sMRT: Multi-Resident Tracking in Smart Homes With Sensor Vectorization

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.

1 Introduction

Smart homes offer a promising technology that combines sensor networks and artificial intelligence algorithms to improve the living experience and productivity of residents. With the ability to comprehend and predict the daily activities of residents, smart homes can offer context-aware services such as home automation and health monitoring. Home automation services can reduce energy consumption and improve living comfort for residents by anticipating their behavior inside the smart home. In the case of health monitoring, smart homes are capable of detecting behavior patterns that indicate sudden or gradual changes in cognitive, mobility, and physical health states. Because a majority of existing smart home research is limited to single-resident environments, there remains a challenge of how to extend this work to encompass multi-resident scenarios.