The present application provides a personal hand-held monitor for the measurement of a subject's blood pressure and, optionally, one or more other vital signs, comprising a housing located on a personal hand-held computing device or a hand-held component of a computing system; a blood flow occlusion means located in the housing; a pressure sensor adapted to provide an electrical signal indicative of the pressure applied; a means for detecting the flow of blood in the body part of the subject when pressure is applied; and means for receiving electrical signals from the pressure sensor and the blood flow detecting means and for transmitting electrical signals indicative of the pressure and blood flow to the processor of the personal hand-held computing device or the computing system, wherein the processor of the personal hand-held computing device or computing system provide at least a measurement of the blood pressure of a subject. The processor is further adapted to carry out a process to measure a diastolic blood pressure value and a systolic blood pressure value.

A system to measure blood pressure of a person comprises a software that is configured to obtain two or more sensor information from one or more sensor near simultaneously. The sensor information comprises data points, from which at least one data feature can be derived. Based on the data features derived from the data points, a value of the cardiovascular time delay can be derived. The system can further derive a blood pressure value based on the cardiovascular time delay, and provide the value of the blood pressure, the cardiovascular delay, or other metrics derived from the cardiovascular delay to a user. The sensors can be placed in one or more mobile or wearable devices, which can communicate with each other wirelessly.

A system and method for determining a stress level of a person is provided. The system creates a numerical solution to mathematical model of a blood pressure (BP) regulation, lumped mathematical model of a radial artery and partial differential equation (PDE) model of the radial artery. These models are then used to generate an inference engine using. The inference engine is trained using an artificial neural network technique. At the same time the PPG signal of the person is sensed and preprocessed. The preprocessed PPG signal is then given to the trained inference engine. The trained inference engine generates a stress parameter corresponding to the person based on the processed PPG signal.

Systems and methods are disclosed for recommending products or services by receiving a three-dimensional (3D) model of one or more products; performing motion tracking and understanding an environment with points or planes and estimating light or color in the environment; and projecting the product in the environment.

A system includes a processor configured to wirelessly receive crash indicia from a vehicle. The processor is also configured to access an occupant profile including medical data relating to an occupant of the vehicle, identify a public safety access point (PSAP), and send the medical data to the identified PSAP, in response to the crash indicia.

A computer access control system includes a client electronic device configured to administer an alertness test to a user. A computer access controller is coupled to and configured to be actuated by the client electronic device.

An Internet-based disease monitoring system may include a miniaturized disease sensor device, which is coupled to a sensor, such as a spirometer, and a transmitter configured to transmit raw input signals from the sensor to a mobile device as cellular voice or text data for forwarding to a remote server. A remote server may be coupled to the network-based disease sensor device to provide analysis of raw input signals from the sensor. The remote server may be able to provide various services for grouping or handling functions relating to such input signals. A service may provide the ability to aggregate input signals from multiple sensors coupled to sensor devices and provide predictive modeling or statistical analysis, which may then be used to adjust future input signals. A service may also contain instructions how to handle billing based on usage of the network-based disease sensor device.

A method and system of screening and monitoring a person for Alzheimer's disease, Mild Cognitive Impairment, and Lewy Body Dementia (Dementia) using the King-Devick Test are provided including providing a visual display and a timing device; displaying on the visual display one or more series of symbols arranged in a pattern; providing an indication to the person being screened to begin reading aloud the symbols arranged in the pattern; capturing with the timing device the completion time to read the one or more series of symbols arranged in the pattern; comparing the completion time with a baseline time to complete reading the one or more series of symbols; and determining whether the person has an “abnormal” result based on comparing the completion time with the baseline time.

A control method of an apparatus according to an aspect of the present disclosure includes acquiring first vibration data that indicates a vibration of the apparatus produced by a speaker outputting a sound. Then the control method includes storing the acquired first vibration data in a memory. Further, the control method includes acquiring second vibration data that is generated by a vibration sensor detecting a vibration including a vibration based upon a body movement of a user on bedding on which the apparatus is placed. And the control method includes generating amended data by amending the acquired second vibration data using the first vibration data stored in the memory.

Methods and apparatus provide monitoring of coughing and/or a sleep disordered breathing state of a person. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of coughing and/or sleep disordered breathing. The processor may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type which may include generating an indication of a coronavirus disease or a coronavirus disease cough type.