Abstract:
A system and method for automatic path light control based on a detected size and classification of motion around the device using passive infrared (PIR) sensor technologies and distributed classification algorithms, and on detected light levels in and around the path area using ambient light sensor (ALS) technologies. By using such sensor data, the path light does not need to be maintained at a fixed value, which may be inadequate or inefficient at times, nor require constant user adjustments. Implementations of the disclosed subject matter enable automatic path light control that can be dynamic and automatically adjusted to fit the environment, the current user characteristics and the current user movements through the environment.
Abstract:
In an implementation of the disclosed subject matter, a device may emit a first emission sequence of infrared radiation at a subject, and capture a first reflected sequence of infrared radiation reflected from the subject. The first emission sequence may be compared to the first reflected sequence, and, based on the comparison, a sequence of variations may be determined. The sequence of variations may be compared to signal pattern stored in a sleep profile for the subject. The subject may be determined to have exhibited sleep behavior based on the comparison of the sequence of variations to the signal pattern stored in the sleep profile. In response to determining the subject has exhibited sleep behavior, the device may capture a second reflected sequence of radiation reflected from the subject. A breathing rate of the subject and/or a heart rate of the subject may be determined based on the second reflected sequence.
Abstract:
A process classifies objects in a scene. The process receives a captured IR image of a scene taken by a 2-dimensional image sensor array of a camera system while one or more IR illuminators of the camera system are emitting IR light, thereby forming an IR intensity map of the scene with a respective intensity value determined for each pixel of the IR image. The process uses the IR intensity map to identify a plurality of pixels whose corresponding intensity values are within a predefined intensity range, and clusters the identified plurality of pixels into one or more regions that are substantially contiguous. The process determines that a first region of the one or more regions corresponds to a specific material based, at least in part, on the intensity values of the pixels in the first region. The process then stores information in the memory that identifies the first region.
Abstract:
This application discloses a method implemented by an electronic device to detect a signature event (e.g., a baby cry event) associated with an audio feature (e.g., baby sound). The electronic device obtains a classifier model from a remote server. The classifier model is determined according to predetermined capabilities of the electronic device and ambient sound characteristics of the electronic device, and distinguishes the audio feature from a plurality of alternative features and ambient noises. When the electronic device obtains audio data, it splits the audio data to a plurality of sound components each associated with a respective frequency or frequency band and including a series of time windows. The electronic device further extracts a feature vector from the sound components, classifies the extracted feature vector to obtain a probability value according to the classifier model, and detects the signature event based on the probability value.
Abstract:
A particular smart hazard detector may itself function as a guide during a process of installation of the same at an installation location. Additionally, the installation location of the particular smart hazard detector may play a central role in how various settings of the smart hazard detector are defined and adjusted over time.
Abstract:
Systems and techniques are provided for dynamic volume adjustment. A signal including a detected distance to a person may be received from a proximity sensor of a smart home environment. A volume adjustment for a speaker of the smart home environment may be generated based on the detected distance to the person and a sound level associated with the detected distance to the person. The volume of the speaker may be adjusted based on the volume adjustment. A signal from a sensor of the smart home environment indicating that the sensor has been tripped may be received. The proximity sensor may be triggered based on the received signal indicating the sensor has been tripped to detect a distance to the person to generate the detected distance. An alarm may be sounded through the speaker.
Abstract:
Hazard detection systems and methods according to embodiments described herein are operative to enable a user to interface with the hazard detection system by performing a touchless gesture. The touchless gesture can be performed in a vicinity of the hazard detection system without requiring physical access to the hazard detection system. This enables the user to interact with the hazard detection system even if it is out of reach. The hazard detection system can detect gestures and perform an appropriate action responsive to the detected gesture. In one embodiment, the hazard detection system can silence its audible alarm or pre-emptively turn off its audible alarm in response to a detected gesture. Gestures can be detected by processing sensor data to determine whether periodic shapes are detected.
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 a security system are provided, including detecting, by a sensor, a sound event, and selecting, by a processor coupled to the sensor, at least a portion of sound data captured by the sensor that corresponds to at least one sound feature of the detected sound event. The systems and methods include classifying the at least one sound feature into one or more sound categories, and determining, by a processor, based upon a database of home-specific sound data, whether the at least one sound feature is a human-generated sound. A notification can be transmitted to a computing device according to the sound event.
Abstract:
This application discloses a method implemented by an electronic device to detect a signature event (e.g., a baby cry event) associated with an audio feature (e.g., baby sound). The electronic device obtains a classifier model from a remote server. The classifier model is determined according to predetermined capabilities of the electronic device and ambient sound characteristics of the electronic device, and distinguishes the audio feature from a plurality of alternative features and ambient noises. When the electronic device obtains audio data, it splits the audio data to a plurality of sound components each associated with a respective frequency or frequency band and including a series of time windows. The electronic device further extracts a feature vector from the sound components, classifies the extracted feature vector to obtain a probability value according to the classifier model, and detects the signature event based on the probability value.