Nonparametric Activity Recognition System in Smart Homes Based on Heterogeneous Sensor Data

Abstract

Throughout the course of life, there is time when we live independently in our house without anyone to look after each other. In order to support these people and ensure their safety with limited medical resources and human labors, it is important to constantly monitor one’s activity of daily living (ADL). Therefore, we propose an activity recognition (AR) system for people living independently in smart homes to achieve the concept of “aging in place.” The AR model adopted by the proposed system is powerful to recognize meaningful ADL by integrating heterogeneous data from both ambient and on-body sensors. Moreover, this proposed system adopts a nonparametric approach, which requires much fewer efforts from humans. The average AR precision and recall rates of this proposed system are up to 98.7% and 99.0%, which indicates its feasibility of deployment in a real-life home environment for monitoring users’ ADL with promising performance and thus helps realize “aging in place.” Note to Practitioners —With the advance of Internet of Things technologies, it is convenient to collect data about ADL in a smart home environment via ambient sensors and on-body sensors. This proposed system integrates data from these two heterogeneous sensors and discovers potential activities automatically without user labeling or parameter setting. By reducing the above-mentioned user efforts, it is more suitable for users and thus greatly helps realize the concept of “aging in place.”