An optical fiber blood pressure continuous detection wristband, comprising: an optical fiber sensing assembly module, the optical fiber sensing assembly module includes a sensing band and an optical fiber configured to extend along the sensing band and form a sensing area to sense a pulse wave; the sensing band includes an inner layer configured to be placed adjacent to the wrist to be detected, and the outer surface of the inner layer is abutted against the optical fiber; an outer layer, the inner surface of the outer layer is provided with a first concave-convex structure with a corrugate shaped, the first concave-convex structure being abutted against the optical fiber; wherein a active space is formed between the sensing band and the inner watchband and configured for the radial artery to beat; a calibration assembly configured to continuously calibrate blood pressure values; and a signal process assembly.

Disclosed is a biosensor arrangement structure, including: a plurality of biosensors that is disposed in a seat for supporting an occupant and that measures a health condition of the occupant. The seat includes: a seat body for holding the occupant; and an auxiliary supporter for supporting a body part of the occupant except for a torso and thighs. At least one of the plurality of biosensors is disposed at the auxiliary supporter.

A non-invasive method of calculating the central venous pressure (CVP) of a patient may include analysis of video of the neck region of the patient. Filters, which may include spatial filters and/or temporal filters, may be applied to the video to enhance the visibility of small movements, which may be due to circulatory pulsations of the patient. The video may be modified to highlight such movements, and motion indicative of venous pulsation may be distinctly identified and highlighted.

Disclosed is an apparatus for estimating bio-information. The apparatus for estimating bio-information includes: a sensor part comprising a pulse wave sensor array configured to detect a pulse wave signal when an object contacts a contact surface of the sensor part, and a load sensor configured to detect a first contact load applied by the object to the contact surface; and a processor configured to obtain contact load distribution of the contact surface based on the pulse wave signal, and to estimate bio-information based on the contact load distribution.

An electronic device includes a pressing component and a pressure sensor. The pressing component is disposed on a wearing structure of the electronic device. The pressing component includes an electromagnetic driving member and a pressure bearing member. The pressure sensor is disposed on the pressure bearing member, and the pressure sensor is located on a surface that is of the wearing structure and that faces a measurement body part of a user. The pressure bearing member is configured to be driven by the electromagnetic driving member to press the pressure sensor on the measurement body part, and the pressure sensor is configured to obtain a pulse wave signal of the measurement body part.

The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.

A clinical evaluation system and method are disclosed that facilitate the use of features or parameters extracted from biophysical signals in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of elevated left ventricular end-diastolic pressure (elevated LVEDP), as an example indicator of a disease medical condition that could be assessed by using the system and method described herein.

The system for cardiovascular parameter data quality determination can include a user device and a computing system, wherein the user device can include one or more sensors, the computing system, and/or any suitable components. The computing system can optionally include a data quality module, a cardiovascular parameter module, a storage module, and/or any suitable modules. The method for cardiovascular parameter data quality determination can include acquiring data and determining a quality of the data. The method can optionally include processing the data, and/or determining a cardiovascular parameter, training a data quality module, any suitable steps.

A health sensor system and method can include a wearable non-invasive biosensor for capturing cardiac waveform signals such as electrocardiogram (ECG) signals and composite vibration objects over one or more channels, one or more processors operatively coupled to the wearable non-invasive biosensor, and memory having computer instructions which causes the system to perform certain operations. In some embodiments, the operations can include powering the health sensor system in response to receiving a radio frequency signal using a near field communication protocol, monitoring pulmonary artery pressures based on cardiac time intervals during a period when the health sensor system is powered by the radio frequency signal, performing a heart and lung function assessment based on the monitoring of the pulmonary artery pressures, and presenting the heart and lung function assessment. In some embodiments, the biosensor can be a single NFC patch biosensor.

An apparatus for estimating blood pressure is provided. The apparatus may include a pulse wave sensor configured to measure from an object, a plurality of pulse wave signals having different wavelengths, a force sensor configured to measure a contact force applied by the object to the pulse wave sensor, and a processor configured to extract, from the plurality of pulse wave signals, at least one similarity feature indicating a similarity between the plurality of pulse wave signals, and estimate the blood pressure based on the similarity and the contact force that is measured at a point in time at which the at least one similarity feature is extracted.