Older People’s Attitudes and Perspectives of Welfare Technology in Norway.

Background: In Norway, as in other European countries, the ageing population is increasing rapidly. Governments seek to enable older people stay in their homes for as long as possible, and welfare technology (WT) has been proposed as a possible solution. Human behaviour modelling (HBM) is a welfare technology that identifies an individual’s behaviour patterns and detects abnormal behaviours, including falls and early signs of dementia. However, the successful development of HBM WT requires the consideration of the older people’s attitudes on this. Aim: The present study sought to explore attitudes and perspectives about welfare technology among older people living alone in Norway. Methods: We used an exploratory, qualitative approach in which semi-structured, in-depth interviews were conducted with five women and four men between the ages of 79 and 91. The interviews were analysed using qualitative content analysis. Results: Two categories and four subcategories were identified: 1) preferences and concerns of welfare technology (i) feeling confident-proactive approach of future technology, (ii) concerns and dilemmas, and 2) reflections of today and tomorrow- awareness of own health (i) feeling healthy, independent, self-sufficient and safe, (ii) facing own ageing- preparedness on unpredictable scenarios. The main theme, welfare technology - a valuable addition to tomorrow’s homes, represents how the participants held positive and proactive attitudes towards the use of WT in their homes. Conclusion: Participants trusted the Norwegian healthcare system and did not rely on their families for care. Independence, autonomy, and feeling safe were essential for all participants, and most participants regarded welfare technology as empowering them to remain in their homes for as long as possible. Participants already confidently used various technologies in their daily lives. Surprisingly, they expressed no concerns about privacy, but some mention concerns about loss of autonomy and dignity. We conclude that a person-centred approach to integrating new WT is necessarypublishedVersio

Welfare Technology, Healthcare, and Behaviour Modelling – An Analysis

Welfare technology is a growing area of research due to the increase in the ageing population. In Nordic countries, public authorities are the primary healthcare providers. Therefore, there is significant of investment to help older people to live as long as they wish at their own home. At the University of South-Eastern Norway, a current project on welfare technology is being developed for this purpose, with a focus on human behaviour modelling. Through the research, gaps were found between the technology and the healthcare aspect of it. Consequently, difficulties for the consumer (the older people) arise. This article presents an analysis of connection and gaps between technology and the healthcare area of welfare technology for the ageing

Decision Trees for Human Activity Recognition in Smart House Environments

Human activity recognition in smart house environments is the task of automatic recognition of physical activities of a person to build a safe environment for older adults or any person in their daily life. The aim of this work is to develop a model that can recognize abnormal activities for assisting people living alone in a smart house environment. The idea is based on the assumption that people tend to follow a specific pattern of activities in their daily life. An open source database is used to train the decision trees classifier algorithm. Training and testing of the algorithm is performed using MATLAB. The results show an accuracy rate of 88.02% in the activity detection task.Decision Trees for Human Activity Recognition in Smart House EnvironmentspublishedVersio

Decision Trees for Human Activity Recognition in Smart House Environments

Human activity recognition in smart house environments is the task of automatic recognition of physical activities of a person to build a safe environment for older adults or any person in their daily life. The aim of this work is to develop a model that can recognize abnormal activities for assisting people living alone in a smart house environment. The idea is based on the assumption that people tend to follow a specific pattern of activities in their daily life. An open source database is used to train the decision trees classifier algorithm. Training and testing of the algorithm is performed using MATLAB. The results show an accuracy rate of 88.02% in the activity detection task

Human behaviour modelling for welfare technology using hidden Markov models

Human behaviour modelling for welfare technology is the task of recognizing a person's behaviour patterns in order to construct a safe environment for that person. It is useful in building environments for older adults or to help any person in his or her daily life. The aim of this study is to model the behaviour of a person living in a smart house environment in order to detect abnormal behaviour and assist the person if help is needed. Hidden Markov models, location of the person in the house, posture of the person, and time frame rules are implemented using a real-world, open-source dataset for training and testing. The proposed model presented in this study models the normal behaviour of a person and detects anomalies in the usual pattern. The model shows good results in the identification of abnormal behaviour when tested

A review of Smart House Analysis Methods for Assisting Older People Living Alone

Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported