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Applications of Wearable Sensors and Body Worn Devices

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 1 July 2027 | Viewed by 2511

Special Issue Editor


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Guest Editor
1. Research Director, Center for Advanced Surgical & Interventional Technology (CASIT), Los Angeles, CA 90095, USA
2. NSF IUCRC Center to Stream HealthCare in Place (C2SHIP), Columbia, MO 65203, USA
3. David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
Interests: digital health; wearables; decentralized clinical trials; care in place; tele-rehabilitation; wound care; fall prevention; dementia; diabetic foot; telehealth; peripheral vascular disease; movement science; mobile health; population health
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Special Issue Information

Dear Colleagues,

The growing economic burden on healthcare systems—both in developed and developing nations—is increasingly linked to population aging, with over 70% of older adults projected to require some form of long-term care. This challenge is intensified by longer life expectancies and a sharp rise in chronic health conditions. In the U.S. alone, over 10,000 individuals become eligible for Medicare each day, many of whom will develop multiple chronic diseases, representing a substantial share of overall healthcare expenditures.

Addressing chronic conditions effectively requires a fundamental shift in our care model, from reactive treatment to proactive, patient-centered care. Current healthcare systems often position patients as passive recipients of services, overlooking the transformative potential of engaging them as active participants in their own health. Chronic disease management must increasingly emphasize self-care, digital enablement, and personalized interventions supported by caregivers and health professionals alike.

The COVID-19 pandemic underscored vulnerabilities in our traditional healthcare infrastructure. It revealed how ill-equipped conventional delivery systems are to provide timely, preventive care during global crises, particularly for older adults and those with chronic illnesses. This disruption highlighted the urgent need for scalable, flexible models of care that reduce unnecessary hospitalizations and extend access to where patients feel most comfortable: at home or in the community.

Fortunately, the rapid evolution and growing acceptance of digital health technologies—particularly wearable sensors, interoperable medical devices, Internet of Things (IoT) platforms, and AI-driven analytics—have opened new avenues to reimagine the way healthcare is delivered. These innovations make it possible to enable continuous, passive, and remote monitoring; derive actionable insights from real-world, longitudinal data; and design tailored, preventive, and context-sensitive interventions that extend care beyond traditional clinical settings. However, significant challenges persist in translating high-dimensional sensor data into clinically meaningful, personalized care strategies. Bridging this gap requires interdisciplinary innovation to ensure that these tools truly empower patients in managing their health, support caregivers with timely and actionable information, and assist healthcare providers in delivering proactive, adaptive, and precise interventions across a continuum of care environments.

This Special Issue invites research contributions that harness wearable technologies, remote monitoring systems, and data-driven solutions to proactively manage chronic conditions and promote preventive care. We are particularly interested in studies that:

  • Develop digital biomarkers to monitor chronic condition severity, including motor and cognitive decline, and predict risks such as diabetic foot ulcers, falls, and unplanned hospitalizations.
  • Apply digital health technologies to manage symptoms like pain, fatigue, stress, and sleep disturbances.
  • Improve care coordination, treatment adherence, and patient engagement through connected solutions.
  • Assess environmental factors (e.g., temperature, humidity, light, air quality) that influence health and wellbeing.
  • Use advanced analytics to integrate multimodal data, visualize behavior patterns, and detect early signs of disease onset or recovery (e.g., COVID-19, post-discharge recovery).
  • Leverage edge computing and AI to enable real-time, context-aware interactions between patients and care teams.
  • Implement gamification strategies to enhance patient motivation, engagement, and adherence to personalized care plans.
  • Design technologies that support continuity of care following hospital discharge and promote sustained health management at home.
  • Develop tools to support patient prehabilitation and strengthen resilience before surgery or other major treatments.
  • Enhance communication between patients, caregivers, and clinicians through intelligent, bidirectional platforms that integrate patient-generated data into clinical workflows.
  • Enable decentralized clinical trials and hybrid models of care through remote monitoring, e-consent, and digital engagement platforms to increase access, inclusivity, and efficiency.

We welcome interdisciplinary work that advances access to care, bridges clinical and technological domains, and supports the evolution of healthcare from institution-centered to patient-centered—bringing personalized, preventive care to wherever the patient is.

Sincerely,

Prof. Dr. Bijan Najafi
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 sensors
  • digital health
  • digital biomarkers
  • healthcare

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Published Papers (3 papers)

