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    • 2. 发明授权
    • Sensing user input using the body as an antenna
    • 使用身体感应用户输入作为天线
    • US08665210B2
    • 2014-03-04
    • US12975373
    • 2010-12-22
    • Desney S. TanDaniel S. MorrisGabriel A. CohnShwetak N Patel
    • Desney S. TanDaniel S. MorrisGabriel A. CohnShwetak N Patel
    • G09G5/00G06F3/033
    • G06F3/0383G06F3/011G06F3/017G06F3/0346
    • A human input system is described herein that provides an interaction modality that utilizes the human body as an antenna to receive electromagnetic noise that exists in various environments. By observing the properties of the noise picked up by the body, the system can infer human input on and around existing surfaces and objects. Home power lines have been shown to be a relatively good transmitting antenna that creates a particularly noisy environment. The human input system leverages the body as a receiving antenna and electromagnetic noise modulation for gestural interaction. It is possible to robustly recognize touched locations on an uninstrumented home wall using no specialized sensors. The receiving device for which the human body is the antenna can be built into common, widely available electronics, such as mobile phones or other devices the user is likely to commonly carry.
    • 本文描述了一种人机输入系统,其提供利用人体作为天线来接收存在于各种环境中的电磁噪声的相互作用模态。 通过观察身体吸收的噪声的特性,系统可以推断现有表面和物体周围的人类输入。 家用电源线已被证明是相对较好的发射天线,产生特别嘈杂的环境。 人体输入系统利用身体作为接收天线和电磁噪声调制手势互动。 没有专门的传感器可以很好地识别未测量的家庭墙上的触摸位置。 人体是天线的接收装置可以内置在普通的广泛使用的电子设备中,例如用户可能通常携带的移动电话或其他设备。
    • 3. 发明申请
    • Motion Detecting Device, Method of Providing the Same, and Method of Detecting Movement
    • 运动检测装置及其提供方法及检测方法
    • US20100288468A1
    • 2010-11-18
    • US12778771
    • 2010-05-12
    • Shwetak N. PatelMatthew S. ReynoldsGregory D. Abowd
    • Shwetak N. PatelMatthew S. ReynoldsGregory D. Abowd
    • F24F7/00G08B21/00B01D46/42
    • G01L13/00B01D46/10F24F11/30F24F2110/10F24F2120/10F24F2120/14G08B13/20
    • In some embodiments, a motion detecting device is configured to detect whether one or more movement events have occurred. The motion detecting device can include: (a) a processing module configured to run on a computational unit; and (b) a sensing device having: (1) one or more pressure sensors configured to provide two or more pressure measurements; and (2) a transmitter electrically coupled to the one or more pressure sensors and configured to transmit the two or more pressure measurements to the computational unit. The processing module is configured to use the two or more pressure measurements to determine whether the one or more movement events have occurred. The sensing device can be configured to be placed in at least one of ductwork of a heating, ventilation, and air conditioning system or an air handler of the heating, ventilation, and air conditioning system. Other embodiments are disclosed.
    • 在一些实施例中,运动检测装置被配置为检测是否发生了一个或多个运动事件。 运动检测装置可以包括:(a)处理模块,被配置为在计算单元上运行; 和(b)感测装置,其具有:(1)配置成提供两个或更多个压力测量的一个或多个压力传感器; 和(2)电耦合到所述一个或多个压力传感器并被配置为将所述两个或更多个压力测量值传送到所述计算单元的发射器。 处理模块被配置为使用两个或更多个压力测量来确定一个或多个运动事件是否已经发生。 感测装置可以被配置成放置在加热,通风和空调系统的管道系统或加热,通风和空调系统的空气处理器中的至少一个中。 公开了其他实施例。
    • 5. 发明授权
    • Motion detecting method and device
    • 运动检测方法和装置
    • US08886489B2
    • 2014-11-11
    • US12778771
    • 2010-05-12
    • Shwetak N. PatelMatthew S. ReynoldsGregory D. Abowd
    • Shwetak N. PatelMatthew S. ReynoldsGregory D. Abowd
    • G01L7/00F24F11/00G01L13/00G08B13/20B01D46/10
    • G01L13/00B01D46/10F24F11/30F24F2110/10F24F2120/10F24F2120/14G08B13/20
    • In some embodiments, a motion detecting device is configured to detect whether one or more movement events have occurred. The motion detecting device can include: (a) a processing module configured to run on a computational unit; and (b) a sensing device having: (1) one or more pressure sensors configured to provide two or more pressure measurements; and (2) a transmitter electrically coupled to the one or more pressure sensors and configured to transmit the two or more pressure measurements to the computational unit. The processing module is configured to use the two or more pressure measurements to determine whether the one or more movement events have occurred. The sensing device can be configured to be placed in at least one of ductwork of a heating, ventilation, and air conditioning system or an air handler of the heating, ventilation, and air conditioning system. Other embodiments are disclosed.
