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Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation

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A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Wearables".

Deadline for manuscript submissions: 1 March 2027 | Viewed by 4152

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Editors

Prof. Dr. Peter Dabnichki

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Guest Editor

School of Engineering, RMIT University-Bundoora Campus, Bundoora, Melbourne, VIC 3001, Australia
Interests: intelligent systems; wearable sensors; motion analysis; performance analysis; pervasive computing; sport biomechanics
Special Issues, Collections and Topics in MDPI journals

Dr. Dominik Hoelbling

E-Mail Website
Guest Editor

1. Research Group for Industrial Software (INSO), TU Wien, Vienna, Austria
2. RISE Institute of Technology (RIT), SSS District, Ongole, India
3. Software and Digital Systems Engineering, University of Applied Sciences Wiener Neustadt (FHWN), Biotech Campus Tulln, Konrad Lorenz Straße 10, 3430 Tulln, Austria
Interests: muscle physiology; wearable sensors; sport biomechanics; smart sports; rehabilitation

Special Issue Information

Dear Colleagues,

This Special Issue, “Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation”, focuses on innovative applications of sensing technologies in software, hardware, and mechanical systems designed to analyse, support, or enhance human movement. Rather than emphasising sensor development alone, this Special Issue highlights how sensor data are integrated into advanced digital tools, engineered devices, and biomechanical systems for use in sports performance, rehabilitation, and human–machine interaction.

We welcome contributions that leverage sensor-based information for computational modelling, machine learning, motion analysis software, digital twins, real-time feedback systems, and serious-game or VR-based rehabilitation platforms. Topics include the use of wearable and vision-based sensors, smart textiles, pressure and force measurement, and multi-sensor environments to drive new sport technologies, rehabilitation devices, assistive systems, or mechanical engineering solutions.

Research addressing musculoskeletal modelling, gait and posture analysis, joint kinematics, muscle dynamics, or neuromechanical control—particularly when these analyses feed into system design, device optimisation, or personalised performance assessment—is strongly encouraged. Studies on rehabilitation robotics, prosthetic and orthotic devices, sensor-integrated mechanical structures, exoskeletons, and performance-optimised sports equipment also fall within the scope.

While applications are central, contributions on novel sensing modalities or improvements in sensor integration, calibration, data processing, and robustness are equally welcome when they enable enhanced functionality in sports or rehabilitation contexts.

This Special Issue encourages interdisciplinary work bridging biomechanics, mechanical and biomedical engineering, data science, robotics, and human–computer interaction. By focusing on sensor-driven software and engineering solutions, the Special Issue aims to promote innovations that improve motion analysis accuracy, enable real-time decision support, enhance clinical and sports outcomes, and advance the state of the art in human movement technology.

Prof. Dr. Peter Dabnichki
Dr. Dominik Hoelbling
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-anonymized 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

integrated sensors and digital devices
computational modelling
machine learning
real-time feedback systems
musculoskeletal modelling
gait and posture analysis
biomechanics
biomedical engineering
new sport technologies
rehabilitation devices
serious-game or VR-based rehabilitation platforms
smart textiles
pressure and force measurement
rehabilitation robotics
assistive systems

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Published Papers (5 papers)

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Research

20 pages, 4190 KB

Open AccessArticle

Motor Imagery Acquisition and Classification Using a Low-Cost 8-Channel EEG System in a VR ADHD Serious Game Environment: A Case Study

by Lukas Röhrling, Selina Breuer, Carina Arnberger, Christoph Aigner, Thomas Grechenig and René Baranyi

Sensors 2026, 26(17), 5576; https://doi.org/10.3390/s26175576 - 2 Sep 2026

Viewed by 361

Abstract

Attention-deficit/hyperactivity disorder (ADHD) involves difficulties in sustaining attention and resisting distraction. This has motivated the development of feedback-driven environments for cognitive control training. Integrating electroencephalography (EEG) sensors into Virtual Reality (VR) serious games for cognitive therapy remains relatively underexplored and requires reliable, non-invasive brain–computer interfaces. The existing solutions use multi-channel systems that primarily suffer from requiring complex hardware, while not combining motor imagery (MI) with concentration levels. Therefore, this case study evaluates the feasibility and data quality of a lightweight, cost-effective sensor configuration for real-time control of mental state. A non-invasive, eight-channel OpenBCI Cyton board was integrated with an EEG cap using the international 10–20 placement system, alongside a Meta Quest 2 headset, to capture MI and concentration signals directly from the user’s scalp. Signal acquisition was hindered by high impedance and channel railing, which required conductive gel mitigation, while mechanical tension from the VR headset strap introduced motion artifacts and noise. Nevertheless, under stable signal conditions, the optimized eight-channel sensor setup achieved a subject-specific online classification accuracy of up to 90% using the deep learning model “EEGNet”. The findings demonstrate the technical feasibility of acquiring and classifying EEG activity using a low-cost eight-channel sensor configuration in an interactive VR-BCI Serious Gaming application, provided that skin–electrode impedance and mechanical sensor interferences are managed. The results provide a basis for future investigation of such systems in cognitive-training applications, while further studies, including clinical evaluations, are required to assess their applicability in therapeutic contexts. Full article

