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Wearable Sensors for Human Activity Monitoring
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Wearable Sensors for Human Activity Monitoring

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3416

Special Issue Editors

Dr. Dhruv R. Seshadri

E-Mail Website
Guest Editor
Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
Interests: wearable sensors; bioelectronics; digital therapeutics; sports medicine; exercise physiology
Dr. Ethan Robert Harlow

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
Interests: wearable tech; digital health; orthopedics; sports medicine; big data; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,  

The digital health field has seen a surge in product development over the last decade, with product introductions ranging from wrist monitors, epidermal electronics, electronic pills and smart garments, much of these precipitated through the commercialisation and commoditization of sensor technology. The convenient use of wireless technology to track and monitor physiological parameters, such as heart rate, distance, sleep and stress, has emerged to become relevant to patient care and human performance assessment. However, collecting data is not enough to inform clinical decision-making. It is essential to translate the acquired data into information relevant to clinicians. This Special Issue focuses on the use of wearable technology for human activity monitoring. We encourage the submission of papers that focus on the use of wearable technology for diagnosis, performance optimization and management, disease management, and outcome measures within healthy populations and patients. We are also seeking submissions of manuscripts related to the keywords listed below.

Dr. Dhruv R. Seshadri
Dr. Ethan Robert Harlow
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
  • digital health
  • human performance
  • edge computing
  • flexible electronics
  • biochemical monitoring
  • return to play
  • artificial intelligence
  • biomechanics
  • physiological monitoring

Published Papers (3 papers)

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Research

14 pages, 11959 KiB  
Article
Wearable Devices and Digital Biomarkers for Optimizing Training Tolerances and Athlete Performance: A Case Study of a National Collegiate Athletic Association Division III Soccer Team over a One-Year Period
by Dhruv R. Seshadri, Helina D. VanBibber, Maia P. Sethi, Ethan R. Harlow and James E. Voos
Sensors 2024, 24(5), 1463; https://doi.org/10.3390/s24051463 - 23 Feb 2024
Viewed by 613
Abstract
Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop [...] Read more.
Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop an algorithm for musculoskeletal modeling, and longitudinally determine the workloads of male college soccer (football) athletes at the Division III (DIII) level over the course of a 12-week season. The results showed that over the course of a season, (1) the average match workload (432 ± 47.7) was 1.5× greater than the average training workload (252.9 ± 23.3) for all positions, (2) the forward position showed the lowest workloads throughout the season, and (3) the highest mean workload was in week 8 (370.1 ± 177.2), while the lowest was in week 4 (219.1 ± 26.4). These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Monitoring)
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13 pages, 2650 KiB  
Article
Failure of Digital Device Performance in Monitoring Physical Exercise in a Pilot Study in Sedentary Persons with HIV
by Matteo Bonato, Federica Marmondi, Filippo Turrini, Andrea Albergoni, Maddalena Pennacchi, Camilla Cerizza, Maria Francesca Piacentini, Antonella Castagna, Laura Galli, Francesco Sartor and Paola Cinque
Sensors 2023, 23(23), 9461; https://doi.org/10.3390/s23239461 - 28 Nov 2023
Viewed by 699
Abstract
Digital devices have gained popularity in the last 10 years as a tool for exercise prescription, the monitoring of daily physical activity, and nutrition for the management of a health-related parameter. Therefore, the aim of this study was to assess the effectiveness of [...] Read more.
Digital devices have gained popularity in the last 10 years as a tool for exercise prescription, the monitoring of daily physical activity, and nutrition for the management of a health-related parameter. Therefore, the aim of this study was to assess the effectiveness of the use of digital devices to monitor exercise data in sedentary persons with HIV who exercise following an individualized activity pacing (AP) protocol on cardiorespiratory fitness body composition, blood lipid profile, and psychological parameters. Twenty-four PLWH were enrolled in an 18-week randomized, open-label, pilot AP exercise protocol. All participants were monitored by a Health Band connected to a mobile app that transmitted the data to a server. At week 3, they were randomized either in an experimental group (EG), in which an open device configuration enabled them to receive training data feedback (n = 12), or continued with no data feedback (control group, n = 12). The primary endpoint was improvement from the baseline of 15% of steady-state oxygen consumption (V˙O2) during a 6-min walking test. Technical issues occurred when pairing the health band with the app, which prevented EG participants from regularly receiving data feedback, and with data transmission to the server, which enabled only 40% monitoring of the total training days. Consequently, the study outcomes could not be compared between the two groups, and participants also lost confidence in the study. However, 19 out of 24 participants completed the AP program. Overall, only 6 (32%) improved steady-state V˙O2, with no significant changes at W18 from the baseline. Significant reductions were observed of BMI (p = 0.040), hip circumference (p = 0.027), and total-(p = 0.049) and HDL-cholesterol (p = 0.045). The failure of digital device performance substantially affected study procedures, monitoring, and participants’ engagement, and likely limited the potential benefits of the AP exercise program. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Monitoring)
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20 pages, 2500 KiB  
Article
Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living
by Sebastjan Šlajpah, Eva Čebašek, Marko Munih and Matjaž Mihelj
Sensors 2023, 23(3), 1289; https://doi.org/10.3390/s23031289 - 23 Jan 2023
Cited by 2 | Viewed by 1550
Abstract
Patients after stroke need to re-learn functional movements required for independent living throughout the rehabilitation process. In the study, we used a wearable sensory system for monitoring the movement of the upper limbs while performing activities of daily living. We implemented time-based and [...] Read more.
Patients after stroke need to re-learn functional movements required for independent living throughout the rehabilitation process. In the study, we used a wearable sensory system for monitoring the movement of the upper limbs while performing activities of daily living. We implemented time-based and path-based segmentation of movement trajectories and muscle activity to quantify the activities of the unaffected and the affected upper limbs. While time-based segmentation splits the trajectory in quants of equal duration, path-based segmentation isolates completed movements. We analyzed the hand movement path and forearm muscle activity and introduced a bimanual movement parameter, which enables differentiation between unimanual and bimanual activities. The approach was validated in a study that included a healthy subject and seven patients after stroke with different levels of disabilities. Path-based segmentation provides a more detailed and comprehensive evaluation of upper limb activities, while time-based segmentation is more suitable for real-time assessment and providing feedback to patients. Bimanual movement parameter effectively differentiates between different levels of upper limb involvement and is a clear indicator of the activity of the affected limb relative to the unaffected limb. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Monitoring)
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