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Interact J Med Res
January 2025
Department of Nursing Science, Diagnostics in Healthcare and eHealth, Trier University, Trier, Germany.
Background: Psychoeducation positively influences the psychological components of chronic low back pain (CLBP) in conventional treatments. The digitalization of health care has led to the discussion of virtual reality (VR) interventions. However, CLBP treatments in VR have some limitations due to full immersion.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada.
Background: Telehomecare monitoring (TM) in patients with cancer is a complex intervention. Research shows variations in the benefits and challenges TM brings to equitable access to care, the therapeutic relationship, self-management, and practice transformation. Further investigation into these variations factors will improve implementation processes and produce effective outcomes.
View Article and Find Full Text PDFJ Am Acad Dermatol
January 2025
Dermatology Research Institute, Calgary, Alberta, Canada; Division of Dermatology, Department of Medicine, University of Calgary, Calgary, Alberta, Canada; Skin Health & Wellness Centre, Calgary, Alberta, Canada; Division of Dermatology, Department of Medicine, University of Calgary, Calgary, Alberta, Canada; Section of Community Pediatrics, Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada; Section of Pediatric Rheumatology, Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada. Electronic address:
Front Robot AI
January 2025
Aveni AI, Edinburgh, United Kingdom.
There have been significant advances in robotics, conversational AI, and spoken dialogue systems (SDSs) over the past few years, but we still do not find social robots in public spaces such as train stations, shopping malls, or hospital waiting rooms. In this paper, we argue that early-stage collaboration between robot designers and SDS researchers is crucial for creating social robots that can legitimately be used in real-world environments. We draw from our experiences running experiments with social robots, and the surrounding literature, to highlight recurring issues.
View Article and Find Full Text PDFFront Neurorobot
January 2025
Department of Architectural Engineering, Jinhua Polytecnich, Jinhua, Zhejiang, China.
Introduction: Space optimization in architectural planning is a crucial task for maximizing functionality and improving user experience in built environments. Traditional approaches often rely on manual planning or supervised learning techniques, which can be limited by the availability of labeled data and may not adapt well to complex spatial requirements.
Methods: To address these limitations, this paper presents a novel architectural planning robot driven by unsupervised learning for automatic space optimization.
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