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Biomechanics
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Biomechanics in Sport and Ageing: Artificial Intelligence
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Biomechanics in Sport and Ageing: Artificial Intelligence

A special issue of Biomechanics (ISSN 2673-7078). This special issue belongs to the section "Sports Biomechanics".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 2048

Special Issue Editors

Prof. Dr. Tibor Hortobágyi

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Guest Editor
1. Research Professor, Department of Kinesiology, Hungarian University of Sports Science, 1123 Budapest, Hungary
2. Research Professor, Institute of Sport Sciences and Physical Education, Faculty of Sciences, University of Pécs, 7624 Pécs, Hungary
3. Professor Emeritus of Movement and Healthy Ageing, Department of Human Movement Sciences, University Medical Center Groningen, 9700 Groningen, The Netherlands
Interests: gait; posture; balance; dynamic balance; static balance; biomechanics; muscle mechanics; neural control of posture; neural control of gait; exercise and other intervention effects on motor and cognitive function; resistance training; exergaming; power training; dance training; weight shift; dual tasking; magnetic brain stimulation; peripheral nerve stimulation; EEG; fMRI; eccentric muscle function; Parkinson’s disease; multiple sclerosis; stroke; interlimb transfer; cross education; motor brain plasticity; motor spinal plasticity
Dr. Melissa Boswell

E-Mail Website
Guest Editor
Joe Gibbs Human Performance Institute, Huntersville, NC 28078, USA
Interests: biomechanics; machine learning; sports performance; injury biomechanics; digital health
Prof. Dr. Ka-Chun (Joseph) Siu

E-Mail Website
Guest Editor
Department of Health and Rehabilitations Sciences, University of Nebraska Medical Center, Omaha, NE 68198-4420, USA
Interests: human movement; gait biomechanics; motor learning; rehabilitation; biomechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this special issue is to provide a scientific platform for a state-of-the-art update on the progress of artificial intelligence, AI, in sport biomechanics and ageing. AI relies on computers to execute commands that historically required human intelligence. As we can surmise it from Turing’s seminal paper, AI builds computational units that mimic human intelligence and abilities: language, communication, comprehension of concepts, automated thinking, (machine) learning, computer vision, and movements via robotics [1]. Supervised or unsupervised machine learning, i.e., the science of coding computers to learn and behave as humans do, as a subset of AI, allows us to discern patterns and structure in data. Deep learning optimizes supervised learning and trains models to learn how to map an input to an expected output [2]. These tools of AI are also becoming ubiquitous in sport biomechanics and ageing research. Sport and ageing might appear unrelated. However, sport and ageing are complementary: assessment and training methods developed in sport science are transformed to improve diagnosis and treatment of ageing-related impairments. Along the spectrum from low to high levels of physical and cognitive function, the current special issue highlights how AI is leveraged to assess and increase top performance, predict motor and cognitive function, and ultimately deliver improved care for all individuals across the lifespan, including athletes and seniors [3].

References

  1. Turing, A.M. On computable numbers, with an application to the Entscheidungsproblem. Proc. London Math Soc. 1936, 58, 230–65.
  2. LeCun, et al. Deep learning. Nature 2015, 521, 436–44.
  3. Zhang, et al. Leveraging physiology and artificial intelligence to deliver advancements in health care. Physiol. Rev. 2023, 103, 2423–50.

Prof. Dr. Tibor Hortobagyi
Dr. Melissa Boswell
Prof. Dr. Ka-Chun (Joseph) Siu
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. Biomechanics is an international peer-reviewed open access quarterly 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 1000 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

  • artificial intelligence
  • machine learning
  • deep learning
  • neural networks
  • biomechanics
  • wearables
  • sport
  • kinesiology
  • ageing

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Published Papers (2 papers)

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15 pages, 1683 KiB  
Article
The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syndrome
by Taylor Miners, Jeremy Witchalls, Jaquelin A. Bousie, Ceridwen R. Radcliffe and Phillip Newman
Biomechanics 2025, 5(2), 22; https://doi.org/10.3390/biomechanics5020022 - 2 Apr 2025
Viewed by 415
Abstract
Background/Objectives: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common methods of estimating tibial [...] Read more.
Background/Objectives: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common methods of estimating tibial loads, yet clinical recommendations for technique modification to reduce these metrics are not well documented. This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. Methods: Sixteen runners completed seven 1 min trials consisting of preferred technique, ±10% speed, ±10% cadence, forefoot, and rearfoot strike. Results: A 10% speed reduction decreased peak tibial acceleration (PTA), vertical average loading rate (VALR), vertical instantaneous loading rate (VILR), and vertical impulse by 13%, 10.9%, 9.3%, and 3.2%, respectively. A 10% cadence increase significantly reduced PTA (11.5%), VALR (15.6%), VILR (13.5%), and impulse (3.5%). Forefoot striking produced significantly lower PTA (26.6%), VALR (68.3%), and VILR (68.9%). Habitual forefoot strikers had lower VALR (58.1%) and VILR (47.6%) compared to rearfoot strikers. Machine-learning models predicted all four vGRF metrics with mean average errors of 9.5%, 10%, 10.9%, and 3.4%, respectively. Conclusions: This study demonstrates that small-scale modifications to running technique effectively reduce tibial load estimates. Machine-learning models offer an accessible, affordable tool for gait retraining by predicting vGRF metrics without reliance on IMU data. The findings support practical strategies for reducing MTSS risk. Full article
(This article belongs to the Special Issue Biomechanics in Sport and Ageing: Artificial Intelligence)
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10 pages, 1707 KiB  
Technical Note
A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research
by Daichi Yamashita, Minoru Matsumoto and Takeo Matsubayashi
Biomechanics 2025, 5(2), 25; https://doi.org/10.3390/biomechanics5020025 - 13 Apr 2025
Viewed by 303
Abstract
Background: This technical note proposes a deep learning-based, few-shot automatic key point tracking technique tailored to sports biomechanics research. Methods: The present method facilitates the arbitrary definition of key points on athletes’ bodies or sports equipment. Initially, a limited number of video frames [...] Read more.
Background: This technical note proposes a deep learning-based, few-shot automatic key point tracking technique tailored to sports biomechanics research. Methods: The present method facilitates the arbitrary definition of key points on athletes’ bodies or sports equipment. Initially, a limited number of video frames are manually digitized to mark the points of interest. These annotated frames are subsequently used to train a deep learning model that leverages a pre-trained VGG16 network as its backbone and incorporates an additional convolutional head. Feature maps extracted from three intermediate layers of VGG16 are processed by the head network to generate a probability map, highlighting the most likely locations of the key points. Transfer learning is implemented by freezing the backbone weights and training only the head network. By restricting the training data generation to regions surrounding the manually annotated points and training specifically for each video, this approach minimizes training time while maintaining high precision. Conclusions: This technique substantially reduces the time and effort required compared to frame-by-frame manual digitization in various sports settings, and enables customized training tailored to specific analytical needs and video environments. Full article
(This article belongs to the Special Issue Biomechanics in Sport and Ageing: Artificial Intelligence)
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