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

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 19597

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Special Issue Editors

Dr. Salvatore Tedesco

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Guest Editor

Wireless Sensor Network Group, Micro and Nano Systems Centre, Tyndall National Institute, University College Cork, T12R5CP Cork, Ireland
Interests: wearable technologies for healthcare and wellbeing; human motion analysis in sports and clinical populations; digital health; physiological monitoring; signal processing; edge analytics; machine learning

Dr. Sokratis Komaris

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Guest Editor

Lecturer in Biomedical Engineering, Aston University, College of Engineering & Physical Sciences, School of Engineering & Technology, Birmingham B4 7ET, UK
Interests: wearables for healthcare and wellbeing; human motion analysis in sports and clinical populations; digital health; acoustic emission monitoring; physiological monitoring; signal processing; the biomechanics of knee and hip implants

Special Issue Information

Dear Colleagues,

An explosive growth in wearable technology has been witnessed in recent years. This area is experiencing massive expansion thanks to huge technical advances in information and communications technology driven by changes in demography, lifestyle, environment, etc. Wearable sensors are currently popular as personal tracking devices, but wearables can assume a more significant role in multiple applications, such as personalized health, sports, rehabilitation, etc. In conjunction with technological advances in smart systems, the continuous growth in numbers of connected wearable devices demonstrates major issues in terms of dealing with huge amounts of data originating from heterogeneous devices. As a result, machine learning and artificial intelligence will enable the real-time recognition of patterns in sensor data, which can help to identify events of interest and provide real-time feedback on such events to the wearer or caregiver so appropriate decisions can be made, thus enhancing the practical applications of wearable technology in a number of domains and driving the vision for the ubiquitous adoption of wearables in healthcare and wellbeing becoming accessible to a wider section of society.

To advance the state of the art, we solicit research contributions focused on novel wearable technology and sensors for healthcare and wellbeing applications, with particular attention to: human motion analysis and (tele)rehabilitation; geriatric care, healthy ageing and chronic disease management (i.e., Parkinson’s disease); health markers, physiological monitoring and emotion AI (also known as affective computing); sports analytics, fitness and injury prevention; fundamental research in machine learning applicable to wearables (i.e., human activity recognition, edge analytics, time series analysis, physics-informed AI, etc.); and novel hardware prototypes (i.e., smart textile, wearable robotic devices).

Dr. Salvatore Tedesco
Dr. Sokratis Komaris
Guest Editors

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Keywords

wearable technology
wearable sensors
human motion analysis
rehabilitation
telehealth
healthy ageing
chronic disease management
sports analytics
healthcare, wellbeing and fitness
health markers
physiological monitoring
emotion AI
injury prevention
machine learning
smart textiles
human activity recognition
edge analytics
hardware prototypes
physics-informed AI
time series analysis

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Published Papers (12 papers)

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Research

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15 pages, 2282 KiB

Open AccessArticle

Wearing WHOOP More Frequently Is Associated with Better Biometrics and Healthier Sleep and Activity Patterns

by Gregory J. Grosicki, Finnbarr Fielding, Jeongeun Kim, Christopher J. Chapman, Maria Olaru, William von Hippel and Kristen E. Holmes

Sensors 2025, 25(8), 2437; https://doi.org/10.3390/s25082437 - 12 Apr 2025

Viewed by 328

Abstract

Wearable devices are increasingly used for health monitoring, yet the impact of consistent wear on physiological and behavioral outcomes is unclear. Leveraging nearly a million days and nights of longitudinal data from 11,914 subscribers, we examined the associations between the frequency of wearing a wrist-worn wearable device (WHOOP Inc., Boston, MA, USA) and 12-week changes in biometric, sleep, and activity profiles, modeling both between- and within-person effects. Higher average wear frequency and week-to-week increases in wear were associated with a lower resting heart rate (RHR), higher heart rate variability (HRV), longer and more consistent sleep, and greater weekly and daily physical activity duration (Ps < 0.01). A within-person multiple mediation analysis indicated that increased sleep duration partially mediated the association between wear frequency and a standardized (z-scored) RHR (indirect effect = −0.0387 [95% CI: −0.0464, −0.0326]), whereas physical activity minutes did not (indirect effect = 0.0003 [95% CI: −0.0036, 0.0040]). A Granger causality analysis revealed a modest but notable association between prior wear frequency and future RHR in participants averaging ≤5 days of weekly wear (p < 0.05 in 10.92% of tests). While further research is needed, our findings provide real-world evidence that sustained wearable engagement may support healthier habits and improved physiological outcomes over time. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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12 pages, 1627 KiB

