An artificial intelligence self-learning-based automatic electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat feature detection, interference signal detection and heartbeat classification based on deep learning, signal quality evaluation and lead combination, heartbeat verification, analysis and calculation of electrocardiography events and parameters, and finally automatic output of reporting data, realizing an automated analysis method having a complete and rapid flow. The automatic electrocardiography analysis method may also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

An apparatus and a method of measuring bioinformation are provided. The apparatus for measuring bioinformation includes a first sensor configured to measure a first biosignal including arterial pulse wave information, a second sensor configured to measure a second biosignal including venous or capillary pulse wave information, and a bioinformation estimator configured to estimate bioinformation of a user based on a time delay between the first biosignal and the second biosignal.

A positioning method of functional rotation center of shoulder based on rigid upper arm model includes: step 1: abstracting a human upper arm into a cylinder with FRCS as a center of top surface; step 2: determining a reference axis vector of the cylinder; step 3: determining an axis vector of the cylinder and a displacement from the reference axis vector to the axis vector; step 4: correcting a central axis direction of the cylinder; step 5: determining a height compensation of the cylinder, and positioning the FRCS. The method has higher accuracy for the positioning result of FRCS, the positioning result of FRCS has better stability relative to the upper arm and trunk, and can be used to establish a more accurate human digital dynamic model and predict more accurate human posture.

A system, apparatus, and method for physiological data collection, providing targeted content, and facilitating remote diagnostics. In one embodiment, a kiosk contains physiological data collection devices, electronic computing devices, and targeted-content display devices to automatically collect physiological data regarding a patient and display targeted content specifically tailored to that patient based on the collected data.

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure may include: receiving a request to determine blood pressure of a first subject from a terminal, obtaining data related to heart activity of the first subject, determining a personalized model for predicting blood pressure with respect to the first subject, determining the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject, and sending the blood pressure of the first subject to the terminal in response to the request.

A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

An example information processing system executes a game application. The information processing system obtains user information for calculating information relating to sleep of a user, and calculates health information relating to sleep and/or fatigue of the user based on the obtained user information. The information processing system controls a game progress of the game application based on the health information.

A method for providing a thermal feedback, includes executing a virtual reality application providing a virtual space that includes a virtual area to which an area temperature attribute is assigned, and a virtual object to which an object temperature attributed is assigned. An area event that reflects that a player character enters the virtual area is detected. A feedback device is controlled to output thermal feedback associated to the area temperature attribute when the area event is detected, the feedback device outputting the thermal feedback using a thermoelectric element performing a thermoelectric operation. An object event reflecting the player character is influenced by the virtual object is detected. The feedback device is controlled to override the thermal feedback associated to the area temperature attribute and output thermal feedback associated to the object temperature when the object is detected while the player character is in the virtual area.

A sensor interface is configured to receive a sensor signal. A transmitter generates a transmit signal. A receiver receives the signal corresponding to the transmit signal. Further, a monitor interface is configured to communicate a waveform to the monitor so that measurements derived by the monitor from the waveform are generally equivalent to measurements derivable from the sensor signal.

Various embodiments described herein relate to a method and related wearable device and non-transitory machine-readable medium including receiving sensor data from at least one sensor of the wearable device; comparing the sensor data to schedule format stored in a memory of the wearable device, wherein the schedule format specifies at least one characteristic of sensor readings previously associated with a predefined context; determining that the received sensor data matches the schedule format; determining, based on the received sensor data matching the schedule format, that the user is currently in the predefined context; identifying an action associated with the predefined context; and executing the action while the user is in the predefined context.