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Sensing Technology and Wearables for Physical Activity

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3193

Special Issue Editor


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Guest Editor
1. School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
2. Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
Interests: sport; physical activity; rehabilitation; physiotherapy interventions (including robotic gait training, wearable devices, etc.); balance and gait technology monitors in musculoskeletal impairment, stroke, traumatic brain injury, sport concussion, dementia, and geriatrics; virtual reality training and healthcare education; health literacy; game-based interventions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Physical activity assessments and interventions have been well developed in the domains of sport, rehabilitation, and physiotherapy (including balance, robotic gait, wearable devices etc.). Balance and gait technology monitors were applied in sport players, geriatrics, musculoskeletal impairment, stroke, traumatic brain injury, sport concussion, and dementia. Furthermore, virtual reality technology was also designed as an add-on for physical activity training. Over 1 billion people—about 15% of the global population—currently experience disability, and this number is increasing due, in part, to population aging and an increase in the prevalence of noncommunicable diseases. Disability results from the interaction between individuals with a health condition, such as musculoskeletal impairment, stroke, head trauma, cerebral palsy, and frailty, with personal and environmental factors, including negative attitudes, inaccessible transportation and public buildings, and limited social support.

In the past decade or so, solid evidence from the physical activities of sport players, geriatrics, and the disabled has accumulated, including observation and intervention experimental research. This new “Sensing Technology and Wearables for Physical Activity” Special Issue is characterized by advanced research methods, such as prospective longitudinal designs, random controlled trials, meta-analyses, innovative technologies (such as virtual reality, robotics, and wearable devices), and the application of these methods as well as technologies in “special needs” groups, including sport players, geriatric and clinical populations (musculoskeletal impairment, stroke, head trauma, dementia, cerebral palsy, COPD, etc.), frailty, sarcopenia, and older people. Papers addressing these topics are invited for this Special Issue, especially those combining a high academic standard coupled with a practical focus on providing advances in sport, gerontology, physiotherapy, and rehabilitation.

Dr. Li-Fong Lin
Guest Editor

Manuscript Submission Information

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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

  • sport
  • gerontology
  • physical activity
  • exercise
  • physiotherapy
  • rehabilitation
  • sensor
  • virtual reality
  • robotic
  • wearable device
  • balance
  • gait

Published Papers (2 papers)

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17 pages, 1263 KiB  
Article
Sensor-Based Assessment of Time-of-Day-Dependent Physiological Responses and Physical Performances during a Walking Football Match in Higher-Weight Men
by Sami Hidouri, Tarak Driss, Sémah Tagougui, Noureddine Kammoun, Hamdi Chtourou and Omar Hammouda
Sensors 2024, 24(3), 909; https://doi.org/10.3390/s24030909 - 30 Jan 2024
Viewed by 944
Abstract
Monitoring key physiological metrics, including heart rate and heart rate variability, has been shown to be of value in exercise science, disease management, and overall health. The purpose of this study was to investigate the diurnal variation of physiological responses and physical performances [...] Read more.
Monitoring key physiological metrics, including heart rate and heart rate variability, has been shown to be of value in exercise science, disease management, and overall health. The purpose of this study was to investigate the diurnal variation of physiological responses and physical performances using digital biomarkers as a precise measurement tool during a walking football match (WFM) in higher-weight men. Nineteen males (mean age: 42.53 ± 12.18 years; BMI: 33.31 ± 4.31 kg·m−2) were engaged in a WFM at two different times of the day. Comprehensive evaluations of physiological parameters (e.g., cardiac autonomic function, lactate, glycemia, and oxygen saturation), along with physical performance, were assessed before, during, and after the match. Overall, there was a significant interaction (time of day x WFM) for mean blood pressure (MBP) (p = 0.007) and glycemia (p = 0.039). Glycemia decreased exclusively in the evening after WFM (p = 0.001), while mean blood pressure did not significantly change. Rating of perceived exertion was significantly higher in the evening than in the morning (p = 0.04), while the heart rate recovery after 1 min (HRR60s) of the match was lower in the evening than in the morning (p = 0.048). Overall, walking football practice seems to be safe, whatever the time of day. Furthermore, HRR60, glycemia, and (MBP) values were lower in the evening compared to the morning, suggesting that evening exercise practice could be safer for individuals with higher weight. The utilization of digital biomarkers for monitoring health status during WFM has been shown to be efficient. Full article
(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)
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34 pages, 8989 KiB  
Systematic Review
Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization
by Sebastian Helmstetter and Sven Matthiesen
Sensors 2023, 23(21), 8997; https://doi.org/10.3390/s23218997 - 06 Nov 2023
Viewed by 1633
Abstract
Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an [...] Read more.
Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary. Full article
(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)
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