Smart Home is one of the most established applications of the Internet of Things. Almost every equipment we use in our daily life—appliances, electric lights, electrical outlets, heating, and cooling systems-connected to a remotely controllable network, giving the user’s ability to remotely control and monitor the house, save energy without compromising on comfort and ultimately improve the quality of experience of staying in the house. We present a cost-effective system and address a major challenge that the industry faces today-Protocol Compatibility. To address the challenge, we make use of separate gateways/bridges for each network and an open-source home automation framework called OpenHAB, where each bridge links with a single master Wi-Fi gateway, providing a single window of control through an Application or a web interface for an integrated Smart Home. We integrate an elderly health monitoring device-Beehealth with OpenHAB; addressing the paramount need of a portable, accurate, and efficient health monitoring and fall detection device. We present two methods for fall detection, namely: threshold-based and Neural Network-based, with the latter resulting in 94% accuracy for fall detection. We evaluate the Smart Home devices on parameters like syncing time, battery life, recharge time, deployability, and cost.

智能家居是物联网最成熟的应用之一。我们几乎将日常生活中使用的所有设备（电器，电灯，电源插座，供暖和冷却系统）连接到可远程控制的网络，从而使用户能够远程控制和监视房屋，节省能源而又不影响舒适性和最终提高了留在家里的体验质量。我们提出了一个具有成本效益的系统，并解决了该行业今天面临的主要挑战-协议兼容性。为了解决这一挑战，我们为每个网络使用了单独的网关/网桥，并使用了称为OpenHAB的开源家庭自动化框架，其中每个网桥与一个主Wi-Fi网关链接，通过应用程序或Web界面为集成的Smart Home提供单个控制窗口。我们将老人健康监控设备-Beehealth与OpenHAB集成在一起；解决便携式，准确，高效的健康监测和跌倒检测设备的最重要需求。我们提出了两种跌倒检测方法，分别是：基于阈值的方法和基于神经网络的方法，后者导致跌倒检测的准确性达到94％。我们根据同步时间，电池寿命，充电时间，可部署性和成本等参数评估智能家居设备。基于阈值和基于神经网络，后者的跌倒检测准确性达到94％。我们根据同步时间，电池寿命，充电时间，可部署性和成本等参数评估智能家居设备。基于阈值和基于神经网络，后者的跌倒检测准确性达到94％。我们会根据同步时间，电池寿命，充电时间，可部署性和成本等参数评估智能家居设备。