Investigating an Intelligent System to Monitor \& Explain Abnormal Activity Patterns of Older Adults

TL;DR

This paper presents an intelligent monitoring system for older adults that uses sensors and machine learning to detect abnormal activities, providing explanations and facilitating communication with caregivers to improve care and intervention.

Contribution

The work introduces a novel prototype system that combines activity monitoring, explainability, and interactive dialogue to enhance older adult care technology.

Findings

Caregivers and older adults valued personalized, proactive communication features.

The system effectively detects abnormal activity patterns using machine learning.

Participants appreciated the system's ability to control information sharing.

Abstract

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.