A cardiac output measurement device according to at least one embodiment of the present disclosure includes a cardiac waveform measurement unit that measures a waveform of microwaves transmitted through a living body; a respiratory waveform measurement unit that measures a waveform during breathing or a waveform during apnea of the living body; and a cardiac output calculation unit that calculates a waveform for obtaining a cardiac output of the living body from the waveform of the microwaves by using the waveform during breathing or the waveform during apnea.

A cardiac output measurement device according to at least one embodiment of the present disclosure includes a cardiac waveform measurement unit that measures a waveform of microwaves transmitted through a living body; a respiratory waveform measurement unit that measures a waveform during breathing or a waveform during apnea of the living body; and a cardiac output calculation unit that calculates a waveform for obtaining a cardiac output of the living body from the waveform of the microwaves by using the waveform during breathing or the waveform during apnea.

A system and method is disclosed where an operating state may be determined by selecting one or more transmitting nodes for transmitting one or more radio-frequency (RF) signals. One or more receiving nodes may receive the one or more RF signals that may include one or more channel state data. The operating state may be determined based on one or more features extracted from the one or more channel state data. Another system and method is disclosed where a range compensation value may be determined by transmitting a radio-frequency (RF) signal from at least one transmitting node. The one or more receiving nodes may receive the RF signal and a channel state data may be estimated using the signal. The range compensation value may be determined using the channel state data and a position value indicating a location of the at least one transmitting node.

A system and method is disclosed where an operating state may be determined by selecting one or more transmitting nodes for transmitting one or more radio-frequency (RF) signals. One or more receiving nodes may receive the one or more RF signals that may include one or more channel state data. The operating state may be determined based on one or more features extracted from the one or more channel state data. Another system and method is disclosed where a range compensation value may be determined by transmitting a radio-frequency (RF) signal from at least one transmitting node. The one or more receiving nodes may receive the RF signal and a channel state data may be estimated using the signal. The range compensation value may be determined using the channel state data and a position value indicating a location of the at least one transmitting node.

The present disclosure provides a high-performance integrated strain sensing device that is highly stretchable, rehealable, recyclable and reconfigurable. This device can include dynamic covalent thermoset polyimine as the substrate and encapsulation, eutectic liquid metal alloy as the strain sensing unit and interconnects, and off-the-shelf chip components for measuring and magnifying functions. The device can be attached on the knee, elbow, wrist and finger joints for strain sensing and motion monitoring, and can also be attached on the abdomen to accurately measure respiration cycles.

Methods, apparatus and systems for wireless vital signs monitoring are described. In one embodiment, a described system comprises: a transmitter, a receiver, a processor. The transmitter transmits, using N1 transmit antennas, a wireless signal through a wireless multipath channel of a venue, while a first object in the venue is having a first repetitive motion. The receiver receives, using N2 receive antennas, the wireless signal through the wireless multipath channel, and extracts a plurality of time series of channel information (TSCI) of the wireless multipath channel from the wireless signal. N1 and N2 are positive integers. Each of the plurality of TSCI is associated with a transmit antenna of the transmitter and a receive antenna of the receiver. The processor computes a first information of the first repetitive motion based on the plurality of TSCI, and monitors the first repetitive motion of the first object based on the first information.

A system includes a memory, an optical subsystem, an audio system, a plurality of sensors outputting sensor data, a communication circuitry and a processor. The processor is configured to input to a baby-specific behavioral state detection machine learning model, image data, audio signal data, sensor data, and baby-specific personal data associated with the baby, to receive an output from the baby-specific behavioral state detection machine learning model that the baby is agitated and/or about to wake up, to transmit instructions based on the output that cause the audio system and/or the optical subsystem to perform at least one of (i) generate a soothing sound when the baby is agitated, (ii) generate a sleep-enhancing sound when the baby is about to wake up, or (iii) project a relaxing image to be viewed by the baby when the baby is agitated.

A system includes a memory, an optical subsystem, an audio system, a plurality of sensors outputting sensor data, a communication circuitry and a processor. The processor is configured to input to a baby-specific behavioral state detection machine learning model, image data, audio signal data, sensor data, and baby-specific personal data associated with the baby, to receive an output from the baby-specific behavioral state detection machine learning model that the baby is agitated and/or about to wake up, to transmit instructions based on the output that cause the audio system and/or the optical subsystem to perform at least one of (i) generate a soothing sound when the baby is agitated, (ii) generate a sleep-enhancing sound when the baby is about to wake up, or (iii) project a relaxing image to be viewed by the baby when the baby is agitated.

Monitoring patient vital signs is performed by analyzing videos recorded of a patient. An algorithm is used to compute the real-time patient vital signs based on video images of the patient. These images can be captured by a video monitoring device positioned over a patient's bed. The video monitoring device can include a thermal camera and a near infrared camera that continuously send video images to a processing unit. The thermal image is used to locate the patient's face and chest. The images are analyzed with an algorithm employed to determine a patient's heart rate, respiration rate, temperature, and body position.

The present disclosure is related to systems and methods for motion detection. The method includes obtaining, via a first detection device, original cardiac motion data of a subject located in a field of view (FOV) of a medical device. The method includes obtaining, via a second detection device, detection data of the subject. The detection data includes at least one of posture data of the subject or physiological motion data of the subject. The method includes determining target cardiac motion data of the subject by correcting, based on the detection data, the original cardiac motion data.