Can Appliances Understand the Behaviour of Elderly via Machine Learning? A Feasibility Study
Can Appliances Understand the Behaviour of Elderly via Machine Learning? A Feasibility Study
复制标题
家电能否通过机器学习了解老年人的行为？
DOI：
10.1109/jiot.2020.3045009
复制
发表时间：
2020
影响因子：
10.6
通讯作者：
Yamamoto Yoshiharu
中科院分区：
文献类型：
--
作者：
Qian Kun;Koike Tomoya;Yoshiuchi Kazuhiro;Schuller Bjorn W.;Yamamoto Yoshiharu
在过去的五年里，物联网和机器学习（ML）的快速发展使得利用人工智能的力量来促进智能家居中的各种智能系统成为可能。然而，设计具体的计算技术来帮助老年人享受舒适、方便、独立的日常生活的研究却非常有限。一方面，老龄化社会对先进科技的需求日益增加，使长者的生活质素得以改善。另一方面，在基础研究、适用的基础设施和先进的数据驱动框架方面仍然缺乏。为此，我们提出了一个新的机器框架来分析老年人的日常生活行为-在本研究中都是独居-通过收集他们的家电，即电视和冰箱的数据。首先，收集76名老年人一个月内使用器具的间隔时间作为描述行为的原始数据。然后，研究和比较了三种机器学习范式，包括“经典”机器学习方法和最先进的深度学习方法。最后，我们指出了目前的研究结果和本可行性研究的局限性。实验结果表明，在对症状/非症状日进行分类的受试者独立测试中，我们提出的方法在未加权平均召回率为58.7%（机会水平为50.0%）时达到性能峰值。
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.