Open AccessArticle

Characterization of Medical Neck Palpation to Inform Design of Haptic Palpation Sensors

by Angela Chan, Anzu Kawazoe, Noah Kim, Rebecca Fenton Friesen, Thomas K. Ferris, Francis Quek and M. Cynthia Hipwell

Sensors 2025, 25(7), 2159; https://doi.org/10.3390/s25072159 - 28 Mar 2025

Viewed by 305

Abstract

Medical palpation is a task that traditionally requires a skilled practitioner to assess and diagnose a patient through direct touch and manipulation of their body. In regions with a shortage of such professionals, robotic hands or sensorized gloves could potentially capture the necessary haptic information during palpation exams and relay it to medical doctors for diagnosis. From an engineering perspective, a comprehensive understanding of the relevant motions and forces is essential for designing haptic technologies capable of fully capturing this information. This study focuses on thyroid examination palpation, aiming to analyze the hand motions and forces applied to the patient’s skin during the procedure. We identified key palpation techniques through video recordings and interviews and measured the force characteristics during palpation performed by both non-medical participants and medical professionals. Our findings revealed five primary palpation hand motions and characterized the multi-dimensional interaction forces involved in these motions. These insights provide critical design guidelines for developing haptic sensing and display technologies optimized for remote thyroid nodule palpation and diagnosis. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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24 pages, 1329 KiB

Open AccessArticle

Personalised Risk Modelling for Older Adult Cancer Survivors: Combining Wearable Data and Self-Reported Measures to Address Time-Varying Risks

by Zoe Valero-Ramon, Gema Ibanez-Sanchez, Antonio Martinez-Millana and Carlos Fernandez-Llatas

Sensors 2025, 25(7), 2097; https://doi.org/10.3390/s25072097 - 27 Mar 2025

Viewed by 270

Abstract

Recent advancements in wearable devices have significantly enhanced remote patient monitoring, enabling healthcare professionals to evaluate conditions within home settings. While electronic health records (EHRs) offer extensive clinical data, they often lack crucial contextual information about patients’ daily lives and symptoms. By integrating continuous self-reported outcomes related to vulnerability, anxiety, and depression from older adult cancer survivors with objective data from wearables, we can develop personalised risk models that address time-varying risk factors in cancer care. Our study combines real-world data from wearable devices with self-reported information, employing process mining techniques to analyse dynamic risk models for vulnerability and anxiety. Unlike traditional static assessments, this approach recognises that risk factors evolve. Collaborating with healthcare professionals, we analysed data from the LifeChamps study to create two dynamic risk models. This collaborative effort revealed how activity and sleep patterns influence self-reported vulnerability and anxiety among participants. It underscored the potential of wearable sensors and artificial intelligence techniques for deeper analysis and understanding, making us all part of a larger effort in cancer care. Overall, patients with prolonged sedentary activity had a higher risk of vulnerability, while those with highly dynamic sleep patterns were more likely to report anxiety and depression. Prostate-metastatic patients showed an increased risk of vulnerability compared to other cancer types. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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15 pages, 1378 KiB

Open AccessArticle

Utilising Inertial Measurement Units and Force–Velocity Profiling to Explore the Relationship Between Hamstring Strain Injury and Running Biomechanics

by Lisa Wolski, Mark Halaki, Claire E. Hiller, Evangelos Pappas and Alycia Fong Yan

