surgical Group Recommendations
by Mehrdad Davoudi, Jacqueline Romkes, Michèle Widmer, Chris Easthope Awai and Elke Viehweger
Bioengineering 2026, 13(8), 862; https://doi.org/10.3390/bioengineering13080862 - 25 Jul 2026
Viewed by 387
Abstract
Three-dimensional instrumented gait analysis is widely used to guide surgical decision-making in children with cerebral palsy (CP), but its interpretation is time-consuming and prone to inter-rater variability. In this single-centre pilot study, we investigated whether a generative large language model (LLM) could consistently [...] Read more.
Three-dimensional instrumented gait analysis is widely used to guide surgical decision-making in children with cerebral palsy (CP), but its interpretation is time-consuming and prone to inter-rater variability. In this single-centre pilot study, we investigated whether a generative large language model (LLM) could consistently generate gait deviation findings and surgical procedure suggestions that align with expert judgement. Kinematic features for lower-limb joints across the gait cycle, stance, and swing were extracted from eight children with unilateral CP using the open-source GaitSharing Toolkit and a structured prompt, then submitted three times per patient to OpenAI’s GPT-5.5 model. The model assessed 28 kinematic deviations and 12 surgical procedure groups using majority voting. One gait analyst and two paediatric orthopaedic surgeons independently rated outputs on a 0–2 ordinal scale, blinded to all clinical information beyond the kinematic curves and diagnosis. Agreement was summarised descriptively as the percentage of the maximum attainable score with 95% confidence intervals (CIs), and quadratic-weighted Cohen’s kappa was used to quantify inter-surgeon agreement. Agreement with the gait expert was highest at the hip (90.6%) and lowest at the knee, particularly in the transverse plane (65.2%). For surgical procedures, agreement with the LLM reached 83.9% and 73.4% for the two surgeons, with the tibialis anterior procedure showing the lowest concordance. Inter-surgeon agreement was 79.2% (95% CI 71.9–85.4) with a kappa of 0.59 (0.47–0.70), indicating moderate agreement. The LLM showed high self-consistency (>90% across runs). These preliminary findings suggest that generative LLMs may be feasible as assistive tools in clinical gait analysis for deviation detection and future treatment planning and should be interpreted as hypothesis-generating, warranting confirmation in larger, more diverse cohorts. Full article
(This article belongs to the Special Issue Biomechanics of Human Motion)
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Review

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24 pages, 4382 KB  
Review
Applications of Wearable Sweat Biosensors in Sports Activities with Real-World Cases
by Jiaxiang Yan, Jingyu Shi, Zhuoer Zhang, Mo Yang, Ming Zhang and Meizi Wang
Bioengineering 2026, 13(8), 906; https://doi.org/10.3390/bioengineering13080906 - 11 Aug 2026
Viewed by 333
Abstract
Sports teams are constantly seeking advanced technologies to gain a competitive edge by enhancing athletic performance, accelerating recovery, and minimizing injury risk. Sweat serves as a readily accessible biofluid which enables non-invasive measurement of key biochemical analytes and physiological parameters during sports activities, [...] Read more.
Sports teams are constantly seeking advanced technologies to gain a competitive edge by enhancing athletic performance, accelerating recovery, and minimizing injury risk. Sweat serves as a readily accessible biofluid which enables non-invasive measurement of key biochemical analytes and physiological parameters during sports activities, providing valuable insights into an athlete’s fatigue status, hydration levels, energy expenditure, and muscle function. With ongoing advancements in wearable sweat sensors, particularly those powered by artificial intelligence (AI), these devices facilitate dynamic adjustments to training intensity, optimized fatigue management, personalized recovery strategies, and overall performance enhancement, which are especially valuable for athletes and sports teams. In this paper, we present a structured narrative overview of sweat-based wearable biosensor technologies, highlighting target analytes of interest, advanced materials, innovative designs, AI integration for data interpretation, and applications of commercial products in sports scenarios. Furthermore, we highlight challenges like sweat composition variability, calibration issues, and limitations in sensor materials. We also explore opportunities for future development, such as enhancing sensor performance with hybrid materials, expanding multimodal biosensors, and refining AI systems for personalized performance optimization. Full article
(This article belongs to the Special Issue Biomechanics of Human Motion)
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