A method, system, and/or apparatus for automatically monitoring for possible infection or other physical health concerns, such as from Covid-19. The method or implementing software application uses or relies upon location information available on the mobile device from any source, such as cell phone usage and/or other device applications. The method and system automatically uses and/or learns user location and activity patterns and determines and infection risk that can be communicated as a warning to community members.

A method is provided that includes determining a quality of a data portion of an input sensor data stream based, at least in part, on data of a first data type and determining between, at least, generation of two or more streams of a second, different data type including at least one synthesised data stream of the second data type. Determining between generation of two or more streams of a second, different data type is based, at least in part, on the determined quality. The synthesis is based, at least in part, on the data of the first data type. The method further includes causing generation of at least one stream of the second, different data type based, at least in part, on the determination between generation of two or more streams of the second, different data type.

The motion data monitoring method includes: collecting, by an electronic device, an angular velocity signal and an acceleration signal of a user; obtaining, by the electronic device, a waveform feature of the angular velocity signal based on the angular velocity signal, and obtaining a waveform feature of the acceleration signal based on the acceleration signal; determining, by the electronic device, a gait feature of the user according to the waveform feature of the angular velocity signal and the waveform feature of the acceleration signal, where the gait feature includes a duration of flight from off-ground of a foot of the user to touching of the ground; and determining, by the electronic device, motion data according to the gait feature, where the motion data includes a jump height.

A method for managing sleep of a user comprises obtaining, by a computing system, sleep data and environmental data for the user; determining, by the computing system, a sleep state of the user based on the sleep data; determining, by the computing system, one or more awakening actions based on the sleep state of the user and the environmental data; and causing one or more devices in an environment of the user to perform the one or more awakening actions to awaken the user.

A motion data processing method and a motion monitoring system provided in the present disclosure may process an electromyography (EMG) signal in the frequency domain or time domain to identify an abnormal signal in the EMG signal, such as an abrupt signal, a missing signal, a saturation signal, an oscillation signal, etc. caused by a high-pass filtering algorithm. The motion data processing method and the motion monitoring system may further perform a data sampling operation on the EMG signal through a data sampling algorithm, and predict data corresponding to the time point when the abnormal signal appears based on the sampling data, so as to obtain prediction data, and replace the abnormal signal by using the prediction data to correct the abnormal signal. The motion data processing method and the motion monitoring system may not merely accurately identify the abnormal signal, but further correct the abnormal signal, so that the corrected data may be more in line with an actual motion of a user, thereby improving user experience.

A system for generating complementary data for a visual display that includes one or plurality of wearable sensors that collect tracking data for a users position, orientation, and movement. The sensor(s) are in communication with at least one processor that may be configured to receive tracking data, identify missing tracking data, generate complementary data to substitute for missing tracking data, generate a 3D model comprised of tracking data and complementary data, and communicate the 3D model to a display. Complementary tracking data may be generated by comparison to a key pose library, by comparison to past tracking data, or by inverse kinematics.

A wearable medical includes a walking detector module with a motion sensor that is configured to detect when the patient is walking or running. In embodiments, a parameter (referred to herein as a “Bouncy” parameter) is determined from Y-axis acceleration measurements. In some embodiments, the Bouncy parameter is a measurement of the AC component of the Y-axis accelerometer signal. This detection can be used by the medical device to determine how and/or whether to provide treatment to the patient wearing the medical device. For example, when used in a WCD, the walking detector can prevent “false alarms” because a walking patient is generally conscious and not in need of a shock.

A method, system, and/or apparatus for automatically monitoring for possible infection or other physical health concerns, such as from Covid-19. The method or implementing software application uses or relies upon location information available on the mobile device from any source, such as cell phone usage and/or other device applications. The method and system automatically uses and/or learns user location and activity patterns, and/or viral variant information, and determines an infection risk that can be communicated as a warning to the user or community members.

An apparatus for promoting vascular circulation, including a garment configured to at least partially surround an anatomical structure of a patient. A compression element is coupled to the garment and configured to compress at least a portion of the anatomical structure when the compression element is actuated. A controller is configured to selectively actuate the compression element over a plurality of cycles. Each cycle has an actuated time during which the compression element is arranged to exert a first pressure and an unactuated time during which the compression element is arranged to exert a second pressure different than the first pressure, and the controller is configured to use a random value in determining the deflated time of one or more of the cycles.

Provided are a smart shoe and a data processing method thereof. The smart shoe includes: a stance phase measurement unit for, when one of two feet is set as a reference foot and a period during which the reference foot is taken off a ground and touches the ground again is set as a walking cycle, detecting acceleration and gyro in a stance phase in which the reference foot touches the ground; a swing phase measurement unit for detecting a difference between acceleration of the swing phase when the reference foot is taken off the ground and acceleration at the moment when the reference foot touches the ground; and a predict velocity update (PUPT) unit for obtaining and correcting a velocity of the stance phase condition.