SBIR Phase I: Free Wireless Presence Detection for the Creation of Automated Micro-Zones in a Home

这个小型企业创新研究第一阶段项目将研究一个无线人类存在检测（WHPD）系统，该系统利用现有的元数据使设备-在短期内，由短距离无线网络控制的通风口-能够自行编程并响应用户习惯和环境的变化，而无需添加任何新硬件。 当用户不太可能在场时，可以建立预测性时间表的通气口可以关闭。例如，人们晚上睡觉时很少使用厨房或书房。由于智能关闭这些通风口已被证明可以节省14%至30%的供暖、通风和空调（HVAC）系统的运行时间，因此自动关闭通风口的方法将节省资金、能源并提高用户舒适度。 根据2009年美国人口普查，有9700万美国家庭拥有中央暖通空调系统，61%的美国家庭拥有家庭Wi-Fi网络。平均有四个房间在不同时间过度空调，至少有1.6亿个房间处于次优温度。 根据这些数字，以及这种设备低于100美元的零售价，美国市场规模约为130亿美元。该项目的智力价值将取决于将现有的无线元数据转化为有效的无线人体存在检测（WHPD）的能力，作为一种能够增强家庭/商业/工业自动化中已经使用的许多不同技术的使能技术。 当前的存在检测方法通常需要运动检测来起作用。存在通过热或可见光形状（对象识别）来识别人的存在检测器，但是由于成本和复杂性，这些设备不适合消费者应用。该项目将产生一种算法，可以通过接收信号强度指示器（RSSI）或任何其他不需要专门硬件的方法可靠或半可靠地检测存在。无线存在检测器的更广泛的影响/商业潜力也使更多的技术成为可能。例如，飞利浦Hue灯泡是其自己的ZigBee供电网络上的设备。当与拟议的WHPD系统集成时，Hue可以用作运动感应灯，当房间里没有人时自动关闭。 同样，任何连接的恒温器都可以成为学习恒温器，使用该系统提供的WHPD数据作为提高家庭供暖和制冷效率的一种方式。

This Small Business Innovation Research phase I project will investigate a wireless human presence detection (WHPD) system that makes use of extant metadata to enable devices - in the near term, vents controlled by short-range wireless networks - to program themselves and respond to changes in users' habits and environment without the addition of any new hardware. A vent that can build a predictive schedule can close when a user is not likely to be present. For example, people rarely use their kitchen or den when sleeping at night. Since closing these vents intelligently has been shown to save between 14% and 30% of a heating, ventilation, and air conditioning (HVAC) system's run-time, a way to automatically close them will save money, energy, and improve user comfort. Per the 2009 U.S. Census, there are 97 million American homes with a central HVAC system and 61% of all American households have a home Wi-Fi network. With an average of four rooms over-conditioned at various times, there are at least 160 million rooms at suboptimal temperatures. Based on these figures, and a sub $100 retail price for such a device, the U.S. market size is roughly $13 billion. The intellectual merit of this project will hinge on the capability to turn extant wireless metadata into effective wireless human presence detection (WHPD) as an enabling technology capable of enhancing many different technologies already used in home/commercial/industrial automation. Current presence-detecting methodologies usually require motion detection to function. There are presence detectors that identify people by their thermal, or visual light shapes (object recognition), but these devices are unfit for consumer applications due to cost and complexity. This project will result in an algorithm to detect presence reliably, or semi-reliably, from received signal strength indicator (RSSI) or any other method that does not require specialized hardware. The broader impact/commercial potential of a wireless presence detector enables many more technologies as well. For instance, the Philips Hue light bulb is a device on its own ZigBee powered network. When integrated with the proposed WHPD system, the Hue could function as a motion sensing light, turning itself off when no one is in the room. Similarly, any connected thermostat could become a learning thermostat, using the WHPD data supplied by this system as a way to improve home heating and cooling efficiency.