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

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 43; no. 8; pp. 2809 - 2821
• Main Authors: Wang, Tinghui, Cook, Diane J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Building automation; Data models; Environment Design; Equipment Design; Floorplans; Hidden Markov models; Humans; Layout; Layouts; Monitoring; Monitoring, Ambulatory; multi-resident tracking; multi-target Bayes filter; Pattern Recognition, Automated; sensor networks; Sensors; Smart buildings; Smart home; Smart homes; Smart houses; Structural health monitoring; time series; Tracking
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2020.2973571

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.