Sensors 2025, 25(5), 1518; https://doi.org/10.3390/s25051518 - 28 Feb 2025

Viewed by 836

Abstract

The purpose of this study was to retrospectively and prospectively explore associations between running biomechanics and hamstring strain injury (HSI) using field-based technology. Twenty-three amateur sprinters performed 40 m maximum-effort sprints and then underwent a one-year injury surveillance period. For the first 30 m of acceleration, sprint mechanics were quantified through force–velocity profiling. In the upright phase of the sprint, an inertial measurement unit (IMU) system measured sagittal plane pelvic and hip kinematics at the point of contact (POC), as well as step and stride time. Cross-sectional analysis revealed no differences between participants with a history of HSI and controls except for anterior pelvic tilt (increased pelvic tilt on the injured side compared to controls). Prospectively, two participants sustained HSIs in the surveillance period; thus, the small sample size limited formal statistical analysis. A review of cohort percentiles, however, revealed both participants scored in the higher percentiles for variables associated with a velocity-oriented profile. Overall, this study may be considered a feasibility trial of novel technology, and the preliminary findings present a case for further investigation. Several practical insights are offered to direct future research to ultimately inform HSI prevention strategies. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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12 pages, 878 KiB

Open AccessCommunication

Depression Recognition Using Daily Wearable-Derived Physiological Data

by Xinyu Shui, Hao Xu, Shuping Tan and Dan Zhang

Sensors 2025, 25(2), 567; https://doi.org/10.3390/s25020567 - 19 Jan 2025

Cited by 2 | Viewed by 1906

Abstract

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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16 pages, 1004 KiB

Open AccessArticle

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step

by Andrew Smith, Musa Azeem, Chrisogonas O. Odhiambo, Pamela J. Wright, Hanim E. Diktas, Spencer Upton, Corby K. Martin, Brett Froeliger, Cynthia F. Corbett and Homayoun Valafar

Sensors 2024, 24(14), 4542; https://doi.org/10.3390/s24144542 - 13 Jul 2024

Cited by 1 | Viewed by 1762

Abstract

The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F₁ score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method’s high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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22 pages, 5274 KiB

Open AccessArticle

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

by Andrea Valerio, Danilo Demarchi, Brendan O’Flynn, Paolo Motto Ros and Salvatore Tedesco

Sensors 2024, 24(11), 3697; https://doi.org/10.3390/s24113697 - 6 Jun 2024

Cited by 1 | Viewed by 1229

Abstract

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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14 pages, 2105 KiB

Open AccessArticle

Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults

by Andrée-Anne Parent, Veronica Guadagni, Jean M. Rawling and Marc J. Poulin

Sensors 2024, 24(7), 2218; https://doi.org/10.3390/s24072218 - 30 Mar 2024

Cited by 1 | Viewed by 2950

Abstract

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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19 pages, 5914 KiB

Open AccessArticle

A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes

by Guanlin Ding, Ioannis Georgilas and Andrew Plummer

Sensors 2024, 24(1), 211; https://doi.org/10.3390/s24010211 - 29 Dec 2023

Cited by 4 | Viewed by 2732

Abstract

Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired t-tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired t-test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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15 pages, 1276 KiB

Open AccessArticle

STELO: A New Modular Robotic Gait Device for Acquired Brain Injury—Exploring Its Usability

by Carlos Cumplido-Trasmonte, Eva Barquín-Santos, María Dolores Gor-García-Fogeda, Alberto Plaza-Flores, David García-Varela, Leticia Ibáñez-Herrán, Carlos González-Alted, Paola Díaz-Valles, Cristina López-Pascua, Arantxa Castrillo-Calvillo, Francisco Molina-Rueda, Roemi Fernandez and Elena Garcia-Armada

