The well-being of people with dementia (PWD) living in long-term care facilities is hindered due to disengagement and social isolation. Animal-like social robots are increasingly used in dementia care as they can provide companionship and engage PWD in meaningful activities. While most previous human-robot interaction (HRI) research studied engagement independent from the context, recent findings indicate that the context of HRI sessions has an impact on user engagement. This study aims to explore the effects of contextual interactions between PWD and a social robot embedded in the augmented responsive environment. Three experimental conditions were compared: reactive context-enhanced robot interaction; dynamic context-enhanced interaction with a static robot; a control condition with only the dynamic context presented. Effectiveness evaluations were performed with 16 participants using four observational rating scales on observed engagement, affective states, and apathy related behaviors. Findings suggested that the higher level of interactivity of a social robot and the interactive contextualized feedback helped capture and maintain users' attention during engagement; however, it did not significantly improve their positive affective states. Additionally, the presence of either a static or a proactive robot reduced apathy-related behaviors by facilitating purposeful activities, thus, motivating behavioral engagement.
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http://dx.doi.org/10.3390/s20133771 | DOI Listing |
J Neurosurg Spine
January 2025
1Department of Spine Surgery, Hospital for Special Surgery, New York.
Objective: When creating minimally invasive spine fusion constructs, accurate pedicle screw fixation is essential for biomechanical strength and avoiding complications arising from delicate surrounding structures. As research continues to analyze how to improve accuracy, long-term patient outcomes based on screw accuracy remain understudied. The objective of this study was to analyze long-term patient outcomes based on screw accuracy.
View Article and Find Full Text PDFPLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFEvol Comput
January 2025
Sorbonne Université, CNRS, ISIR., Paris, 75005, France
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and highperforming solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains'mainly applied to locomotion, where the fitness and the behavior signal are dense. Grasping is a crucial task for manipulation in robotics.
View Article and Find Full Text PDFSci Adv
January 2025
School of Chemical Engineering, Pusan National University, Busan, Republic of Korea.
The development of fibrous actuators with diverse actuation modes is expected to accelerate progress in active textiles, robotics, wearable electronics, and haptics. Despite the advances in responsive polymer-based actuating fibers, the available actuation modes are limited by the exclusive reliance of current technologies on thermotropic contraction along the fiber axis. To address this gap, the present study describes a reversible and spontaneous thermotropic elongation (~30%) in liquid crystal elastomer fibers produced via ultraviolet-assisted melt spinning.
View Article and Find Full Text PDFSci Adv
January 2025
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
Electronic skins endow robots with sensory functions but often lack the multifunctionality of natural skin, such as switchable adhesion. Current smart adhesives based on elastomers have limited adhesion tunability, which hinders their effective use for both carrying heavy loads and performing dexterous manipulations. Here, we report a versatile, one-size-fits-all robotic adhesive skin using shape memory polymers with tunable rubber-to-glass phase transitions.
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