A system for automatically assessing orthostatic hypotension for a patient supported on a patient support apparatus. The system receives position data identifying a first position of a patient supported on a patient support apparatus, and after a delay, receives vital signs data of the patient. The system receives position data identifying a second position of the patient supported on the patient support apparatus, and after a delay, receives vital signs data of the patient. The system determines an orthostatic hypotension assessment based on a difference in the vital signs data between the first and second positions. Based on the orthostatic hypotension assessment, the system modifies one or more conditions on the patient support apparatus to mitigate a risk for patient fall.

Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

A method and system for presence and vitals detection of a living subject is disclosed herein. The system comprises a device that monitors and is an interface. The device comprises an RGB/IR imaging sensor, a radar, a processor, and a first communication module. The processor is configured to run an algorithm to perform digital signal processing on data provided by the radar and the RGB/IR imaging sensor to generate presence and vitals information for the living subject for communication to the device.

A pulse sensor and a pulse sensing system are provided. The pulse sensor includes a pressure sensing circuit, a reference circuit, and an output circuit. The pressure sensing circuit may sense a pulse vibration to generate a sensing signal. The reference circuit may generate a reference signal according to a base signal. A first input terminal of the output circuit is coupled to the pressure sensing circuit. A second input terminal of the output circuit is coupled to the reference circuit. The output circuit generates a pulse sensing current at an output terminal of the output circuit according to a difference between the sensing signal and the reference signal.

An electronic device is provided. The electronic device includes a first biometric sensor comprising a plurality of electrodes and a measurement sensor electrically connected with the plurality of electrodes, a display, a memory, and a processor configured to obtain first biometric sensing information including a first electrocardiogram waveform using the first biometric sensor, identify a suspected disease based on the first electrocardiogram waveform and a previously obtained second electrocardiogram waveform, obtain second biometric sensing information corresponding to the identified suspected disease, and display, on the display, suspected disease information obtained based on the second biometric sensing information.

A signal representing heartbeat behavior is accurately restored. The present signal restoration system includes: a signal acquirer configured to acquire a first heartbeat signal representing heartbeat behavior; a first band-pass filter configured to generate a first signal by performing first band-pass filter processing on the first heartbeat signal; an integral calculator configured to calculate an integral value by integrating frequency intensity of the heartbeat represented by the first signal; a second band-pass filter configured to generate a third signal by performing second band-pass filter processing on a second signal representing the integral value with respect to time; and a restored signal generator configured to generate a restored signal representing heartbeat behavior based on first data generated by dividing the third signal at intervals of a predetermined time.

Devices and methods for determining item-specific information for single or multiple items on one or multiple substrates are described. The method includes generating multiple sensor multiple dimensions array (MSMDA) data from multiple sensors, where each of the multiple sensors capture sensor data for one or more items in relation to a substrate. For each item, the method includes determining relationships between the multiple sensors based on characteristics of the MSMDA data, determining a location of the item on the substrate based on at least the determined relationships between the multiple sensors, determining an angular orientation of the item on the substrate based on at least the determined relationships between the multiple sensors, and determining a body position of the subject on the substrate based at least the determined relationships between the multiple sensors, the location of the subject, and the angular orientation of the item.

A pulse sensor is capable of measuring a pulse rate of a wearer at a peripheral artery. In an embodiment, the pulse sensor includes a magnet supported to move responsive to an arterial pulse and a magnetometer configured to detect changes in a magnetic field produced by the magnet. The magnet may include a plurality of ferromagnetic particles disposed in or on a flexible substrate configured to be held adjacent to human skin subject to arterial palpation and a magnetic sensor configured to sense movement of the ferromagnetic particles. A system and method may measure hydration includes using a pulse sensor to measure pulse rate and modulation. The wearer is prompted when the pulse rate and pulse modulation indicate a response to dehydration of the wearer.

The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or 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, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.