(This article belongs to the Special Issue Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation)

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21 pages, 3812 KB

Open AccessArticle

The Effect of Non-Invasive Brain Stimulation on Running Performance and Inertial Measurement Unit-Derived Spatiotemporal Parameters in Endurance-Trained Runners

by Isabella Sierra, Yiyang Chen, Gleydciane Alexandre Fernandes, Henri Lajeunesse, Julien Clouette, Alexandra Potvin-Desrochers, Jenna C. Gibbs, Julie N. Côté, Fabien A. Basset and Caroline Paquette

Sensors 2026, 26(17), 5390; https://doi.org/10.3390/s26175390 - 26 Aug 2026

Viewed by 378

Abstract

Integrating wearable motion sensing with neuromodulation may improve understanding of how alterations in neural excitability influence running performance and biomechanics. This study investigated whether intermittent theta burst stimulation (iTBS) applied to the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), or both regions influences running performance and sensor-derived spatiotemporal parameters during a 3000 m time-trial run. Ten endurance-trained runners (7 males) completed four stimulation conditions (M1, DLPFC, M1 + DLPFC, and sham) in a randomized, sham-controlled, repeated-measures crossover design. Running performance and spatiotemporal gait parameters were continuously monitored using wearable inertial measurement units (IMUs), with analyses conducted across the initial, steady-state, and final acceleration phases of the run. The M1 + DLPFC condition resulted in the fastest mean completion time, averaging approximately three seconds faster than sham. However, these differences were not statistically significant. Sensor-derived biomechanical measures revealed significantly higher running speeds and alterations in stride time and step frequency during the initial phase following combined stimulation compared with the other conditions. Ratings of perceived exertion and spatiotemporal variability did not differ between stimulation conditions. These findings demonstrate the utility of wearable IMUs for detecting subtle phase-specific changes in running biomechanics and suggest that combined stimulation of motor and cognitive control regions may influence early-stage running performance, warranting further investigation in larger cohorts. As the complete sample consisted of only ten runners, these findings are preliminary and require confirmation in a larger sample size. Full article

(This article belongs to the Special Issue Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation)

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31 pages, 6499 KB

Open AccessArticle

A Frequency-Aware Dual-Stream Deep Learning Framework for Athlete Workload Monitoring and Injury Risk Assessment: A Multi-Dataset Validation Study in Professional Team Sports

by Jinnian Tong and Peng Gao

Sensors 2026, 26(13), 4228; https://doi.org/10.3390/s26134228 - 3 Jul 2026

Viewed by 822

Abstract

The accumulation of training and competition loads represents a critical determinant of musculoskeletal injury risk in professional team sports, yet contemporary monitoring systems remain limited by their reliance on single-domain temporal analysis that overlooks the multi-scale rhythmic patterns inherent in athletic workload signals. This study introduces FDTM (frequency-aware dual-stream temporal model), a deep learning framework that jointly encodes time-domain dependencies and frequency-domain spectral signatures from digital athlete monitoring streams to predict individual injury risk over a forward-looking seven-game horizon. The framework integrates a stacked bidirectional long short-term memory branch augmented with temporal self-attention pooling, a spectral encoding branch employing discrete Fourier transform decomposition across high-frequency (weekly), mid-frequency (bi-weekly), and low-frequency (seasonal) bands, and a cross-modal gated attention fusion module that adaptively balances temporal and spectral representations conditioned on player context. We evaluate FDTM on three heterogeneous public sports datasets spanning basketball (NBA game-log corpus 2013–2023), Australian rules football (AFL Player Workload Dataset), and soccer (SoccerMon open monitoring corpus), comprising 612 athletes and 247,830 player-game observations across ten competitive seasons. FDTM achieves AUC-ROC values of 0.858, 0.833, and 0.821 on the three datasets respectively, outperforming the strongest deep-learning baseline (FEDformer) by 2.0 to 3.3 percentage points and the strongest non-spectral baseline (TCN) by 3.2 to 4.5 percentage points while maintaining a Brier score below 0.04. Ablation studies confirm that the spectral branch contributes 5.1 percent to overall discriminative performance. SHAP attribution analyses identify high-frequency weekly components as the dominant injury-relevant signal, followed by low-frequency seasonal trends and the cumulative acute-to-chronic workload temporal feature, with gating-weight visualizations revealing dynamic modality contributions consistent with established sports science theory. Direct spectral analysis of the raw workload signal confirms that injury-preceding windows exhibit significantly elevated weekly-band power across all three datasets (Mann–Whitney U test, p < 1 × 10⁻⁷), and the architectural advantage is shown to be robust across 30 independent training seeds. These findings suggest that frequency-aware modeling may serve as a transferable methodology for sports engineering applications in injury prevention, return-to-play planning, and individualized rehabilitation, pending further external validation in female athletes and additional team sports. Full article

