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Advanced Sensing Technologies in Sports Biomechanics
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Advanced Sensing Technologies in Sports Biomechanics

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

Deadline for manuscript submissions: 30 July 2026 | Viewed by 462

Special Issue Editors

Prof. Dr. Alejandro Molina-Molina

E-Mail Website
Guest Editor
Faculty of Health Sciences, Universidad San Jorge, 50830 Zaragoza, Spain
Interests: biomechanics; human locomotion; sports performance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Enrique Roche-Seruendo

E-Mail Website
Guest Editor
Faculty of Health Sciences, Universidad San Jorge, 50830 Zaragoza, Spain
Interests: biomechanics; human locomotion; sports performance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Cartón-Llorente

E-Mail Website
Guest Editor
Faculty of Health Sciences, Universidad San Jorge, 50830 Zaragoza, Spain
Interests: biomechanics; human locomotion; sports performance
Special Issues, Collections and Topics in MDPI journals
Dr. Alberto Rubio-Peirotén

E-Mail Website
Guest Editor
Faculty of Health Sciences, Universidad San Jorge, 50830 Zaragoza, Spain
Interests: biomechanics; human locomotion; sports performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced sensing technologies are reshaping sports biomechanics by providing highly accurate, real-time measurements of human motion, mechanical load, and performance. This Special Issue focuses on how both wearable and non-wearable sensors enable deeper insights into movement patterns, technique optimization, injury prevention, and training monitoring.

This Special Issue welcomes studies that develop, validate, or apply innovative sensing systems, as well as approaches involving signal processing, artificial intelligence, machine learning, or biomechanical modeling that enhance the interpretation and practical deployment of sensor-based data. The goal is to gather strong scientific evidence and advanced methodologies that improve accuracy, accessibility, and applicability of sensing technologies in real sports settings.

The topic fits squarely within the scope of Sensors, as it integrates sensor development, implementation, and evaluation with a domain—sports biomechanics—where reliable measurement is essential for performance and health. Contributions from engineering, biomechanics, sports science, data science, and related fields are encouraged.

Prof. Dr. Alejandro Molina-Molina
Prof. Dr. Luis Enrique Roche-Seruendo
Prof. Dr. Antonio Cartón-Llorente
Dr. Alberto Rubio-Peirotén
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 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-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

  • sports
  • biomechanics
  • sensors
  • wearable technology
  • IMUs
  • motion capture
  • force platforms
  • computer vision
  • AI in sport
  • performance analysis

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Published Papers (2 papers)

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12 pages, 1167 KB  
Article
Estimation of Vertical Ground Reaction Forces During Vertical Jumping in Children Using OpenCap
by Jiongyi You, Zhicheng Lin and Baifa Zhang
Sensors 2026, 26(11), 3375; https://doi.org/10.3390/s26113375 - 26 May 2026
Abstract
Vertical ground reaction force is an important parameter for describing the developmental characteristics of young children’s vertical jumping. However, its application in large-scale physical fitness monitoring and routine teaching practice is greatly limited. Previous studies have used OpenCap to estimate vertical ground reaction [...] Read more.
Vertical ground reaction force is an important parameter for describing the developmental characteristics of young children’s vertical jumping. However, its application in large-scale physical fitness monitoring and routine teaching practice is greatly limited. Previous studies have used OpenCap to estimate vertical ground reaction force during adult jumping tasks and have provided preliminary validation, but its effectiveness in young children remains unclear. To examine the correlation and agreement of vertical ground reaction force (GRF) estimated by the OpenCap markerless motion capture system during young children’s vertical jumping and to explore the characteristics of vertical GRF estimated by OpenCap during the vertical jump. Kinematic and kinetic data during vertical jumping were synchronously collected from 16 young children using the OpenCap markerless motion capture system and a three-dimensional force platform, with each child completing three trials. Kinematic data were acquired using the OpenCap markerless motion capture system, and the vertical acceleration of the whole-body center of mass was calculated to estimate vertical GRF based on Newton’s second law. Pearson linear correlation analysis and Bland–Altman analysis were used to examine the differences in characteristics between the estimated vertical GRF and the measured vertical GRF. The vertical GRF characteristics estimated by OpenCap showed moderate-to-high correlations with the measured values. Specifically, the time and mean impulse during the push-off phase, flight phase, and landing stabilization phase were highly correlated (r > 0.85), while the peak force and mean force during the push-off phase showed moderate-to-high correlations (r > 0.7). Bland–Altman analysis showed that the bias in time and impulse during the vertical jump was less than 15%, indicating relatively high agreement; however, the bias in peak force during the landing phase exceeded 40%, indicating weak agreement. These results suggest that the OpenCap markerless motion capture system can effectively estimate vertical GRF characteristics during young children’s vertical jumping, with the best performance observed for vertical GRF variables in the push-off phase. The method used in this study may be applied to obtain vertical GRF during young children’s vertical jumping in non-laboratory settings and to assist in evaluating the developmental level of young children’s vertical jump performance. Nevertheless, OpenCap-derived rapid impact variables, particularly landing peak force, should be interpreted with caution. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Sports Biomechanics)
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8 pages, 868 KB  
Brief Report
The Validity of Stryd Leg Stiffness Against the Morin (2005) Sine-Wave Method: A Level-1 Assessment of Flat and Uphill Treadmill Running
by Diego Jaén-Carrillo and Antonio Cartón-Llorente
Sensors 2026, 26(10), 3244; https://doi.org/10.3390/s26103244 - 20 May 2026
Viewed by 203
Abstract
This study evaluated the validity of the leg stiffness metric provided by the Stryd running power meter against the Morin (2005) sine-wave spring–mass model. Twenty-three highly trained trail runners (11 women) completed a 12 min uphill time trial at +12% grade and one [...] Read more.
This study evaluated the validity of the leg stiffness metric provided by the Stryd running power meter against the Morin (2005) sine-wave spring–mass model. Twenty-three highly trained trail runners (11 women) completed a 12 min uphill time trial at +12% grade and one hour of submaximal level running. Leg stiffness was calculated from contact time, flight time, running speed, and leg length using Morin’s method, and compared with Stryd values. Agreement was assessed following the Dhahbi and Chamari Level-1 analytical framework, including intraclass correlation coefficient (ICC2,1), Bland–Altman analysis, mean absolute percentage error (MAPE), and paired t-tests. Stryd and Morin estimates showed excellent agreement in both conditions: uphill running: ICC2,1 = 0.96 (95%CI: 0.91–0.98), bias = −0.02 kN·m−1, limits of agreement (LoAs) = [−0.61, 0.58] kN·m−1, MAPE = 2.5% (p = 0.803); and level running: ICC2,1 = 0.97 (95%CI: 0.93–0.99), bias = −0.04 kN·m−1, LoAs = [−0.62, 0.54] kN·m−1, MAPE = 2.6% (p = 0.505). The Stryd sensor provides valid leg stiffness estimates in highly trained trail runners on both level and inclined terrain. The negligible systematic bias and narrow limits of agreement support the use of Stryd for leg stiffness monitoring in field and laboratory settings. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Sports Biomechanics)
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