A sleep position determination device according to an embodiment of the present disclosure is provided with a receiver that receives a measurement result obtained by measuring a subject using a contactless sensor, an extraction circuit that extracts a respiration signal of the subject from the measurement result, memory that stores reference information related to a level of the respiration signal, and a determination circuit that determines a sleep position of the subject on a basis of a comparison between the level of the respiration signal of the subject and the reference information.

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

The invention relates to a piece of sleeping or reclining furniture, comprising a support element (2) for applying a padding or a mattress and at least one sensor (12) for detecting vibrations, movement, and/or sound. The piece of sleeping or reclining furniture is characterized in that the at least one sensor (12) is arranged at a section (120) of the support element (2) that is decoupled from further sections of the support element (2) and/or of the sleeping or reclining furniture.

The invention relates to a piece of sleeping or reclining furniture, comprising a support element (2) for applying a padding or a mattress and at least one sensor (12) for detecting vibrations, movement, and/or sound. The piece of sleeping or reclining furniture is characterized in that the at least one sensor (12) is arranged at a section (120) of the support element (2) that is decoupled from further sections of the support element (2) and/or of the sleeping or reclining furniture.

A system and method for determining a measurement of anxiety in a vehicle that include receiving sensor data from a plurality of sensors disposed within a plurality of areas of the vehicle. The system and method also include processing the sensor data into metrics associated with a type of measurement. Processing of the sensor data is completed by a neural network. The system and method also include analyzing the processed metrics and determining an anxiety level associated with an occupant of the vehicle. The system and method further include training the neural network based on the anxiety level and at least one driving event that is correlated with the anxiety level.

A system and method for determining a measurement of anxiety in a vehicle that include receiving sensor data from a plurality of sensors disposed within a plurality of areas of the vehicle. The system and method also include processing the sensor data into metrics associated with a type of measurement. Processing of the sensor data is completed by a neural network. The system and method also include analyzing the processed metrics and determining an anxiety level associated with an occupant of the vehicle. The system and method further include training the neural network based on the anxiety level and at least one driving event that is correlated with the anxiety level.

A sensor for motion or gesture sensing may be configured to emit radio frequency signals such as for pulsed range gated sensing. The sensor may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency signals. The received pulses may be processed by a motion channel and/or a gesture channel. The gesture channel may produce signals for further processing for identification of one or more different motion gestures such as by calculating and evaluating features from one or more of the amplitude, phase and frequency of the output signals of the gesture channel. The sensing apparatus may optionally serve as a monitor for evaluating user activities, such as by counting certain activities. The sensor may optionally serve as a user control interface for many different devices by generating control signal(s) based on the identification of one or more different motion gestures.

A sensor for motion or gesture sensing may be configured to emit radio frequency signals such as for pulsed range gated sensing. The sensor may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency signals. The received pulses may be processed by a motion channel and/or a gesture channel. The gesture channel may produce signals for further processing for identification of one or more different motion gestures such as by calculating and evaluating features from one or more of the amplitude, phase and frequency of the output signals of the gesture channel. The sensing apparatus may optionally serve as a monitor for evaluating user activities, such as by counting certain activities. The sensor may optionally serve as a user control interface for many different devices by generating control signal(s) based on the identification of one or more different motion gestures.

A system for estimating breathing frequency and heartbeat frequency of a subject, comprises at least one ultra-wideband (UWB) transceiver, connected to at least one hardware processor, configured to: receive a plurality of readings from the UWB transceiver, each of the readings having a time and comprising a plurality of intensity values at the time, each of the intensity values having a corresponding distance value representing a distance between the UWB transceiver and the subject; determine a Range-Doppler map from the plurality of readings; analyze the Range-Doppler map to determine a breathing frequency value of the subject; perform null steering of a plurality of Doppler-map frequencies in the Range-Doppler map to nullify the breathing frequency value and integer multiples of the breathing frequency value to obtain a plurality of combined frequencies; and determine a heartbeat frequency value for which a plurality of computed frequencies corresponds with the plurality of combined frequencies.