A cover-type electronic device includes a cover-type housing, an electrode set including a plurality of electrodes disposed on an exterior surface of the housing, and a printed circuit board electrically connected to the plurality of electrodes. A driving circuit in the printed circuit board is configured to acquire sensing information, and identify a user's gripping posture, which matches one of a plurality of gripping postures for measurement of designated biometric data, based on the sensing information. The driving circuit identifies information corresponding to a contact impedance difference between both hands of the user from a biosignal detected through the electrode set, and switches a connection state of the electrode set, based on the corresponding information such that the user's biometric data is measured in the switched state.

The present invention provides a method and an apparatus for obtaining relevant characteristic parameters and indexes of tonoarteriogram (TAG) signals, relating to the technical field of blood pressure monitoring. A signal acquisition module acquires TAG signals from a target subject; a signal processing module obtains relevant characteristic parameters and indexes of continuous blood pressure from obtained TAG signals of target subject by calculating and processing through a predetermined mathematical model and a statistical algorithm, wherein the relevant characteristic parameters and indexes of continuous blood pressure include at least one of root mean square value of continuous blood pressure (RMSBP) and standard deviation of continuous blood pressure (SDBP), an evaluation module evaluates the status of continuous blood pressure based on characteristic parameters and indexes obtained from the target subject, an alarm module initiates an alarm when the relevant characteristic parameters and indexes of continuous blood pressure monitored to be abnormal, which allows to monitor the blood pressure of the target user at different period of time, realizing rapid blood pressure measurement, and enabling timely alarms to ensure the safety of the target user.

Provided is a method for estimating a pulse rate with high accuracy in the case that short-term burst noise is mixed in an estimation interval. Included are a pulse wave signal generating step of generating a pulse wave signal from an image of a pulse rate estimation target; a peak component suppression step of limiting an amplitude value of the pulse wave signal that is larger than a first threshold value to the first threshold value, and outputting a pulse wave analysis signal; and a frequency analysis step of outputting a frequency spectrum of the pulse wave analysis signal.

In some embodiments, a computer-implemented method of generating a visualization of wavelength-dependent surface characteristics is provided. A computing device receives an input image captured by a camera, wherein the input image includes information in a low-dimensional color space. The computing device processes the input image to determine spectrum band information in a high-dimensional color space that corresponds to the input image. The computing device extracts subtractive information from the spectrum band information to obtain wavelength-dependent surface characteristic information, The computing device generates the visualization using the wavelength-dependent surface characteristic information. In some embodiments, the computing device may be a smartphone.

Embodiments herein disclose computer-implemented methods, computer program products and computer systems for performing neurological diagnostic assessments. The computer-implemented method may include processors configured for receiving biometric activity data corresponding to user extremity movement from a mobile device associated with a user. Further, the computer-implemented method may include processors configured for transmitting the biometric activity data to a machine learning model. Furthermore, the computer-implemented may be configured for processing, using the machine learning model, the biometric activity data to generate first model output data corresponding to a first score. Even further, the computer-implemented method may include processors configured for determining that the first model output data corresponds to a neurological disorder classification based at least on the first score exceeding a predetermined threshold.

A method, system and mobile device can be used by a subject in diagnosing a disease or virus or other illness. The system can capture and analyze olfactory information providing a diagnosis based on the olfactory information. The system can also capture and output biometric data corresponding to the subject and can further include at least one camera or video sensor that can result in a diagnosis or a microphone or other acoustic sensor that can result in another diagnosis. Other sensors providing other corresponding diagnoses can be included. A sensor fusion component can receive and combine the biometric data and the various diagnoses results and further determine a confidence score. An event record creator compiles the biometric data and the confidence scores to create an event record having a higher confidence score with respect to a final diagnosis result. A data storage device stores the event record.

A method for determining a user's stress level is performed by a smartphone app. Touch and motion feature values are generated, the feature values are weighted by regression parameters, and a stress score is generated based on the weighted touch and motion feature values. The touch feature values indicate how the user's finger moves over the smartphone and are generated from touch data points including X positions, Y positions and associated touch timestamp values. The motion feature values indicate movement of the smartphone and are generated from motion data points including X movements, Y movements, Z movements and associated motion timestamp values. The regression parameters are generated using touch and motion data identified by other users as being acquired while those other users were experiencing various perceived levels of stress. The app indicates to the user whether the stress score is higher or lower than a previously generated stress score.

An electronic device may include body composition analysis circuitry that estimates body composition based on captured images of a face, neck, and/or body (e.g., depth map images captured by a depth sensor, visible light and infrared images captured by image sensors, and/or other suitable images). The body composition analysis circuitry may analyze the image data and may extract portions of the image data that strongly correlate with body composition, such as portions of the cheeks, neck, waist, etc. The body composition analysis circuitry may encode the image data into a latent space. The latent space may be based on a deep learning model that accounts for facial expression and neck pose in face/neck images and that accounts for breathing and body pose in body images. The body composition analysis circuitry may output an estimated body composition based on the image data and based on user demographic information.

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 apparatus for estimating concentration may include: a spectrum obtainer configured to obtain Raman spectra of an object; and a processor configured to extract, from the Raman spectra, at least one analyte spectrum related to an analyte and at least one non-analyte spectrum related to a biological component other than the analyte, and estimate concentration of the analyte based on a first area under a curve of the at least one analyte spectrum and a second area under a curve of the at least one non-analyte spectrum.