Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient's progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand's mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient-therapist interaction and therapist-therapist data sharing.
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http://dx.doi.org/10.3390/s23073463 | DOI Listing |
Inf Commun Soc
June 2024
Department of Ethics and Political Philosophy and Interdisciplinary Hub for Digitalization and Society, Radboud University Nijmegen, Nijmegen, Netherlands.
The rapid expansion of Big Tech companies into various societal domains (e.g., health, education, and agriculture) over the past decade has led to increasing concerns among governments, regulators, scholars, and civil society.
View Article and Find Full Text PDFGastro Hep Adv
September 2024
Department of Surgery, The University of Auckland, Auckland, New Zealand.
Background And Aims: Gastric Alimetry™ (Alimetry, New Zealand) is a new clinical test for gastroduodenal disorders involving simultaneous body surface gastric electrical mapping and validated symptom profiling. Studies have demonstrated a range of distinct pathophysiological profiles, and a classification scheme is now required. We used Gastric Alimetry spectral and symptom profiles to develop a mechanism-based test classification scheme, then assessed correlations with symptom severity, psychometrics, and quality of life.
View Article and Find Full Text PDFMach Learn Appl
June 2024
McGill University Department of Biostatistics, 805 rue Sherbrooke O, Montréal, H3A 0B9, Quebec, Canada.
In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11633, Saudi Arabia.
The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments.
View Article and Find Full Text PDFTob Prev Cessat
January 2025
Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, United Kingdom.
Introduction: Challenges with designing invitation materials and accessing high risk communities are all factors in encouraging attendance at lung screening. This study focused on ways to improve participation in those potentially eligible for lung screening.
Methods: A total of 50 qualitative interviews and 4 focus groups (n=17) were undertaken with people aged 50-75 years from East Midlands, UK.
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