Abstract-As the population ages, smart home technology and applications are expected to support older adults to age in place and reduce the associated economic and societal burden. This paper describes a study where the relationship between ambient sensors, permanently deployed as part of smart aware apartments, and clinically validated health questionnaires is investigated. 27 sets of ambient data were taken from a 28 day block from 13 participants all of whom were over 60 years old. Features derived from ambient sensor data were found to be significantly correlated to measures of anxiety, sleep quality, depression, loneliness, cognition, quality of life and independent living skills (IADL). Subsequently, linear discriminant analysis was shown to predict participants suffering from increased anxiety and loneliness with a high accuracy (≥70%). While the number of participants is small, this study reports that objective ambient features may be used to infer clinically validated health metrics. Such findings may be used to inform interventions for active and healthy ageing.

Sleep problems have been shown to have significant negative impact on health. As such it is important to examine night time behaviour to objectively determine when sleep disturbances arise. Due to the large night-tonight variability in sleep quality for older adults, it is important to objectively measure behaviour over a significant period to establish trends or changes in patterns of sleep. In this paper we present a means of ambiently monitoring sleep through the use of sensors installed in each of sixteen independent living apartments. We investigate the effect of time outside the home and movement within the home on sleep. These measures are validated against comparative measures from two actigraph datasets. The first consisting of five adults, two of whom are healthy subjects and the other three adults have previously fallen, gathered over a period of between two and four nights. The second consisting of three older adults recorded over seven nights in their own homes. Results relating time outside the home and movement within the home to sleep are presented for three individuals spanning a period of between 630 and 650 days.

With an ageing population and the constant need towards improving the quality of life for older people in our society, there comes an urgent challenge to support people where they live in an environment that adapts to their needs as they age. While much research on ubiquitous sensor systems and telehealth devices focuses on this need, many of these solutions operate at less than full capacity, and with little scope at present to assess everyday aspects of wellbeing. They focus on detecting sudden critical physiological and behavioural changes and offer few mechanisms to support preventative actions. The challenge of predicting changes and prompting positive preventative intervention measures, aiding the avoidance of severe physical or mental harm, has not adequately been addressed. This paper discusses our experiences of designing, deploying and testing an integrated home-based ambient assisted living (AAL) system for older adults, consisting of ambient monitoring, behaviour recognition and feedback to support self-management of wellness, in addition to providing feedback on home security and home energy. It offers a complete system overview of an AAL solution in smart environments and discusses our lessons learned with the goal of assisting other researchers in the field in designing and deploying similar environments.

Sleep problems have been shown to have significant negative impact on health. As such it is important to examine night time behaviour to objectively determine when sleep disturbances arise. Due to the large night-to-night variability in sleep quality for older adults, it is important to objectively measure behaviour over a significant period to establish trends or changes in patterns of sleep. In this paper we present a means of ambiently monitoring sleep through the use of sensors installed in each of sixteen independent living apartments. We investigate the effect of time outside the home and movement within the home on sleep. These measures are validated against comparative measures from two actigraph datasets. The first consisting of five adults, two of whom are healthy subjects and the other three adults have previously fallen, gathered over a period of between two and four nights. The second consisting of three older adults recorded over seven nights in their own homes. Results relating time outside the home and movement within the home to sleep are presented for three individuals spanning a period of between 630 and 650 days.

One of the challenges of an ageing population is the impact this has on healthcare systems, as living longer can potentially result in higher levels of frailty, chronic disease, dementia and other age-related illnesses. In turn, this may result in higher numbers of hospitalisations and longer hospital stays. Thus, understanding how to support safe and timely discharge of older adults from hospital to their home, and support a return to independence, is critical. Monitoring technology can play an important role in this. However, it is necessary to understand the key role of technology in transitional care as well as the facilitators and barriers to integrating such technology into practice. This paper explores these issues, by presenting a study that uses remote monitoring technology to support older adult patients transitioning from hospital to home. We present findings from evaluations with a range of healthcare professionals on the potential uses of such technology to support transitioning. We also highlight potential barriers and facilitators to integration within health systems.