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Wearable Technologies and Sensors for Healthcare and Wellbeing
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Wearable Technologies and Sensors for Healthcare and Wellbeing

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4926

Special Issue Editors

Dr. Salvatore Tedesco

E-Mail Website
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

E-Mail Website
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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 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

Published Papers (4 papers)

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Research

14 pages, 2105 KiB  
Article
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
Viewed by 1065
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 [...] Read more.
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  
Article
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 1 | Viewed by 1003
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 [...] Read more.
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  
Article
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 1 | Viewed by 941
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 [...] Read more.
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  
Article
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 1 | Viewed by 1290
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, [...] Read more.
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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