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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 28185

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Special Issue Editor

Dr. Li-Fong Lin

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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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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

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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 (11 papers)

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18 pages, 1368 KB

Open AccessArticle

Comparative Validity of Smartwatch-Derived Heart Rate and Energy Expenditure During Endurance and Resistance Exercise

by Tae-Hyung Lee, Dong-Uk Jun, Ju-Yong Bae, Hee-Tae Roh and Su-Youn Cho

Sensors 2026, 26(8), 2526; https://doi.org/10.3390/s26082526 - 19 Apr 2026

Viewed by 145

Abstract

Smartwatches are widely used to monitor physiological responses during exercise; however, their accuracy in measuring heart rate (HR) and energy expenditure (EE) across different exercise modalities remains insufficiently characterized. This study evaluated the accuracy of HR and EE measurements obtained from four commercially available smartwatches in comparison with gold-standard reference methods. Sixty-two healthy adult men performed standardized endurance and resistance exercise protocols while simultaneously wearing four smartwatches (Apple, Galaxy, Fitbit, and Garmin). HR was measured using electrocardiography (ECG), and EE was determined using indirect calorimetry. Measurement accuracy was assessed using repeated-measures analysis of variance, Pearson’s correlation analysis, intraclass correlation coefficients (ICCs), and Bland–Altman analyses. All smartwatches demonstrated high accuracy in HR measurements during both endurance and resistance exercises. During endurance exercise, HR measurements from all smartwatch brands were comparable to those obtained via ECG, whereas during resistance exercise, only the Apple Watch showed no significant difference from the ECG. HRs showed strong correlations with ECG readings (r = 0.64–0.97), excellent reliability (ICC > 0.94), and narrow limits of agreement (approximately ±10 bpm). In contrast, the EE measurements exhibited limited accuracy across all devices. During endurance exercise, EE was consistently underestimated with wide limits of agreement. EE accuracy further deteriorated during resistance exercise, showing weak correlations with indirect calorimetry (r = 0.10–0.34) and poor reliability (ICC < 0.45). Overall, smartwatches provide accurate HR measurements across endurance and resistance exercise modalities, supporting their use in exercise intensity monitoring and HR-based training. However, smartwatch-derived EE estimates do not accurately reflect the metabolic demands, particularly during resistance exercises. Future research should focus on improving EE estimation algorithms through multimodal biosignal integration and machine-learning approaches, and validating these methods across diverse populations and exercise modalities. Full article

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

21 pages, 4407 KB

Open AccessArticle

An Intelligent Pressurized Thigh Band for Muscular Assistance and Multi-Mode Activity Recognition

by Wenda Wang, Wenbin Jiang, Yang Yu, Wei Dong, Hui Dong, Yongzhuo Gao, Dongmei Wu and Weiqi Lin

Sensors 2026, 26(5), 1502; https://doi.org/10.3390/s26051502 - 27 Feb 2026

Viewed by 1269

Abstract

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control. Full article

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

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14 pages, 764 KB

Open AccessArticle

Agreement Between Reserve Heart Rate, Perceived Exertion and Wint Index During HIIT Using a Low-Cost ANT+ Armband in University Students

