Floor Plan-Agnostic Detection of Gait Speed Drifts Using Ambient Sensors

TL;DR

This paper introduces a novel, floor plan-agnostic ambient sensor-based method for detecting gait speed drifts in older adults, enabling scalable and privacy-preserving in-home health monitoring.

Contribution

It presents a new approach that detects gait speed changes without needing home floor plans, using sensor transition analysis and statistical testing.

Findings

Performance comparable or superior to floor plan-dependent methods.

Effective detection of gait speed drifts across various home layouts.

Demonstrates feasibility for scalable, cost-effective health monitoring.

Abstract

Gait speed is a vital health indicator for older adults, as changes in gait speed can reflect physiological and functional decline. Ambient sensors offer a promising, privacy-preserving solution for continuous in-home monitoring of gait speed; although it is often limited by methods requiring a home floor plan, which is frequently unfeasible. This paper proposes a novel, floor plan-agnostic method to detect gait speed drifts using only sparse ambient sensors. Our approach identifies informative sensor-to-sensor transitions and analyses fluctuations in their duration. For each sequence a non-parametric statistical test detects changes between a recent period and an initial baseline; and daily test results are aggregated to provide a robust drift detection response. We evaluate our method on a simulated dataset across four different home layouts, showing performance comparable to, and in some cases exceeding, a state-of-the-art baseline that requires floor plan information. This work demonstrates a feasible approach for scalable, cost effective gait drift detection monitoring, providing a foundation for future validation in complex real-world environments.