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Advancements and Future Prospects of Sensing Technologies for Health Monitoring and Sports Performance

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1489

Special Issue Editors


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Guest Editor
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China
Interests: functional garments for scoliosis; new materials and technology; surface treatments on textiles; moulding or seamless techniques; intimate apparel; activewear
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China
Interests: smart textiles; fiber Bragg grating (FBG) sensors; health monitoring; scoliosis treatment

Special Issue Information

Dear Colleagues,

Sensing technologies are undergoing transformative advancements, revolutionizing approaches to both personalized health monitoring and athletic performance optimization. This progress delivers unprecedented capabilities for capturing intricate physiological and biomechanical data continuously and non-invasively.

Modern wearable biosensors—integrated into smartwatches, fitness bands, and smart textiles—enable the continuous tracking of vital parameters (e.g., heart rate, SpO2, temperature, motion). These devices leverage wireless connectivity and cloud-based analytics to facilitate real-time data sharing, enabling personalized feedback from healthcare providers and coaches. Recent innovations include flexible/stretchable electronics for enhanced comfort and accuracy, alongside novel non-invasive modalities like sweat biomarker analysis and advanced photoplethysmography. Crucially, Artificial Intelligence (AI) and Machine Learning (ML) algorithms are now essential for interpreting complex, multi-parametric datasets, driving early anomaly detection in health contexts and optimizing training load/technique for athletes. Integration with mobile health platforms and telemedicine significantly broadens accessibility and user engagement.

The Special Issue ‘Advancements and Future Prospects of Sensing Technologies for Health Monitoring and Sports Performance’ directly addresses the core scope of Sensors by focusing on cutting-edge developments across multiple sensor domains critical to these fields.

This topic inherently involves wearable sensors and biomedical sensors (e.g., integrated into smart textiles and devices), biosensors (for physiological markers), and chemical sensors (e.g., for sweat biomarker analysis). It explores non-invasive modalities like photoplethysmography and lab-on-a-chip concepts, aligning with the journal's emphasis on novel sensing principles and applications. The critical role of signal processing and data fusion is highlighted through the use of AI/ML algorithms for interpreting complex, multi-parametric datasets from sensor arrays, enabling anomaly detection and performance optimization.

This Special Issue thus provides a comprehensive platform for research spanning physical, chemical, and biosensor technologies, their materials, interfaces, signal processing, networking, and transformative applications in health and sports science, perfectly fitting the multidisciplinary aims of Sensors.

Prof. Joanne Yip
Dr. Ka-Po Lee
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

  • wearable sensors
  • biomedical sensing
  • athletic performance monitoring
  • remote health monitoring
  • AI-enabled sensing
  • non-invasive biosensors
  • sensor data fusion
  • IoT for health and sports

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Published Papers (1 paper)

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Research

20 pages, 2334 KB  
Article
From Laboratory to Field: Concurrent Validity of Kinovea’s Linear Kinematics Tracking Tool for Semi-Automated Countermovement Jump Analysis
by Lucija Faj, Jelena Aleksić, Olivera M. Knežević, Branislav Božović, Hrvoje Brkić, Damir Sekulić and Dragan M. Mirkov
Sensors 2026, 26(1), 24; https://doi.org/10.3390/s26010024 - 19 Dec 2025
Viewed by 1231
Abstract
Affordable high-frame-rate cameras and open-source software, such as Kinovea (ver. 2025.1.0), have expanded the potential for conducting kinematic assessments outside laboratory settings. This study examined the reliability and validity of Kinovea’s semi-automated linear kinematics tracking tool by comparing its outputs with those from [...] Read more.
Affordable high-frame-rate cameras and open-source software, such as Kinovea (ver. 2025.1.0), have expanded the potential for conducting kinematic assessments outside laboratory settings. This study examined the reliability and validity of Kinovea’s semi-automated linear kinematics tracking tool by comparing its outputs with those from a 3D marker-based motion capture system (Qualisys). Ten recreationally active male basketball players (x̄ ± SD: age 23.7 ± 1.7 years; height 183 ± 5 cm; body mass 76.8 ± 9.8 kg) performed three CMJ trials, simultaneously recorded using both systems. Reflective markers placed on the shoulder, hip, and knee were tracked in Kinovea by two raters with different levels of experience to extract core CMJ variables (total take-off time and maximum vertical displacement) and complementary variables (eccentric and propulsion duration, and minimum vertical displacement). Inter-rater reliability and concurrent validity were evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CV%), standard error of measurement (SEM), and Bland–Altman analysis. Results showed excellent inter-rater reliability (ICC = 0.73–0.99) across all markers, with the hip and knee demonstrating the highest consistency. Strong validity relative to Qualisys was observed for both raters (ICC = 0.68–0.99; r > 0.80), with small systematic biases primarily in temporal variables. Collectively, these findings demonstrate that Kinovea’s semi-automated 2D analysis yields reliable and valid CMJ measurements comparable to 3D motion capture, even for less experienced users. As a free and easily deployable tool, it offers a widely accessible alternative for field-based performance monitoring and applied biomechanics research where laboratory-grade equipment is not available. Full article
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