Sensors 2024, 24(1), 198; https://doi.org/10.3390/s24010198 - 29 Dec 2023

Cited by 3 | Viewed by 2205

Abstract

In recent years, the prevalence of acquired brain injury (ABI) has been on the rise, leading to impaired gait functionality in affected individuals. Traditional gait exoskeletons are typically rigid and bilateral and lack adaptability. To address this, the STELO, a pioneering modular gait-assistive device, was developed. This device can be externally configured with joint modules to cater to the diverse impairments of each patient, aiming to enhance adaptability and efficiency. This study aims to assess the safety and usability of the initial functional modular prototype, STELO, in a sample of 14 ABI-diagnosed participants. Adverse events, device adjustment assistance and time, and gait performance were evaluated during three sessions of device use. The results revealed that STELO was safe, with no serious adverse events reported. The need for assistance and time required for device adjustment decreased progressively over the sessions. Although there was no significant improvement in walking speed observed after three sessions of using STELO, participants and therapists reported satisfactory levels of comfort and usability in questionnaires. Overall, this study demonstrates that the STELO modular device offers a safe and adaptable solution for individuals with ABI, with positive user and therapist feedback. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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18 pages, 2685 KiB

Open AccessArticle

Familiarization with Mixed Reality for Individuals with Autism Spectrum Disorder: An Eye Tracking Study

by Maxime Leharanger, Eder Alejandro Rodriguez Martinez, Olivier Balédent and Luc Vandromme

Sensors 2023, 23(14), 6304; https://doi.org/10.3390/s23146304 - 11 Jul 2023

Cited by 4 | Viewed by 2776

Abstract

Mixed Reality (MR) technology is experiencing significant growth in the industrial and healthcare sectors. The headset HoloLens 2 displays virtual objects (in the form of holograms) in the user’s environment in real-time. Individuals with Autism Spectrum Disorder (ASD) exhibit, according to the DSM-5, persistent deficits in communication and social interaction, as well as a different sensitivity compared to neurotypical (NT) individuals. This study aims to propose a method for familiarizing eleven individuals with severe ASD with the HoloLens 2 headset and the use of MR technology through a tutorial. The secondary objective is to obtain quantitative learning indicators in MR, such as execution speed and eye tracking (ET), by comparing individuals with ASD to neurotypical individuals. We observed that 81.81% of individuals with ASD successfully familiarized themselves with MR after several sessions. Furthermore, the visual activity of individuals with ASD did not differ from that of neurotypical individuals when they successfully familiarized themselves. This study thus offers new perspectives on skill acquisition indicators useful for supporting neurodevelopmental disorders. It contributes to a better understanding of the neural mechanisms underlying learning in MR for individuals with ASD. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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20 pages, 1359 KiB

Open AccessReview

Transdisciplinary Innovations in Athlete Health: 3D-Printable Wearable Sensors for Health Monitoring and Sports Psychology

by Mustafa Onder Sekeroglu, Metin Pekgor, Aydolu Algin, Turhan Toros, Emre Serin, Meliha Uzun, Gunay Cerit, Tugba Onat and Sermin Agrali Ermis

Sensors 2025, 25(5), 1453; https://doi.org/10.3390/s25051453 - 27 Feb 2025

Cited by 1 | Viewed by 1036

Abstract

The integration of 3D printing technology into wearable sensor systems has catalyzed a paradigm shift in sports psychology and athlete health monitoring by enabling real-time, personalized data collection on physiological and psychological states. In this study, not only is the technical potential of these advancements examined but their real-world applications in sports psychology are also critically assessed. While the existing research primarily focuses on sensor fabrication and data acquisition, a significant gap remains in the evaluation of their direct impact on decision-making processes in coaching, mental resilience, and long-term psychological adaptation in athletes. A critical analysis of the current state of 3D-printed wearable sensors is conducted, highlighting both their advantages and limitations. By combining theoretical insights with practical considerations, a comprehensive framework is established for understanding how sensor-based interventions can be effectively incorporated into sports training and psychological evaluation. Future research should prioritize longitudinal studies, athlete-centered validation, and interdisciplinary collaborations to bridge the gap between technological developments and real-world applications. Additionally, the integration of artificial intelligence and advanced biomaterials has significant potential to enhance the reliability and interpretability of sensor-driven interventions. However, without rigorous scientific validation, their effectiveness remains uncertain. This study highlights the importance of a systematic approach in implementing and evaluating 3D-printed wearable sensors in sports psychology. Full article

(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)

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