A piezoelectric sensor (10) having an elongated-sheet shape includes a piezoelectric layer (11) containing an elastomer and piezoelectric particles and electrode layers (12a and 12b) which are disposed with the piezoelectric layer (11) sandwiched between the electrode layers. In the piezoelectric sensor (10), a pressure sensing region (S) has a length of 500 mm or longer in a longitudinal direction thereof; the electrode layers (12a and 12b) contain an elastomer and flaky conductive materials and are capable of elongating by 10% or more in one direction of plane directions; and when a space between one end portion (A) and the other end portion (B) of the pressure sensing region (S) in the longitudinal direction is set as a measurement zone, an electrical resistance in the measurement zone in the electrode layers (12a and 12b) is 3,000Ω or lower, and the specific Expression (I) is satisfied.

The present disclosure relates to a contactless sleep monitoring system and method for monitoring a plurality of characteristics of a subject based on vibration signals of a structure supporting the subject. The system can include a sensor that is coupled to the structure, but not in direct contact with the subject. A computing device in data communication with the sensor can obtain real-time sensor data from the sensor. The computing device can further analyze the sensor data to determine continuous and real-time measurements of characteristics of the subject where the characteristics can include a heart rate, a respiratory rate, a movement of the subject, and/or a posture of the subject. A user interface including a display of the determined measurements of the plurality of characteristics can be generated and displayed to a user.

Systems and methods for filtering time-varying data for filtering and extracting a predicted physiological signal. A method including: segmenting the time-varying data into temporal windows; using a trained filter machine learning model, predicting an error for each prediction of the physiological signal for each window of time-varying data, the filter machine learning model trained using physiological signal predictions based on training time-varying data and known values of the physiological signal for the training time-varying data; discarding each window of time-varying data when the predicted error for such window is greater than a threshold; and where the window of time-varying data is not discarded, outputting at least one of the window of time-varying data and the predicted error for each prediction of the physiological signal.

According to the invention, a method for detecting at least one physiological signal (1) of a patient (7), wherein the method comprises the following method steps: monitoring at least a subsection (2) of a patient's surface (4) with a thermal camera (5) which generates consecutive video frames with multiple pixels (6) of the monitored subsection (2), wherein the subsection (2) of a patient's surface (4) includes at least a part of the mouth and/or nose area of the patient (7) as a region of interest (3); generating time-resolved temperature values of at least one pixel (8) of the region of interest (3); and generating a cardiac signal (9) as the physiological signal (1) based on the generated time-resolved temperature values. In this way, a possibility is provided for contactless tempera-ture-based monitoring of a patient in an easy and cost-efficient way.

According to the invention, a method for detecting at least one physiological signal (1) of a patient (7), wherein the method comprises the following method steps: monitoring at least a subsection (2) of a patient's surface (4) with a thermal camera (5) which generates consecutive video frames with multiple pixels (6) of the monitored subsection (2), wherein the subsection (2) of a patient's surface (4) includes at least a part of the mouth and/or nose area of the patient (7) as a region of interest (3); generating time-resolved temperature values of at least one pixel (8) of the region of interest (3); and generating a cardiac signal (9) as the physiological signal (1) based on the generated time-resolved temperature values. In this way, a possibility is provided for contactless tempera-ture-based monitoring of a patient in an easy and cost-efficient way.

A method for analyzing respiratory kinematics includes collecting a plurality of kinematic signal data streams from each of a respective plurality of inertial sensor devices applied to a subject, wherein the kinematic signal data streams are synchronized with each other, transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals, determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices, and analyzing two or more of the individual breathing intervals to establish a magnitude-synchronicity relationship that is utilized to determine a probability of a presence of a respiratory condition existing in the subject.

A method for analyzing respiratory kinematics includes collecting a plurality of kinematic signal data streams from each of a respective plurality of inertial sensor devices applied to a subject, wherein the kinematic signal data streams are synchronized with each other, transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals, determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices, and analyzing two or more of the individual breathing intervals to establish a magnitude-synchronicity relationship that is utilized to determine a probability of a presence of a respiratory condition existing in the subject.

An infant sleep device may include a platform for supporting an infant, a base upon which the platform is supported, and one or more weight sensors positioned to measure weight of an infant positioned on the platform.

An apparatus may be configured for documenting a code blue scenario when adhered to the chest of a subject undergoing resuscitation by sensing and transmitting information associated with the code blue scenario. Such information may include one or more of vital signs of the subject during resuscitation, information associated with chest movements of the subject during resuscitation, and audio information from an environment of the subject during resuscitation. A computing platform that is separate and distinct from the apparatus may provide code blue documentation conveying information related to the vital signs of the subject and derived from the audio information from the environment of the subject during resuscitation.

An apparatus may be configured for documenting a code blue scenario when adhered to the chest of a subject undergoing resuscitation by sensing and transmitting information associated with the code blue scenario. Such information may include one or more of vital signs of the subject during resuscitation, information associated with chest movements of the subject during resuscitation, and audio information from an environment of the subject during resuscitation. A computing platform that is separate and distinct from the apparatus may provide code blue documentation conveying information related to the vital signs of the subject and derived from the audio information from the environment of the subject during resuscitation.