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Research

51 pages, 29705 KB  
Article
Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone
by Ehsan Sharafian Moghaddam and Babak Hejrati
Sensors 2026, 26(10), 3166; https://doi.org/10.3390/s26103166 - 16 May 2026
Viewed by 330
Abstract
Wearable sensors are transformative tools for continuous gait assessment in daily life. Tripping, a leading cause of falls, is closely linked to inadequate foot clearance, making accurate foot height measurement critical for fall risk evaluation. Inertial measurement units offer a practical solution for [...] Read more.
Wearable sensors are transformative tools for continuous gait assessment in daily life. Tripping, a leading cause of falls, is closely linked to inadequate foot clearance, making accurate foot height measurement critical for fall risk evaluation. Inertial measurement units offer a practical solution for foot trajectory reconstruction; however, conventional drift correction methods such as zero-velocity updates fail to adequately address cumulative height errors. Recent kinematic constraint-based approaches improve height accuracy but remain limited to offline processing and lack simultaneous activity classification. To address these gaps, we developed a real-time, single-IMU system for continuous foot height trajectory reconstruction with simultaneous classification of five locomotion activities deployed on a smartphone. Twenty healthy adults were recruited for model training and independent validation. Level walking maintained ground reference (0.0 cm, 95% CI: [1.8, 1.8] cm), cumulative height errors remained below 1.1 cm across ramp and stair negotiation with a mean absolute error of 0.42%, and obstacle clearance was quantified. The system achieved 96.08% overall classification accuracy with less than one gait cycle latency. Toe height was estimated through rigid-body transformation with comparable accuracy to the foot height. This framework provides a practical foundation for real-time gait intervention and fall prevention applications. Full article
(This article belongs to the Special Issue Applications of Wearable Sensors and Body Worn Devices)
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23 pages, 1801 KB  
Article
Multimodal Fusion of Environmental and Physiological Data for Real-World Personalised Comfort Modelling
by Sothearak Heng and Ali Yavari
Sensors 2026, 26(10), 2940; https://doi.org/10.3390/s26102940 - 7 May 2026
Viewed by 882
Abstract
People spend the majority of their lives within environments shaped by multiple interacting exposures, including thermal conditions, acoustic noise, lighting, and air quality, yet remain largely unaware of how these settings affect their comfort. Existing comfort research treats domains in isolation under controlled [...] Read more.
People spend the majority of their lives within environments shaped by multiple interacting exposures, including thermal conditions, acoustic noise, lighting, and air quality, yet remain largely unaware of how these settings affect their comfort. Existing comfort research treats domains in isolation under controlled laboratory conditions, leaving real-world multi-domain effects on personal comfort underexplored. This paper proposes a unified Comfort Framework that fuses three practical data layers: macro-environmental conditions retrieved via location-based APIs, kinematic and micro-environmental context captured through smartphone sensors, and physiological responses recorded by a chest-worn ECG sensor. Binary comfort states are labelled in real time using a minimal-disruption lap-button protocol on a consumer smartwatch. We validate the pipeline through a single-subject pilot of 18 free-living sessions. Random Forest classification across 10 valid leave-one-session-out folds achieved an F1 macro of 0.456 ± 0.151, indicating that consumer wearable comfort prediction in unconstrained free-living conditions is more challenging than controlled chamber studies suggest. Descriptive statistics showed dataset-level differences between comfort states in wrist skin temperature (31.9 vs. 33.3 °C), heart rate (70.7 vs. 77.1 bpm), and RMSSD (42.1 vs. 34.3 ms), with overlap between classes consistent with the modest classification performance. SHAP analysis identified acoustic features, HRV features, and wrist temperature as the strongest comfort signals. The framework is architecturally designed to address all four IEQ domains, though this pilot empirically validated only thermal and acoustic signals. Full article
(This article belongs to the Special Issue Applications of Wearable Sensors and Body Worn Devices)
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12 pages, 1687 KB  
Article
Analysis of Gait Biomechanics in Patients After Total Hip and Knee Arthroplasty Using Low-Cost Sensors: An Observational Repeated-Measures Study
by Lea Atelšek, Matic Sašek and Žiga Kozinc
Sensors 2026, 26(9), 2731; https://doi.org/10.3390/s26092731 - 28 Apr 2026
Viewed by 720
Abstract
Osteoarthritis is a leading cause of lower-limb arthroplasty, and although total hip arthroplasty (THA) and total knee arthroplasty (TKA) reduce pain and improve quality of life, gait impairments often persist after surgery. This study aimed to analyze gait patterns in individuals following THA [...] Read more.
Osteoarthritis is a leading cause of lower-limb arthroplasty, and although total hip arthroplasty (THA) and total knee arthroplasty (TKA) reduce pain and improve quality of life, gait impairments often persist after surgery. This study aimed to analyze gait patterns in individuals following THA and TKA using the wearable RunScribe™ sensor system and to examine its sensitivity to short-term changes during rehabilitation. Thirty-seven patients (19 THA, 18 TKA) attending a two-week inpatient rehabilitation program were assessed twice, on the first and final day of rehabilitation. Gait was measured during a 2 min circular walk test, and both global spatiotemporal variables and limb-specific loading-related variables were analyzed. A significant main effect of time was observed for walking speed (p = 0.001, ηp2 = 0.284), with improvements of approximately 10% in both groups, as well as for step cadence (p < 0.001, ηp2 = 0.429) and contact time (p < 0.001, ηp2 = 0.380). Loading-related variables also changed significantly over time, including impact acceleration (p = 0.004, ηp2 = 0.226), braking acceleration (p < 0.001, ηp2 = 0.419), and rate of force development (p < 0.001, ηp2 = 0.412). No statistically significant between-group differences were observed for global gait variables, although participants following THA showed a tendency toward better walking performance (e.g., higher cadence, p = 0.065). These findings suggest that early rehabilitation is associated with measurable improvements in gait after arthroplasty and support the potential of affordable wearable sensors as practical tools for objective gait assessment in clinical settings. Full article
(This article belongs to the Special Issue Applications of Wearable Sensors and Body Worn Devices)
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