An apparatus and methods for non-contact monitoring of one or more persons/patients are disclosed. The apparatus includes a radar system configured for acquiring motion and proximity data of one or more persons at a plurality of distances, a processor configured for processing the acquired motion and proximity data to identify one or more physiological and/or behavioral features of one or more persons, and a transmitter configured for transmitting the one or more physiological and/or behavioral features of one or more persons to a remote device. In various embodiments, the apparatus may include a wearable sensor, a light or ambient sensor, a microphone and a speaker, or one or more buttons for user input. In various embodiments, a system for monitoring includes a plurality of apparatuses in a mesh network for sharing data from the plurality of apparatuses.

An apparatus and methods for non-contact monitoring of one or more persons/patients are disclosed. The apparatus includes a radar system configured for acquiring motion and proximity data of one or more persons at a plurality of distances, a processor configured for processing the acquired motion and proximity data to identify one or more physiological and/or behavioral features of one or more persons, and a transmitter configured for transmitting the one or more physiological and/or behavioral features of one or more persons to a remote device. In various embodiments, the apparatus may include a wearable sensor, a light or ambient sensor, a microphone and a speaker, or one or more buttons for user input. In various embodiments, a system for monitoring includes a plurality of apparatuses in a mesh network for sharing data from the plurality of apparatuses.

Various examples are provided for accurate heart rate measurement. In one example, a method includes determining a respiratory rate (RR) and respiration displacement from radar-measured cardiorespiratory motion data; adjusting notch depths of a harmonics comb notch digital filter (HCNDF) based upon the respiration displacement; generating filtered cardiorespiratory data by filtering the radar-measured cardiorespiratory motion data with the HCNDF; and identifying a heart rate (HR) from the filtered cardiorespiratory data. In another example, a system includes radar circuitry configured to receive a cardiorespiratory motion signal reflected from a monitored subject; and signal processing circuitry configured to determine a respiration displacement based upon the cardiorespiratory motion signal; adjust notch depths of a HCNDF based upon the respiration displacement; filter the cardiorespiratory motion data with the HCNDF; and identifying a heart rate (HR) from the filtered cardiorespiratory data.

Methods and systems estimate cardio-respiratory parameter(s), such as from in-phase and quadrature channels. The channels may represent patient chest movement and may be generated with a sensor, such as a contactless sensor that may sense movement with radio-frequency signals. In the methods/systems, the in-phase and quadrature channels may be processed, such as in a processor(s), using relative demodulation to generate cardio-respiratory parameter estimate(s). Optionally, the processing produces a jerk signal that may be filtered for producing a heart rate estimate, such as from zero-crossings of the filtered signal. Optionally, the processing produces a chest velocity signal that may be filtered for producing a respiratory rate estimate, such as from zero-crossings of the filtered signal. Optionally, a respiratory volume, such as tidal volume, may be estimated from an intrapulmonary pressure signal generated by applying a function to a chest displacement signal where the function relates intrapulmonary pressure and chest displacement.

Methods and systems estimate cardio-respiratory parameter(s), such as from in-phase and quadrature channels. The channels may represent patient chest movement and may be generated with a sensor, such as a contactless sensor that may sense movement with radio-frequency signals. In the methods/systems, the in-phase and quadrature channels may be processed, such as in a processor(s), using relative demodulation to generate cardio-respiratory parameter estimate(s). Optionally, the processing produces a jerk signal that may be filtered for producing a heart rate estimate, such as from zero-crossings of the filtered signal. Optionally, the processing produces a chest velocity signal that may be filtered for producing a respiratory rate estimate, such as from zero-crossings of the filtered signal. Optionally, a respiratory volume, such as tidal volume, may be estimated from an intrapulmonary pressure signal generated by applying a function to a chest displacement signal where the function relates intrapulmonary pressure and chest displacement.

A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment. In some embodiments, the system can further include a support surface having one or more sensors incorporated therein either in addition to sensors affixed to the patient or as an alternative thereof. The support surface is, in some embodiments, capable of responding to commands from the host for assisting in implementing a course of action for patient treatment. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.

In a configuration in which a holder holding a controller is mounted on a seat part with a plate-shaped member, the exposure of the mounting part of the plate-shaped member on which the holder is mounted is eliminated. A seat device includes a pressure sensor measuring a value relating to the seated person's state, a vibration imparting device performing a vibration imparting operation, an ECU controlling the vibration imparting device corresponding to the measurement result of the pressure sensor, a holder holding the ECU, and a mounting bracket fixed to a lower frame such that the holder is mounted on the lower frame of a seat part. The mounting bracket includes a mounting projection on which side wall of the holder is mounted in a predetermined mounting direction. When the side wall is mounted on the mounting projection, the mounting projection is covered with the side wall.

A fiber-optic sensor matt detects movements of a person on the matt that cause microbending of a fiber-optic cable that is arranged into a symmetric pair of radial ring groups within the matt. There are no cross-over points or overlapping of the fiber-optic cable within the symmetric pair of radial ring groups that could cause fiber wear and noisy readings. Microbending of the fiber-optic cable pressed into a mesh modulates the light intensity received, which is analyzed to extract both respiration and heart BallistoCardioGram (BCG) waveforms by convolution with Daubechies dB5 wavelet and scaling functions. The reconstructed level-4 detail waveform is output as the extracted BCG, while the reconstructed level-6 approximation waveform is output as the extracted respiration waveform. Respiration and heart rates and variations can be generated from the extracted waveforms. An integrated Fast Wavelet Transform (FWT) using dB5 wavelet thus generates both respiration rate and heart rate.

A fiber-optic sensor matt detects movements of a person on the matt that cause microbending of a fiber-optic cable that is arranged into a symmetric pair of radial ring groups within the matt. There are no cross-over points or overlapping of the fiber-optic cable within the symmetric pair of radial ring groups that could cause fiber wear and noisy readings. Microbending of the fiber-optic cable pressed into a mesh modulates the light intensity received, which is analyzed to extract both respiration and heart BallistoCardioGram (BCG) waveforms by convolution with Daubechies dB5 wavelet and scaling functions. The reconstructed level-4 detail waveform is output as the extracted BCG, while the reconstructed level-6 approximation waveform is output as the extracted respiration waveform. Respiration and heart rates and variations can be generated from the extracted waveforms. An integrated Fast Wavelet Transform (FWT) using dB5 wavelet thus generates both respiration rate and heart rate.

A method, apparatus, and system for determining a correspondence of physiological data obtained in a physiological study of a subject. The method includes extracting a first signal from the physiological study. A second signal from the physiological study is also extracted. The first signal and the second signal are obtained by one or more biometric sensors. Data of the first signal and data of the second signal are stored on a memory storage. A coherency value is determined between components of the extracted first signal and components of the extracted second signal. And the correspondence of the physiological study data is determined based on the determined coherency value.