Objectives: This work presents an ambient-assisted living application that encourages seniors during nocturnal wandering episodes to return to bed in calm and comfort reassurance.
Methods: Structuring knowledge by designing a software architecture capable of delivering high-level analysis and processing. A senior's home has been upgraded into a smart home enabling the gathering of habits for two weeks and set up for personalized assistance over four weeks. Home automation devices associated with Actigraph monitors and self-reported sleep were used for more accuracy.
Results: The architectural model can be used in ambient-assisted living applications for which data collection is permanent and continuous. Its layered organization facilitates the management of specific and general activities of daily life. The results of the home experience show that the system gave a notification whenever the need arose. On the other hand, it allowed the caregiver to get more information about the lifestyle of the senior.
Conclusions: Future work should focus on providing more services to contextualize assistance. Ontology is used to structure all the ambient knowledge of the smart home. We also plan to do more home experiments.
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http://dx.doi.org/10.1177/2055668319887864 | DOI Listing |
Sensors (Basel)
December 2024
Department of Industrial Design, Guangdong University of Technology, Guangzhou 510006, China.
Research into new solutions for wearable assistive devices for the visually impaired is an important area of assistive technology (AT). This plays a crucial role in improving the functionality and independence of the visually impaired, helping them to participate fully in their daily lives and in various community activities. This study presents a bibliometric analysis of the literature published over the last decade on wearable assistive devices for the visually impaired, retrieved from the Web of Science Core Collection (WoSCC) using CiteSpace, to provide an overview of the current state of research, trends, and hotspots in the field.
View Article and Find Full Text PDFBackground: Ambient artificial intelligence offers promise for improving documentation efficiency and reducing provider burden through clinical note generation. However, challenges persist in workflow integration, compliance, and widespread adoption. This study leveraged a Learning Health System (LHS) framework to align research and operations using a hybrid effectiveness-implementation protocol, embedded as pragmatic trial operations within the electronic health record (EHR).
View Article and Find Full Text PDFBMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFLight Sci Appl
January 2025
State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, 100871, China.
Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans.
View Article and Find Full Text PDFBiosensors (Basel)
December 2024
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518107, China.
The demand for non-invasive, real-time health monitoring has driven advancements in wearable sensors for tracking biomarkers in sweat. Ammonium ions (NH) in sweat serve as indicators of metabolic function, muscle fatigue, and kidney health. Although current ion-selective all-solid-state printed sensors based on nanocomposites typically exhibit good sensitivity (~50 mV/log [NH]), low detection limits (LOD ranging from 10 to 10 M), and wide linearity ranges (from 10 to 10 M), few have reported the stability test results necessary for their integration into commercial products for future practical applications.
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