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Wearable Sensing in Rehabilitation Therapy and Human Activity Analysis

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1898

Special Issue Editors


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Guest Editor
Department of Physics, NOVA School of Science and Technology, NOVA University of Lisbon, 2825-149 Caparica, Portugal
Interests: technology; innovation; rehabilitation; physiological parameters
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Escola Superior de Saúde, Instituto Politécnico de Setúbal (ESS/IPS), 2910-761 Setúbal, Portugal
2. Comprehensive Health Research Centre (CHRC), Lisboa, Portugal
Interests: stroke; Parkinson’s disease; wearable sensing; brain-computer interfaces in rehabilitation; post-stroke rehabilitation

Special Issue Information

Dear Colleagues,

In recent years, wearable sensing technologies have emerged as transformative tools bridging the gap between laboratory-based assessments and real-world functional monitoring. The ability to provide continuous, non-invasive, and real-time data has significantly expanded the potential for personalized rehabilitation and the objective analysis of human activity.

This Special Issue places particular emphasis on the application of wearable sensing technologies to support individualized and adaptive rehabilitation strategies. By enabling the continuous and objective collection of physiological and functional data, wearable systems allow clinicians to tailor interventions to each patient’s specific clinical profile. These data are not only essential for guiding clinical decision-making throughout the rehabilitation process, but also for evaluating the effectiveness of interventions and monitoring their long-term impact on the individual’s quality of life, particularly in cases of functional impairment or chronic conditions.

Submissions that address the development, implementation, and clinical evaluation of sensor-based approaches in rehabilitation settings are particularly encouraged, especially those that demonstrate measurable improvements in patient outcomes and contribute to the advancement of evidence-based practice through real-world validation.

Topics of interest include, but are not limited to, gait and posture assessment; upper and lower limb functional analysis; wearable electromyography and inertial sensing for motor evaluation and therapeutic feedback; objective monitoring of physiological parameters relevant to rehabilitation, such as electrodermal activity; remote patient monitoring systems; telerehabilitation frameworks; and activity recognition in daily living environments for individuals with neurological or musculoskeletal conditions.

By highlighting innovative research in wearable sensing, this Special Issue aims to advance the field of rehabilitation technologies and promote the integration of data-driven approaches in both research and clinical practice.

Dr. Cláudia Regina Pereira Quaresma
Dr. Carla M. Pereira
Guest Editors

Manuscript Submission Information

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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
  • personalized rehabilitation
  • physiological monitoring
  • functional assessment
  • activity recognition

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Published Papers (2 papers)

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Research

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19 pages, 94562 KB  
Article
Application of a Smart Orthosis in the Treatment of Idiopathic Scoliosis—A Pilot Case Study
by Patrycja Tymińska-Wójcik, Katarzyna Zaborowska-Sapeta and Tomasz Giżewski
Sensors 2026, 26(10), 3169; https://doi.org/10.3390/s26103169 - 17 May 2026
Viewed by 341
Abstract
The increasing demand for personalized conservative treatment of idiopathic scoliosis (IS) highlights the need for objective and continuous monitoring of corrective forces during brace therapy. This study aims to evaluate the feasibility and clinical relevance of a smart orthopedic brace equipped with integrated [...] Read more.
The increasing demand for personalized conservative treatment of idiopathic scoliosis (IS) highlights the need for objective and continuous monitoring of corrective forces during brace therapy. This study aims to evaluate the feasibility and clinical relevance of a smart orthopedic brace equipped with integrated force sensors for long-term biomechanical assessment. Three female patients with different types of idiopathic scoliosis were treated using a custom-designed thoracolumbosacral orthosis incorporating four flexible pressure sensors, enabling real-time and long-term recording of corrective forces at key anatomical locations. Sensor data were analyzed in relation to brace-wearing adherence, patient activity, and radiological outcomes assessed using Cobb angle measurements. The results demonstrated substantial variability in force distribution and wearing patterns among patients, which was associated with differences in treatment effectiveness. Higher and more stable corrective forces near curve apices were generally accompanied by improved radiological outcomes, whereas irregular brace use and uneven pressure distribution limited therapeutic effects. Long-term monitoring enabled identification of insufficient correction zones and adherence issues. In conclusion, the proposed sensor-based orthotic system provides clinically relevant information on force distribution and brace use, supporting individualized therapy optimization. These findings indicate that smart braces can enhance clinical decision-making and contribute to more effective and personalized scoliosis management. Full article
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21 pages, 1305 KB  
Systematic Review
Effectiveness of Mobile Health Application-Based Interventions for Fall Prevention in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
by Saad M. Bindawas, Vishal Vennu, Maha Almarwani, Hussam M. Alsaleh and Saad M. Alsaad
Sensors 2026, 26(3), 864; https://doi.org/10.3390/s26030864 - 28 Jan 2026
Cited by 1 | Viewed by 1026
Abstract
Falls are a leading cause of morbidity and loss of independence among community-dwelling older adults. Mobile health (mHealth) application (app)-based interventions have emerged as a scalable approach to fall prevention. However, evidence from individual trials remains fragmented, underscoring the need for a comprehensive [...] Read more.
Falls are a leading cause of morbidity and loss of independence among community-dwelling older adults. Mobile health (mHealth) application (app)-based interventions have emerged as a scalable approach to fall prevention. However, evidence from individual trials remains fragmented, underscoring the need for a comprehensive quantitative synthesis. This systematic review and meta-analysis examined whether mHealth app-based interventions reduce fall incidence and improve fall-related risk factors. A systematic search of PubMed, EMBASE, CENTRAL, and Web of Science identified randomized controlled trials meeting predefined eligibility criteria. Nine trials comprising 3437 participants were included, with dual-independent data extraction, quality appraisal, and assessment of evidence certainty. Compared with usual care or control conditions, mHealth app-based interventions reduced fall risk by 11% over 12 months (risk ratio 0.89, 95% CI 0.81–0.98), corresponding to an absolute risk reduction of 6.6%. The pooled reduction in fall rate, however, did not reach statistical significance. Secondary analyses showed moderate improvements in balance, strength, and mobility, a significant decrease in fear of falling, and no serious adverse events. Overall, mHealth app-based interventions provide modest but meaningful benefits and may complement comprehensive fall-prevention strategies for older adults. Full article
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