by Julio Martín-Ruiz and Laura Ruiz-Sanchis

Sensors 2026, 26(3), 1049; https://doi.org/10.3390/s26031049 - 5 Feb 2026

Viewed by 411

Abstract

High-intensity interval training (HIIT) provides substantial cardiovascular benefits; however, precise monitoring typically requires expensive devices. These systems are feasible in research laboratories but are costly for schools and the fitness industry. Low-cost, validated devices are required to facilitate broader implementation. A cross-sectional study was conducted with 213 students (173 men and 40 women) from the Catholic University of Valencia, Spain. The participants completed an HIIT protocol consisting of five 3 min blocks. Heart rate (HR) was recorded using a Moofit HW401 armband (ANT+ technology). Ratings of perceived exertion (RPE, Omni-Res scale) and the Wint index were also obtained. Pearson correlations were computed between reserve heart rate (HRr), RPE, and Wint index during the warm-up phases (T1, T2) and HIIT, stratified by sex, age, and body mass index (BMI). HRr was strongly correlated with the Wint index (r = 0.95, p < 0.0001) and moderately correlated with RPE (r = 0.235, p = 0.001). No significant sex differences were observed (men 83.66 ± 8.18% vs. women 82.31 ± 10.89%; p > 0.05). Correlations were weaker in participants with extreme BMI values (n < 10, obese). The Moofit HW401 armband showed consistent agreement between HRr, RPE, and Wint index during HIIT, supporting its practical use for group monitoring in educational settings, pending formal validation against gold standards. Full article

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

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11 pages, 1712 KB

Open AccessArticle

Evaluation of Reaction Time and Hand–Eye Coordination in Schoolchildren Using Wearable Sensor-Based Systems: A Study with Neural Trainer Devices

by José Alfredo Sulla-Torres, Nadia Yunorvi Chavez-Salas, María Fernanda Valverde-Riveros, Diego Alonso Iquira-Becerra, Karina Rosas-Paredes and Marco Antonio Cossio-Bolaños

Sensors 2025, 25(22), 7006; https://doi.org/10.3390/s25227006 - 17 Nov 2025

Viewed by 1348

Abstract

Reaction time and hand–eye coordination are critical neuromotor skills in school-aged children, influencing academic, cognitive, and motor development. The objective of this study was to evaluate schoolchildren’s performance on reaction time tests using Neural Trainer device sensors and wearable technology, establishing baseline metrics and identifying lateral performance asymmetries. Fifty-nine schoolchildren performed six sensor-based motor tests involving bimanual and unimanual interaction: P1 (10 timed repetitions, bimanual), P2 (10 timed repetitions, left hand), P3 (10 timed repetitions, right hand), P4 (hits, bimanual), P5 (hits, left hand), and P6 (hits, right hand). Neural Trainer devices with four light nodes were used for activity monitoring. Data was analyzed using statistical methods to assess time, accuracy, and variability. The results showed that the average times were P1 = 8.69 ± 1.44 s, P2 = 8.90 ± 1.30 s, and P3 = 8.83 ± 1.29 s. The average successes were P4 = 22.90 ± 3.10, P5 = 22.00 ± 3.40, and P6 = 24.42 ± 2.72 hits. Significant differences were found between hands in successes (p < 0.001) but not in times (p = 0.716). The ANOVA for the hit trials revealed significant differences between conditions, F(2, 174) = 9.30, p < 0.001. The conclusions indicate that sensor-based systems such as the Neural Trainer device demonstrated the potential to provide objective and consistent measurements of reaction time in schoolchildren; however, further studies comparing its performance with established clinical assessment tools are necessary to confirm its validity and diagnostic accuracy. Full article

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

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27 pages, 3836 KB

Open AccessArticle

A Feature Engineering Method for Smartphone-Based Fall Detection

by Pengyu Guo and Masaya Nakayama

Sensors 2025, 25(20), 6500; https://doi.org/10.3390/s25206500 - 21 Oct 2025

Viewed by 1873

Abstract

A fall is defined as an event in which a person inadvertently comes to rest on the ground, floor, or another lower level. It is the second leading cause of unintentional death worldwide, with the elderly population (aged 65 and above) at the highest risk. In addition to preventing falls, timely and accurate detection is crucial to enable effective treatment and reduce potential injury. In this work, we propose a smartphone-based method for fall detection, employing K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers to predict fall events from accelerometer data. We evaluated the proposed method on two simulated datasets (UniMiB SHAR and MobiAct) and one real-world fall dataset (FARSEEING), performing both same-dataset and cross-dataset evaluations. In same-dataset evaluation on UniMiB SHAR, the method achieved an average accuracy of 98.45% in Leave-One-Subject-Out (LOSO) cross-validation. On MobiAct, it achieved a peak accuracy of 99.89% using KNN. In cross-dataset validation on MobiAct, the highest accuracy reached 96.41%, while on FARSEEING, the method achieved 95.35% sensitivity and 98.12% specificity. SHAP-based interpretability analysis was further conducted to identify the most influential features and provide insights into the model’s decision-making process. These results demonstrate the high effectiveness, robustness, and transparency of the proposed approach in detecting falls across different datasets and scenarios. Full article

