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Purpose Of Review: While wearable robotics is expanding within clinical settings, particularly for neurological rehabilitation, there is still a lack of consensus on how to effectively assess the performance of these devices. This review focuses on the most common metrics, whose selection and design are crucial for optimizing treatment outcomes and potentially improve the standard care.
Recent Findings: The literature reveals that while wearable robots are equipped with various embedded sensors, most studies still rely on traditional, nontechnological methods for assessment. Recent studies have shown that, although quantitative data from embedded sensors are available (e.g., kinematics), these are underutilized in favor of qualitative assessments. A trend toward integrating automatic assessments from the devices themselves is emerging, with a few notable studies pioneering this approach.
Summary: Our analysis suggests a critical need for developing standardized metrics that leverage the data from embedded sensors in wearable robots. This shift could enhance the accuracy of patient assessments and the effectiveness of rehabilitation strategies, ultimately leading to better patient outcomes in neurological rehabilitation.
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http://dx.doi.org/10.1097/WCO.0000000000001328 | DOI Listing |
J Orthop Res
September 2025
Department of Kinesiology, College of Health Sciences, University of Rhode Island, Kingston, Rhode Island, USA.
Arthroplasty surgery is a common and successful end-stage intervention for advanced osteoarthritis. Yet, postoperative outcomes vary significantly among patients, leading to a plethora of measures and associated measurement approaches to monitor patient outcomes. Traditional approaches rely heavily on patient-reported outcome measures (PROMs), which are widely used, but often lack sensitivity to detect function changes (e.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
Department of Mechanical Engineering, State University of New York at Binghamton, Binghamton, New York 13902, United States.
Soft conductive composites are significant components of soft and wearable electronics. Existing soft conductive composites encounter difficulties in attaining 10% of copper's electrical conductivity while maintaining high stretchability. In this work, a novel "soft conductive junction" concept is introduced to overcome the conductivity-stretchability trade-off.
View Article and Find Full Text PDFJ Multidiscip Healthc
September 2025
Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Background: Falls are a major cause of injury and death among the elderly, highlighting the need for effective and real-time detection systems. Embedded Internet of Health Things (IoHT) technologies integrating sensors, microcontrollers, and communication modules offer continuous monitoring and rapid response. However, the research landscape remains fragmented, and no comprehensive bibliometric review has been conducted.
View Article and Find Full Text PDFNanomicro Lett
September 2025
Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, Republic of Korea.
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian 116620, China.
Achieving both high sensitivity and a wide detection range in flexible pressure sensors poses a challenge due to their inherent trade-off. Although porous structures offer promising solutions, conventional methods (templating, foaming, and freeze-drying) fail to precisely control cavity dimensions, spatial arrangement, and 3D morphology, which are crucial for sensing performance. Here, we propose a scalable fabrication strategy that integrates triply periodic minimal surface (TPMS) geometries─precisely engineered via FDM 3D printing─with ultrasonic impregnation of carbon black (CB) into TPU scaffolds.
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