(This article belongs to the Special Issue Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation)

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13 pages, 502 KB

Open AccessArticle

Test–Retest Reliability of Heart Rate and Parasympathetic Modulation Indices Across Exercise and Recovery Phases in Athletes

by Süleyman Ulupınar, Serhat Özbay, Cebrail Gençoğlu, İzzet İnce, Salih Çabuk, Özgür Bakar, Abdullah Demirli and Kaan Kaya

Sensors 2026, 26(8), 2448; https://doi.org/10.3390/s26082448 - 16 Apr 2026

Viewed by 896

Abstract

This study examined the within-session (same-day) test–retest reliability of heart rate (HR) and parasympathetic modulation, assessed using the root mean square of successive differences (RMSSD), across exercise and recovery phases in trained soccer players. Twenty-seven male soccer players (age: 24.9 ± 3.7 years) completed a standardized soccer training session. HR and RMSSD were recorded using an ECG-based chest-strap monitor at rest, pre-exercise, and at ~10–20 min, 1 h, and 3 h post-exercise. At each time point, two consecutive 5 min seated recordings were obtained under identical conditions. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC_(3,1)), standard error of measurement (SEM), coefficient of variation (CV%), minimal detectable change (MDC₉₅), paired-samples t-tests, and Hedges’ g effect sizes. HR demonstrated excellent reliability across all time points (ICC = 0.980–0.994; SEM = 0.87–1.25 bpm; CV% = 1.33–3.70%). RMSSD showed excellent reliability at rest (ICC = 0.944) and pre-exercise (ICC = 0.918), moderate reliability during early recovery (~10–20 min; ICC = 0.551), and good reliability at 1 h (ICC = 0.826) and 3 h post-exercise (ICC = 0.873). No significant systematic differences were observed between test and retest measurements (all p > 0.05), and effect sizes were trivial. These findings indicate that within-session reliability of HR remains consistently high across exercise and recovery phases, whereas RMSSD reliability varies according to measurement timing, particularly during early recovery. Full article

(This article belongs to the Special Issue Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation)

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18 pages, 2383 KB

Open AccessArticle

Position-Independent Lactate Kinetic Phenotypes in Professional Soccer Players: A Machine Learning Approach for Maximal Running Velocity Prediction

by Erkan Tortu, İzzet İnce, Salih Çabuk, Süleyman Ulupınar, Cebrail Gençoğlu, Serhat Özbay and Kaan Kaya

Sensors 2026, 26(7), 2252; https://doi.org/10.3390/s26072252 - 6 Apr 2026

Viewed by 1032

Abstract

This study aimed to identify distinct lactate kinetic phenotypes in professional soccer players using unsupervised machine learning and determine their relationship with maximal running velocity (V_max) through explainable artificial intelligence methods. A total of 361 professional male soccer players from the First Division participated in the study. Incremental treadmill tests measured lactate concentrations at five standardized velocities, alongside VO_2max, V_max, lactate threshold (LT), and anaerobic threshold (AT) parameters. Three distinct lactate kinetic phenotypes emerged: Economical Aerobic (n = 216), Balanced Metabolic (n = 19), and High Producer (n = 126). The Economical Aerobic phenotype demonstrated superior performance metrics compared to High Producer (V_max: 15.85 ± 0.85 km/h; VO_2max: 56.20 ± 4.26 mL/kg/min; p < 0.001). Initial multicollinearity assessment revealed notable collinearity among all 10 candidate predictors (VIF > 10; maximum VIF = 10.75 for V_AT), necessitating rigorous feature selection. Ridge regression with 4 selected features (V_AT, VO_2max, 9.5 km/h lactate, 14 km/h lactate) achieved moderate but statistically significant predictive performance: 10-fold cross-validation R²= 0.392 ± 0.147 (permutation test p = 0.001). Standardized coefficients identified V_AT (β = 0.399) as the dominant predictor, followed by VO_2max (β = 0.253), 9.5 km/h lactate (β = 0.107), and 14 km/h lactate (β = −0.066). Lactate kinetic phenotyping reveals position-independent metabolic profiles with potentially meaningful performance associations in professional soccer. The Economical Aerobic phenotype demonstrates performance advantages associated with superior anaerobic threshold capacity. These exploratory findings suggest that individualized training strategies based on metabolic phenotype rather than playing position alone warrant further investigation, with potential applications for talent identification, training periodization, and return-to-play protocols pending prospective validation. Full article

(This article belongs to the Special Issue Sensors in Digital and Biomechanical Engineering Sciences for Sports and Rehabilitation)

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