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

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11 pages, 1516 KB

Open AccessArticle

Variability and Reliability of the Axivity AX6 Accelerometer in Technical and Human Motion Conditions

by Marcos Echevarría-Polo, Pedro J. Marín, Esther Pueyo, Javier Ramos Maqueda and Nuria Garatachea

Sensors 2025, 25(8), 2480; https://doi.org/10.3390/s25082480 - 15 Apr 2025

Cited by 2 | Viewed by 2679

Abstract

This study aimed to evaluate the intra- and inter-instrument variability and reliability of the Axivity AX6 accelerometer under controlled technical conditions and human motion scenarios. In the first experiment, 12 accelerometers were affixed to a vibration platform and tested at four frequencies (2.2, 3.2, 6.5, and 9.4 Hz) along three axes to assess frequency- and axis-dependent variability. In the second experiment, four AX6 accelerometers were simultaneously placed on a subject’s wrist and tested under four human motion conditions (walking at 4 km·h⁻¹ and 6 km·h⁻¹ and running at 8 km·h⁻¹ and 10 km·h⁻¹). Results demonstrated low intra- and inter-instrument variability (CVintra: 3.3–4.5%; CVinter: 6.3–7.7%) with high reliability (ICC = 0.98). Similar results were observed in human motion conditions (CVintra: 5.3–8.8%; CVinter: 7.1–10.4%), with ICC values of 0.98 for combined devices, and 0.99 for each device individually. Despite statistically significant differences (p < 0.05) between devices in human motion all conditions, the variations remained below the minimal clinically significant difference threshold. These findings indicate that under technical conditions on a vibrating platform, and within the range of typical human accelerations, the Axivity AX6 is a reliable tool for measuring accelerations representative of physical activity. However, further research is necessary to validate its performance under free-living conditions. Full article

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

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23 pages, 1713 KB

Open AccessArticle

Sensing the Inside Out: An Embodied Perspective on Digital Animation Through Motion Capture and Wearables

by Katerina El-Raheb, Lori Kougioumtzian, Vilelmini Kalampratsidou, Anastasios Theodoropoulos, Panagiotis Kyriakoulakos and Spyros Vosinakis

Sensors 2025, 25(7), 2314; https://doi.org/10.3390/s25072314 - 5 Apr 2025

Cited by 4 | Viewed by 3345

Abstract

Over the last few decades, digital technology has played an important role in innovating the pipeline, techniques, and approaches for creating animation. Sensors for motion capture not only enabled the incorporation of physical human movement in all its precision and expressivity but also created a field of collaboration between the digital and performing arts. Moreover, it has challenged the boundaries of cinematography, animation, and live action. In addition, wearable technology can capture biosignals such as heart rate and galvanic skin response that act as indicators of the emotional state of the performer. Such metrics can be used as metaphors to visualise (or sonify) the internal reactions and bodily sensations of the designed animated character. In this work, we propose a framework for incorporating the role of the performer in digital character animation as a real-time designer of the character’s affect, expression, and personality. Within this embodied perspective, sensors that capture the performer’s movement and biosignals are viewed as the means to build the nonverbal personality traits, cues, and signals of the animated character and their narrative. To do so, following a review of the state of the art and relevant literature, we provide a detailed description of what constitute nonverbal personality traits and expression in animation, social psychology, and the performing arts, and we propose a workflow of methodological and technological toolstowardsan embodied perspective for digital animation. Full article

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

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13 pages, 272 KB

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

Cited by 6 | Viewed by 2482

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 KB

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 10 | Viewed by 3908

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 KB

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

Cited by 3 | Viewed by 3304

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 KB

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 7 | Viewed by 5693

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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