X-BCD: Explainable Sensor-Based Behavioral Change Detection in Smart Home Environments

TL;DR

X-BCD is an explainable, unsupervised framework that detects and characterizes behavioral changes in smart home sensor data, aiding clinical monitoring of cognitive decline.

Contribution

It introduces a novel method combining change point detection and cluster tracking to interpret activity pattern evolution in smart homes.

Findings

Produces interpretable descriptions of behavioral change.

Supports clinical interpretation with natural-language explanations.

Validated on longitudinal data from MCI patients.

Abstract

Behavioral changes in daily life activities at home can be digital markers of cognitive decline. However, such changes are difficult to assess through sporadic clinical visits and remain challenging to interpret from continuous in-home sensing data. Extensive work has been done in the ubiquitous computing area on recognizing activities in smart homes, but only limited efforts have focused on analysing the evolution of patterns of activities, hence identifying behavior changes. In particular, understanding how daily habits and routines evolve and reorganize (e.g., simplification, fragmentation) is still an open challenge for clinical monitoring and decision support. In this paper, we present X-BCD, an explainable, unsupervised framework for detecting and characterizing changes in activity routines from multimodal smart home sensor data, combining change point detection and cluster evolution tracking. To support clinical interpretation, detected changes in routines are transformed into natural-language explanations grounded in interpretable features. Our preliminary evaluation on longitudinal data from real MCI patients shows that X-BCD produces interpretable descriptions of behavioral change, as supported by cohort-level comparisons, expert assessment, and parameter sensitivity analysis.