A light sensing method, applied to a light sensing system comprising a light sensor and at least one light source. The light sensor comprises a plurality of light sensing units. The light sensing method comprises: controlling the light sensor to capture images according to the light source; generating an exposure condition according brightness that each of the light sensing units senses, to control all the light sensing units to generate a target brightness distribution according to the exposure condition; and controlling the light sensing units to sense light from the light source according to the exposure condition. The light sensing system can have a better SNR via adjusting the exposure condition for each one of the light sensing units. Such light sensing method can be applied to compute physiological parameters.

A method includes receiving acoustic inputs; generating signal channels from the acoustic inputs; pre-processing data in the signal channels; extracting S1-S2 peaks from the pre-processed data; removing artifacts and outliers from the S1-S2 peaks; generating S1-S2 signal channels based on the S1-S2 peaks in the pre-processed signal channels; selecting two or more of the S1-S2 signal channels; and combining the selected two or more S1-S2 signal channels to produce an acoustic uterine monitoring signal.

A flexible, passive pressure sensor includes three LC tank circuits. The first LC tank circuit is a pressure sensing LC tank circuit, having a capacitance that varies in response to changes in environmental pressure. The second and third LC tank circuits are reference LC tank circuits, having capacitances that are relatively constant over changes in environmental pressure. A measurement tool measures the resonant frequencies of the three LC tank circuits and then computes a pressure measurement that accounts for changes in resonant frequencies in the LC tank circuits due to environmental effects and deforming.

Various embodiments relate to a method for estimating a breathing rate, including: receiving a plurality of channel state information (CSI) from a wireless device; selecting an initial reference CSI from the plurality of CSI; computing a perturbation index using the initial reference CSI and a portion of the plurality of CSI; determining an optimal reference CSI based upon the perturbation index; re-computing the perturbation index using the optimal reference CSI on a portion of the plurality of CSI subsequent to the optimal reference CSI; and determining a breathing rate from the perturbation index using a frequency analysis of the perturbation index.

There is provided a heart rate detection device including a sensing unit for sensing emergent light from subcutaneous tissues illuminated by a single light source of multiple light colors to output multiple light detection signals associated with multiple wavelengths. The heart rate detection device further includes a processor uses the multiple light detection signals associated with the multiple wavelengths to cancel motion artifact to obtain a clean heart rate signal.

An Electrocardiography (ECG) noise-filtering device is provided in the invention. The ECG device includes a filter and a calculation circuit. The filter receives a first ECG signal and performs a Savitzky-Golay algorithm to generate a second ECG signal. The calculation circuit is coupled to the filter to receive the second ECG signal and processes the second ECG signal according to a Stationary Wavelet Transform (SWT) algorithm to generate a noise signal, and subtracts the noise signal from the second ECG signal to filter the noise signal in the first ECG signal.

There is provided a heart rate detection device including a sensing unit for sensing emergent light from subcutaneous tissues illuminated by a single light source of multiple light colors to output multiple light detection signals associated with multiple wavelengths. The heart rate detection device further includes a processor uses the multiple light detection signals associated with the multiple wavelengths to cancel motion artifact to obtain a clean heart rate signal.

This application relates to physiological monitoring typically for health and fitness purposes. Specifically, this application targets health and fitness monitors that require low noise acquisition of low amplitude biopotential signals. The method herein allows measurement and acquisition of biopotential signals that are normally too small to resolve due to the noise floor limitations of modern low noise amplifiers. Examples of applications that this method enables include monitoring devices located in far proximity from the location in which a biopotential signal originates, such as a wrist worn cardiac monitor, or a device that needs to sense low amplitude, fine muscle or nerve activity in a localized region.

Systems and methods for interference and motion detection from dark periods are provided, including analysis of a physiological signal to determine a physiological parameter of a subject, using a photoplethysmography system to monitor signals during an LED-off period to identify interference or motion artifacts in the signal.

Various examples are provided for non-contact vital sign acquisition. Information can be provided regarding vibrations of a target using a radar signal such as, e.g., non-contact vital sign measurement. Examples include estimation of heart rate, change in heart rate, respiration rate, and/or change in respiration rate, for a human or other animal. Implementations can produce one or both rates of vibration and/or change in one or both rates of vibration for a target other than an animal or human experiencing two vibrations at the same time, such as a motor, a vehicle incorporating a motor, or another physical object. Some implementations can estimate the respiration movement in the radar baseband output signal. The estimated respiration signal can then be subtracted from radar signals in the time domain and, optionally, can be further enhanced using digital signal processing techniques, to produce an estimate of the heartbeat pulses.