Analysing the Correlation of Geriatric Assessment Scores and Activity in Smart Homes

TL;DR

This study demonstrates that ambient motion sensor data in smart homes correlates well with traditional geriatric mobility assessments, offering a non-intrusive way to monitor elderly health continuously.

Contribution

It introduces a method to correlate ambient sensor data with established geriatric assessments, enabling unobtrusive health monitoring in smart homes.

Findings

Moderate to strong correlation between sensor activity and assessment scores

Sensor data can predict mobility assessment outcomes

Supports continuous health monitoring for elderly

Abstract

A continuous monitoring of the physical strength and mobility of elderly people is important for maintaining their health and treating diseases at an early stage. However, frequent screenings by physicians are exceeding the logistic capacities. An alternate approach is the automatic and unobtrusive collection of functional measures by ambient sensors. In the current publication, we show the correlation among data of ambient motion sensors and the well-established mobility assessments Short-Physical-Performance-Battery, Tinetti and Timed Up & Go. We use the average number of motion sensor events as activity measure for correlation with the assessment scores. The evaluation on a real-world dataset shows a moderate to strong correlation with the scores of standardised geriatrics physical assessments.