Investigating an Intelligent System to Monitor \& Explain Abnormal Activity Patterns of Older Adults
This addresses the challenge of adopting older adult care technologies by involving caregivers and older adults in design, though it appears incremental in applying existing methods to this domain.
The researchers developed a system to monitor and explain abnormal activity patterns in older adults using motion sensors and machine learning, with qualitative studies showing appreciation for faster, personalized service and controlled information sharing through interactive dialogue.
Despite the growing potential of older adult care technologies, the adoption of these technologies remains challenging. In this work, we conducted a focus-group session with family caregivers to scope designs of the older adult care technology. We then developed a high-fidelity prototype and conducted its qualitative study with professional caregivers and older adults to understand their perspectives on the system functionalities. This system monitors abnormal activity patterns of older adults using wireless motion sensors and machine learning models and supports interactive dialogue responses to explain abnormal activity patterns of older adults to caregivers and allow older adults proactively sharing their status with caregivers for an adequate intervention. Both older adults and professional caregivers appreciated that our system can provide a faster, personalized service while proactively controlling what information is to be shared through interactive dialogue responses. We further discuss other considerations to realize older adult technology in practice.