Toward real-time in-home activity recognition using indoor positioning sensor and power meters

Eri Nakagawa, K. Moriya, H. Suwa, Manato Fujimoto, Yutaka Arakawa, K. Yasumoto
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引用次数: 18

Abstract

Automatic recognition of activities of daily living (ADL) can be applied to realize services to support user life such as elderly monitoring, energy-saving home appliance control, and health support. In particular, “real-time” ADL recognition is essential to realize such a service that the system needs to know the user's current activity. There are many studies on ADL recognition. However, none of these studies address all of the following problems: (1) privacy intrusion due to the utilization of high privacy-invasive devices such as cameras and microphones; (2) limited number of recognizable activities; (3) low recognition accuracy; (4) high deployment and maintenance costs due to many sensors used; and (5) long recognition time. In our prior work, we proposed a system which solves the problems (1)– (4) to some extent by using user's position data and power consumption data of home electric appliances. In this paper, aiming to solve all the above problems including (5), we propose a new system by extending our prior work. To realize “real-time” ADL recognition while keeping good recognition accuracy, we developed new power meters with higher sensing frequency and introduced new techniques such as adding new features, selecting the best subset of the features, and selecting the best training dataset used for machine learning. We collected the sensor data in our smart home facility for 11 days, and applied the proposed method to these sensor data. As a result, the proposed method achieved accuracy of 79.393% in recognizing 10 types of ADLs.
利用室内定位传感器和电表实现实时的家庭活动识别
应用日常生活活动自动识别(ADL),实现老年人监控、家电节能控制、健康支持等用户生活支持服务。特别是,“实时”ADL识别对于实现系统需要知道用户当前活动的服务至关重要。关于ADL识别的研究很多。然而,这些研究都没有解决以下所有问题:(1)由于使用相机和麦克风等高度侵犯隐私的设备而导致的隐私侵犯;(2)可识别的活动数量有限;(3)识别精度低;(4)传感器数量多,部署和维护成本高;(5)识别时间长。在之前的工作中,我们提出了一个系统,利用用户的位置数据和家用电器的功耗数据,在一定程度上解决了问题(1)-(4)。本文针对上述所有问题,包括(5),我们在原有工作的基础上提出了一个新的系统。为了在保持良好识别精度的同时实现“实时”ADL识别,我们开发了具有更高传感频率的新型功率计,并引入了添加新特征、选择特征的最佳子集、选择用于机器学习的最佳训练数据集等新技术。我们在智能家居设施中收集了11天的传感器数据,并将所提出的方法应用于这些传感器数据。结果表明,该方法对10种adl的识别准确率达到79.393%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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