Toward unsupervised multiresident tracking in ambient assisted living: methods and performance metrics

Aging is a global challenge facing our society in the next few decades. Ambient assisted living (AAL) is a promising technology that helps people stay active, socially connected, and independent into older age. Even though ambient binary sensors, such as passive infrared (PIR) motion sensors, offer a low cost, easy to deploy, and less intrusive solution to constructing a smart environment, the limited ability of coping with multiple residents hinders the wide adoption of the AAL technology. In this work, we present three multiresident tracking algorithms, nearest neighbor with sensor graph (NN-SG), global nearest neighbor with sensor graph (GNN-SG), and multiresident tracking with sensor vectorization (sMRT), to solve the data association problem between the ambient sensor events and residents in the smart environment. We also introduce new performance metrics to evaluate the success of alternative approaches to multiresident tracking in smart homes. We evaluate all the algorithms with a recent smart home dataset recorded in real-life settings. Among the three algorithms, NN-SG and GNN-SG require sensor location and floor plan of the environment to derive the sensor graph, while sMRT does not require such information and relies solely on the unannotated sensor data. As an initiative of the unsupervised resident tracking solution, sMRT prompts additional research opportunities in multiresident tracking to improve the adoption of AAL technology in our daily life.