    • 在一些实施例中,运动检测装置被配置为检测是否发生了一个或多个运动事件。 运动检测装置可以包括:(a)处理模块,被配置为在计算单元上运行; 和(b)感测装置,其具有:(1)配置成提供两个或更多个压力测量的一个或多个压力传感器; 和(2)电耦合到所述一个或多个压力传感器并被配置为将所述两个或更多个压力测量值传送到所述计算单元的发射器。 处理模块被配置为使用两个或更多个压力测量来确定一个或多个运动事件是否已经发生。 感测装置可以被配置成放置在加热,通风和空调系统的管道系统或加热,通风和空调系统的空气处理器中的至少一个中。 公开了其他实施例。
    • 7. 发明申请
    • Detecting Actuation of Electrical Devices Using Electrical Noise Over a Power Line
    • 使用电力线上的电气噪声检测电气设备的动作
    • US20120092142A1
    • 2012-04-19
    • US13335045
    • 2011-12-22
    • Shwetak N. PatelThomas M. RobertsonGregory D. AbowdMatthew S. Reynolds
    • Shwetak N. PatelThomas M. RobertsonGregory D. AbowdMatthew S. Reynolds
    • G05B11/01
    • G01R29/26G01R21/006G05B2219/40408G06F17/00H04B3/544H04B2203/5425H04B2203/5458Y04S20/38
    • Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.
    • 家庭活动感知有各种重要应用,包括医疗保健,娱乐,家庭自动化,能源监测和住院后研究。 用于检测乘员活动的许多现有系统需要大量传感器,侵入性视觉系统或广泛的安装程序。 公开了一种使用单个插入式传感器来检测整个家庭中的各种电气事件的方法。 该传感器检测由电气设备的突然切换产生的住宅电力线路上的电气噪声以及某些设备在运行时产生的噪音。 机器学习技术用于识别电噪声事件,例如打开或关闭特定的光开关,电视机或电炉。 该系统已经过测试,以评估系统性能随着时间的推移和不同类型的房屋。 结果表明,各种电气事件可以从85-90%的准确度学习和分类。
    • 8. 发明授权
    • Detecting actuation of electrical devices using electrical noise over a power line
    • 使用电力线上的电气噪声检测电气设备的启动
    • US08094034B2
    • 2012-01-10
    • US12283869
    • 2008-09-16
    • Shwetak N. PatelThomas M. RobertsonGregory D. AbowdMatthew S. Reynolds
    • Shwetak N. PatelThomas M. RobertsonGregory D. AbowdMatthew S. Reynolds
    • G08B21/00
    • G01R29/26G01R21/006G05B2219/40408G06F17/00H04B3/544H04B2203/5425H04B2203/5458Y04S20/38
    • Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.
    • 家庭活动感知有各种重要应用,包括医疗保健,娱乐,家庭自动化,能源监测和住院后研究。 用于检测乘员活动的许多现有系统需要大量传感器,侵入性视觉系统或广泛的安装程序。 公开了一种使用单个插入式传感器来检测整个家庭中的各种电气事件的方法。 该传感器检测由电气设备的突然切换产生的住宅电力线路上的电气噪声以及某些设备在运行时产生的噪音。 机器学习技术用于识别电噪声事件,例如打开或关闭特定的光开关,电视机或电炉。 该系统已经过测试,以评估系统性能随着时间的推移和不同类型的房屋。 结果表明,各种电气事件可以从85-90%的准确度学习和分类。