Beijing Shunyuan Kaihua Technology Limited

Anhui Huami Health Technology Co., Ltd.

Zepp, Inc.

Beijing Shunyuan Kaihua Technology Co., Ltd.

Beling Shunyuan Kaihua Technology Limited

Artem Galeev

Yan Vule

Kongqiao Wang

Yoav Aminov

Alexander Sromin

Ninel Gorev

Elisabeth Dickinson

Shimon Hayun

Aanchan Kumar Mohan

Gabrielle Toupin

Andrew J Rosenthal

Elena Sokolova

Vladimir Kolesnikov

Shaghayegh Zihajehzadeh

Rana Sadeghi Chegani

Vahid Zakeri

Aaron Zimmer

Aiden Arnold

Training data are obtained. Each training datum includes environment characteristics obtained based on sensor data. Respective user settings corresponding to the training data are obtained. At least one respective user setting corresponds to one training datum, and a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device. A machine-learning model for the hearing aid device is trained to output values for the at least one parameter. The hearing aid device is reconfigured based on an output of the machine-learning model. Reconfiguring the hearing aid device includes using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter and configuring the hearing aid device to use at least one current value.

Hearing aid personalization using machine leaning

A system and methods are used to enable hearing device calibration using auditory filters. The system and methods are used to reduce processing time for estimating auditory filters for new hearing device users and can be performed by the user without the aid of an audiologist. The system and methods use a database of notched-noise test results of other users to create a prior distribution of possible auditory filter parameters. The prior distribution is used to minimize the number of notched-noise test performed, thereby reducing the amount of calibration time.

Auditory filter fitting for hearing devices

A device including sensors and a processor. The sensors are configured to detect movements of a user. The processor is configured to categorize the movements of the user as a micro-movement or a macro-movement; quantify a number of the micro-movements; quantify a number of the macro-movements; determine based upon the number of micro-movements whether a body part of interest of a user is supported; and provide feedback to the user if the body part of interested is unsupported and continuing to monitor the body part of interest if the user is supported without providing any feedback.

Neck evaluation method and device

A method includes obtaining a baseline blood pressure at an initial time window; estimating a plurality of intermediate blood pressure change estimates, the intermediate blood pressure change estimates correspond to respective time windows that are subsequent to the initial time window; estimating a final blood pressure change estimate between the initial and final time windows; and obtaining the blood pressure by adding the baseline blood pressure to the final blood pressure change estimate. Estimating the final blood pressure includes estimating a first blood pressure change between the initial time window and the final time window; estimating a plurality of second blood pressure changes, each second blood pressure change is between a respective time window of the respective time windows and the final time window; and estimating the final blood pressure change estimate as a combination of the first blood pressure change and the plurality of second blood pressure changes.

Blood pressure measurement

A wearable device includes a processor and a lower module. The lower module includes a pressure sensor for detecting a mechanical movement of a skin that covers an artery. The mechanical movement of the skin is due to blood flow through the artery. The processor is configured to receive skin movement information from the movement sensor; calculate a pulse front velocity (PFV), which is a velocity of a blood wave as the blood wave passes under the pressure sensor; estimate a pulse wave velocity (PWV) using the PFV; and estimate the blood pressure using the PWV.

Examples described herein include systems and methods for determining sleep characteristics based on movement data. In one example, a device including at least one accelerometer can be worn by a user during the night. The accelerometer can be configured to detect small motions of the user and provide an output signal. The output signal can be filtered and used to determine whether the user was sleeping during a particular timeframe. The output signal can also be used to determine a sleep characteristic of the sleep, such as light sleep, deep sleep, REM sleep, obstructive apnea, or central apnea.

Systems and methods for respiratory analysis

Described herein are systems and methods for collecting physiological information from a user and determining information including, but not limited to, a user's heart rate, blood pressure, oxygen levels (SvO), hydration, respiration rate, and heart rate variability. Physiological information is collected from one or more modules, each comprising a sensor array. The sensor array(s) can comprise, among other things, light sources, photo detectors, ECG electrodes/sensors, bio impedance sensors, galvanic skin response sensors, tonometry/contact sensors, accelerometers, pressure sensors, acoustic sensors, and electromagnetic sensors. The information collected from a user can be used to cross-reference a database comprising similar information for a number of subjects as well as verified measurements for each subject including, but not limited to, blood pressure, oxygen levels (SvO), hydration, respiration rate, and heart rate variability. As such, a user's blood pressure, oxygen levels, hydration, respiration rate, and heart rate variability can be accurately estimated without direct measurement.

Systems and methods for collecting physiological information of a user

A system can include a wearable device that obtains real-time physiological data and activity data from a user and transmits that data to another device. A computing device can receive the data and calculate a first HRV score for the user based on physiological data from first time period and a second HRV score for the user based physiological data from a second time period. The device can present the user with at least one of the first and second HRV scores. In one example, a graphical display is provided on a GUI that includes indicators for each day of the week. In response to a user selecting an indicator for a day of the week, the GUI can display an HRV score for the selected day, among other information.

Systems and methods for providing user insights based on heart rate variability

A system can include a wearable device that obtains real-time physiological data and activity data from a user and transmits that data to another device. A computing device can receive HRV and activity data and determine whether the user's autonomic nervous system is in a predominantly sympathetic or parasympathetic state. For example, the determination can include comparing an average variance in a portion of the HRV data with a threshold value. In response to determining that the user's autonomic nervous system is in a sympathetic state, the device can perform an action.

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Vancouver, Canada

Artem Galeev