iCity Lab researchers clinched the Best Paper Award at the 3rd International Conference on Human Aspects of IT for the Aged Population

19 Jul 2017


Sensor-Driven Detection of Social Isolation in Community-Dwelling Elderly authored by iCity Lab researchers: Dr Nadee GOONAWARDENE, XiaoPing TOH and, Dr Hwee-Pink TAN won the Best Paper Award at the 3rd International Conference on Human Aspects of IT for the Aged Population, held at the Vancouver Convention Centre in Canada.

The selection process was performed by a review committee coordinated by the Chairs of the Affiliated Conference. The Conference is affiliated under HCI International 2017, with 15 distinguished international boards, of 377 members from 42 countries around the world.

Paper Abstract

"Ageing-in-place, the ability to age holistically in the community, is increasingly gaining recognition as a solution to address resource limitations in the elderly care sector. Effective elderly care models require a personalised and all-encompassing approach to caregiving. In this regard, sensor technologies have gained attention as an effective means to monitor the wellbeing of elderly living alone. In this study, we seek to investigate the potential of non-intrusive sensor systems to detect socially isolated community dwelling elderly. Using a mixed method approach, our results showed that sensor-derived features such as going-out behavior, daytime napping and time spent in the living room are associated with different social isolation dimensions. The average time spent outside home is associated with the social loneliness level, social network score and the overall social isolation level of the elderly and the time spent in the living room is positively associated with the emotional loneliness level. Further, elderly who perceived themselves as socially lonely tend to take more naps during the day time. The findings of this study provide implications on how a non-intrusive sensor-based monitoring system comprising of motion-sensors and a door contact sensor can be utilized to detect elderly who are at risk of social isolation."


The full paper is available through SpringerLink.

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Last updated on 25 Sep 2017 .