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Easy-to-Deploy Living Activity Sensing System and Data Collection in General Homes

机译：易于部署的一般家庭生活活动感应系统和数据收集

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摘要

Emergence of smart appliances and high performance IoT devices is promoting studies on more functional and intelligent home services using these devices. Especially, in developed countries including Japan with aging population and declining birthrate, it is urgent to develop technologies to monitor living situations of residents including elderly persons and improve their quality of life (QoL) through home services based on the activity recognition technology. However, activity recognition systems in general require many typesumber of sensors and hence they are difficult to deploy and operate. In this paper, we propose a system consisting of low-cost and easy-to-deploy sensors based on energy harvesting that can collect data of resident's activities of daily living (ADL) for months without maintenance. The system was deployed in 10 homes of senior citizens where we collected ADL data for two months each. We also estimated the ADLs from the collected data by using long short-term memory (LSTM), a deep learning model. As a result, ADLs could be estimated at high recall rate of 82.4% on average and hence we found that the proposed system has high applicability to actual services.
机译：智能设备和高性能物联网设备的出现正在促进对使用这些设备的更多功能性和智能家庭服务的研究。特别是在人口老龄化，出生率下降的日本等发达国家，迫切需要开发基于活动识别技术的家庭服务，以监测包括老年人在内的居民的生活状况并改善其生活质量（QoL）的技术。然而，活动识别系统通常需要许多类型/数量的传感器，因此它们难以部署和操作。在本文中，我们提出了一个由低成本和易于部署的传感器组成的系统，该传感器基于能量收集，可以在不维护的情况下收集居民几个月的日常生活活动（ADL）数据。该系统已部署在10个老年人住宅中，我们在每个家庭中收集了两个月的ADL数据。我们还通过使用深度学习模型长短期记忆（LSTM）从收集的数据中估算了ADL。结果，ADL可以平均以82.4％的高召回率进行估算，因此我们发现所提出的系统对实际服务具有很高的适用性。

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