Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.
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http://dx.doi.org/10.3390/s22114254 | DOI Listing |
BMJ Open
December 2024
School of Psychology, Wenzhou-Kean University, China, Wenzhou, Zhejiang, China.
Introduction: End-of-life care is essential for older adults aged ≥60, particularly those residing in long-term care facilities, such as nursing homes, which are known for their home-like environments compared with hospitals. Due to potential limitations in medical resources, collaboration with external healthcare providers is crucial to ensure comprehensive services within these settings. Previous studies have primarily focused on team-based models for end-of-life care in hospitals and home-based settings.
View Article and Find Full Text PDFSci Rep
January 2025
Media Technology and Interaction Design, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Lindstedtsv. 3-5, Stockholm, 100 44, Sweden.
Energy poverty affects 550,000 homes in the Netherlands yet policy interventions to alleviate this issue are rare. Therefore, we test two energy coaching interventions in Amsterdam: a static information group (n = 67) which received energy efficient products and one energy-use report, and a smart information group (n = 50), which also had a display providing real-time feedback on energy-use. Results across both groups, show a 75% success rate for alleviating energy poverty.
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January 2025
Center for Health Technology and Services Research (CINTESIS@RISE), School of Health Sciences, University of Aveiro, Aveiro, Portugal.
This study describes the process of designing and developing the user interface of a digital solution aiming to promote physical and cognitive training (DanceMove) and testing for its usability by community-dwelling older adults. This study is subdivided into four phases: (i) concept and ideation, (ii) design and development of the prototype, (iii) testing of the functional mock-ups, and (iv) testing of the prototype in the laboratory and in the real context of use. Through the different phases of the study technological and healthcare professionals and users were involved.
View Article and Find Full Text PDFJMIR AI
January 2025
Faculty of Social Science, Ruhr University Bochum, Bochum, Germany.
Background: Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.
View Article and Find Full Text PDFSensors (Basel)
January 2025
University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, Croatia.
The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user's preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets.
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