Can Appliances Understand the Behavior of Elderly Via Machine Learning? A Feasibility Study

• Published in: IEEE internet of things journal Vol. 8; no. 10; pp. 8343 - 8355
• Main Authors: Qian, Kun, Koike, Tomoya, Yoshiuchi, Kazuhiro, Schuller, Bjorn W., Yamamoto, Yoshiharu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ageing society; Aging; Artificial intelligence; context awareness; Feasibility studies; Household appliances; Internet of Things; Internet of Things (IoT); Machine learning; machine learning (ML); Monitoring; Older people; Refrigerators; Senior citizens; smart appliances; Smart buildings
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3045009

Over the last half decade, fast development of the Internet of Things and machine learning (ML) made it feasible to leverage the power of artificial intelligence to facilitate a variety of intelligent systems in smart home. Nevertheless, the studies on designing specific computing technologies for helping elderly to enjoy a comfortable, convenient, and independent daily life are extremely limited. On the one hand, there are increasingly growing demands from the ageing society to implement the cutting edge technology enabling a better life quality for the elderly. On the other hand, there is still a lack on fundamental investigations, applicable infrastructures, and advanced data-driven frameworks. To this end, we propose a novel machine framework for analyzing the daily life behavior of elderly-all in this study are living alone-by the data collected from their home appliances, i.e., television and refrigerator. First, the interevent intervals for the use of the appliances collected in one month from 76 elderly are the raw data to describe the behaviors. Then, three ML paradigms are investigated and compared, which include "classic" ML methods and the state-of-the-art deep learning approaches. Finally, we indicate the current findings and limitations in this feasibility study. Experimental results demonstrate that, our proposed method can reach performance peak at an unweighted average recall of 58.7% (chance level: 50.0%) in a subject-independent test for classifying symptom/nonsymptom days.