Abstract: A smart-home device may include a main power rail that provides power to components of the smart-home device; an integrator coupled to the main power rail that stores energy on an energy-storage device, where the energy stored on the energy-storage device is representative of an amount an amount of power provided to the smart-home device through the main power rail during an integration cycle of the integrator; and a counter that stores a number of integration cycles performed by the integrator during a time interval, where a total amount of power provided to the smart-home device through the main power rail during the time interval is represented by: (1) the number of integration cycles performed by the integrator during the time interval; and (2) the energy stored on the energy-storage device.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A smart-home device may include a temperature sensor, energy-consuming subsystems, and processors programmed to receive a temperature measurement from the temperature sensor for an ambient environment surrounding the temperature sensor; receive inputs from the energy-consuming subsystems that indicate power-consuming activities of the energy-consuming subsystems; providing the inputs from the energy-consuming subsystems to a model that is trained to calculate an effect of the power-consuming activity of the energy-consuming subsystems on the temperature measurement from the temperature sensor; and calculating an estimate of the temperature of the ambient environment by compensating the temperature measurement from the temperature sensor with using the effect of the power-consuming activity of the energy-consuming subsystems.

Abstract: A smart-home device may include a main power rail that provides power to components of the smart-home device; an integrator coupled to the main power rail that stores energy on an energy-storage device, where the energy stored on the energy-storage device is representative of an amount an amount of power provided to the smart-home device through the main power rail during an integration cycle of the integrator; and a counter that stores a number of integration cycles performed by the integrator during a time interval, where a total amount of power provided to the smart-home device through the main power rail during the time interval is represented by: (1) the number of integration cycles performed by the integrator during the time interval; and (2) the energy stored on the energy-storage device.

Abstract: A smart-home device may include a temperature sensor, energy-consuming subsystems, and processors programmed to receive a temperature measurement from the temperature sensor for an ambient environment surrounding the temperature sensor; receive inputs from the energy-consuming subsystems that indicate power-consuming activities of the energy-consuming subsystems; providing the inputs from the energy-consuming subsystems to a model that is trained to calculate an effect of the power-consuming activity of the energy-consuming subsystems on the temperature measurement from the temperature sensor; and calculating an estimate of the temperature of the ambient environment by compensating the temperature measurement from the temperature sensor with using the effect of the power-consuming activity of the energy-consuming subsystems.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Abstract: Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Abstract: A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.