A computer-implemented medical method of determining a breathing signal of a patient is disclosed. The method includes determining a motion trajectory of a structure associated with at least one body part of the patient, the motion trajectory being indicative of a respiratory movement of the structure, acquiring surface data representative of a position of a surface region of the patient, computing an intersection of the determined motion trajectory and the acquired surface data, and determining a breathing signal of the patient based on the computed intersection. The breathing signal is indicative of a breathing state of the patient.

The present invention relates to a remote photoplethysmography device (150) for registering a first image frame (120) acquired by a first imaging unit (110) and a second image frame (140) acquired by a second imaging unit (130), both the first and the second image frames (120, 140) depicting a common region of interest (160), the remote photoplethysmography device (150) comprising a processing unit (190) configured to measure a first pixel displacement (200) between the first image frame (120) and the second image frame (140), to correct the first pixel displacement (200) according to spatial and/or temporal geometric constraints between the first imaging unit (110) and the second imaging unit (130), and to register the first image frame (120) with the second image frame (140) based on the corrected first pixel displacement (200).

Methods and systems for non-contact monitoring of a patient to determine a respiratory parameter such as respiration rate. The systems and methods receive a depth signal from the patient to determine patient movement indicative of respiration. The methods include analyzing multiple regions in a region of interest (ROI) to determine whether or not respiration is occurring in the analyzed region, and preparing a mask with the regions determined to have respiration. The mask is used to determine the respiratory parameter of the patient in the masked ROI.

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.

The present disclosure is related to systems and methods for radiation. The method may include obtaining a plurality of reference images of a target of a subject and reference physiological motion information of the subject. The plurality of reference images and the reference physiological motion information may be acquired in a radiation period. The method may include establishing a correlation model based on the plurality of reference images and the reference physiological motion information. The method may include monitoring real-time motion information of the target based on the correlation model during a radiation operation performed during the radiation period.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

An overdose of opioids can cause the user to stop breathing, resulting in death. A physiological monitoring system monitors respiration based on oxygen saturation readings from a fingertip pulse oximeter in communication with a smart mobile device and sends opioid monitoring information from the smart mobile device to an opioid overdose monitoring service. The opioid overdose monitoring service notifies a first set of contacts when the opioid monitoring information indicates a non-distress stats and notifies a second set of contact when the opioid monitoring information indicates an overdose event. The notification can be a phone call or text message to a specified person, emergency personnel, or first responders, and can include the location of the smart mobile device. The smart mobile device can also include the location of the nearest treatment center having emergency medication used in treating opioid overdose, such as naloxone.

A system for respiration monitoring includes a garment, which is configured to be fitted snugly around a body of a human subject, and which includes, on at least a portion of the garment that fits around a thorax of the subject, a pattern of light and dark pigments having a high contrast at a near infrared wavelength. A camera head is configured to be mounted in proximity to a bed in which the subject is to be placed, and includes an image sensor and an infrared illumination source, which is configured to illuminate the bed with radiation at the near infrared wavelength, and is configured to transmit a video stream of images of the subject in the bed captured by the image sensor to a processor, which analyzes movement of the pattern in the images in order to detect a respiratory motion of the thorax.

An ultrasonic diagnostic apparatus according to an embodiment includes an image obtaining unit, a contour position obtaining unit, a volume information calculating unit, and a controlling unit. The image obtaining unit obtains a plurality of groups of two-dimensional ultrasound image data each of which is generated by performing ultrasound scans, the ultrasound scans being performed on each of a plurality of predetermined cross-sectional planes, and performed for predetermined time. The contour position obtaining unit obtains, by performing a tracking process over the predetermined time period, time-series data of contour positions, the contour positions being either one of, or both of, a cavity interior and a cavity exterior of a predetermined site. The volume information calculating unit calculates, on a basis of a plurality of the time-series data of contour positions, volume information of the predetermined site. The controlling unit exercises control so as to output the volume information.

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state-of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. The present approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of-the-art HR estimation methods in naturalistic conditions.