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Sensing Technology and Wearables for Physical Activity

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

Deadline for manuscript submissions: 10 August 2025 | Viewed by 8427

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Special Issue Editor

Dr. Li-Fong Lin

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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
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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

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Keywords

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

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Published Papers (4 papers)

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13 pages, 272 KiB

Open AccessArticle

Identification of Game Periods and Playing Position Activity Profiles in Elite-Level Beach Soccer Players Through Principal Component Analysis

by Pau Vaccaro Benet, Alexis Ugalde-Ramírez, Carlos D. Gómez-Carmona, José Pino-Ortega and Boryi A. Becerra-Patiño

Sensors 2024, 24(23), 7708; https://doi.org/10.3390/s24237708 - 2 Dec 2024

Viewed by 773

Abstract

Beach soccer has gained increasing interest for study in the sports sciences. In this sense, the analysis of activity profiles is important for training design and load individualization. Therefore, the aims of this study were to identify the most important variables to assess the activity profile and to compare them according to the playing position and game periods in international beach soccer matches. A total of 19 matches of the Spanish national beach soccer team were analyzed during their participation in different international competitions during the 2021–2022 season. A Principal Component Analysis (PCA) was applied to objectively select the external load variables that best explain the data. Kaiser–Meyer–Olkin values of 0.705 and Bartlett’s Sphericity (p < 0.01) were obtained. Kruskal–Wallis and Friedman tests was performed for playing positions and game period comparisons, respectively. The PCA showed seven components that grouped a total of 20 variables, explaining 66% of the total variance. Only PC1 and PC2 explained more than 15% (23% and 17%, respectively). Differences were found between playing positions (H > 22.73; p < 0.01) and between game periods (X2 > 16.46; p < 0.01). A significant decrease was found throughout the game, with the highest demands in period 1 and the lowest in period 3. The highest workloads were performed by wingers and the lowest by goalkeepers. The differences between positions and game periods were found in the following: Total Distance (m/min), HIBD (m/min), High Acc (m/s), High Dec (m/s), Acc 1–2 m/s² (m), Acc 2–3 m/s² (m), Imp 4–5G (n), Imp 5–6G (n), Sprint (n), and Dec 10–6 m/s² (m) (p < 0.01). In conclusion, physical and tactical demands faced by elite-level beach soccer players will be influenced by playing positions and game periods. Coaches needs to develop position-specific training programs and optimize substitution strategies for enhancing overall team performance. Full article

(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)

12 pages, 395 KiB

Open AccessArticle

Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms

by Łukasz Czekaj, Mateusz Kowalewski, Jakub Domaszewicz, Robert Kitłowski, Mariusz Szwoch and Włodzisław Duch

Sensors 2024, 24(12), 3891; https://doi.org/10.3390/s24123891 - 15 Jun 2024

Cited by 2 | Viewed by 1670

Abstract

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human–computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model’s quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research. Full article

(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)

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17 pages, 1263 KiB

Open AccessArticle

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 1995

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 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

Open AccessSystematic 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 - 6 Nov 2023

Cited by 1 | Viewed by 2937

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 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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