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Nonintrusive system for assistance and guidance in smart homes based on electrical devices identification

机译：基于电气设备识别的智能家居辅助干预非侵入式系统

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Recently, sensors and actuators have quickly spread throughout our everyday life. These devices are robust, cheap, accessible, connected to the Internet, etc. With the growing needs in terms of human and medical resources to help cognitively-impaired people to remain at home, researchers are investing in new ways to exploit this technology with artificial intelligence, in order to build expert systems to assist the residents in their daily activities. Several systems have been proposed in the last few years, mostly based on binary sensors, cameras and other sensors such as Radio-frequency identification (REID) tags. Cameras are very intrusive, binary sensors (such as movement detectors) give only basic information, and other types of sensors (such as RFID) need complex deployment. In this context, this paper presents a new assistive expert system based on electric device identification to address the problem of guidance and supervision in the performance of activities for people with cognitive disorders living in a smart home. This system is solely based on a single power analyzer placed in the electric panel. We propose an algorithmic approach used to recognize erratic behaviors related to cognitive deficits and provides cues to guide the person in the completion of an ongoing task. This is achieved through load signatures study of appliances represented by three features (active power (P), reactive power (Q) and line-to-neutral), which allows to determine the errors committed by the resident. We implemented this system within a genuine smart-home prototype equipped with household appliances used by the patient during his morning routines. Different multimedia prompting devices (iPad, screen, speakers, etc.) were used. We tested the system with real-case scenarios modeled from former clinical trials, allowing demonstration of accuracy and effectiveness of our system in assisting a cognitively-impaired resident in the completion of daily activities. (C) 2015 Elsevier Ltd. All rights reserved.
机译：最近，传感器和执行器已迅速普及到我们的日常生活中。这些设备功能强大，价格低廉，可访问，可连接到Internet等。随着对人力和医疗资源的需求不断增长，以帮助认知障碍者留在家中，研究人员正在投资开发新方法，以人工方式利用该技术。情报，以建立专家系统来协助居民的日常活动。在最近几年中已经提出了几种系统，这些系统主要基于二进制传感器，摄像机和其他传感器，例如射频识别（REID）标签。相机非常具有侵入性，二进制传感器（例如运动检测器）仅提供基本信息，而其他类型的传感器（例如RFID）则需要复杂的部署。在这种情况下，本文提出了一种基于电子设备识别的新辅助专家系统，以解决居住在智能家居中的认知障碍患者的活动进行中的指导和监督问题。该系统仅基于放置在配电盘中的单个功率分析仪。我们提出了一种算法方法，用于识别与认知缺陷相关的不稳定行为，并提供线索来指导人们完成正在进行的任务。这是通过对由三个功能（有功功率（P），无功功率（Q）和线对中性）代表的设备的负载特征研究来实现的，该功能可以确定居民所犯的错误。我们在一个真正的智能家居原型机中实施了该系统，该原型机配备了患者在早晨例行工作中使用的家用电器。使用了不同的多媒体提示设备（iPad，屏幕，扬声器等）。我们使用从以前的临床试验中模拟出来的实际案例测试了该系统，从而证明了我们的系统在协助有认知障碍的居民完成日常活动中的准确性和有效性。 （C）2015 Elsevier Ltd.保留所有权利。

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