Abstract: A training session scheduling method, device, and non-volatile computer readable medium are provided. The method includes: sensing a maximum ability value of a test exercise item; estimating a fitness score of the user according to the maximum ability value; arranging a training exercise item schedule of a training session according to the fitness score; sensing a training intensity value of the training exercise item performed by the user during the training time; estimating a unit intensity value of the training exercise item in the unit time according to the training intensity value; comparing the unit intensity value with a preset intensity value corresponding to the training exercise item; updating the preset intensity value of the training exercise item and updating the maximum ability value of the training exercise item; and estimating the fitness score according to the updated maximum ability value.

Abstract: The invention provides a human body detection method, a human body detection device, and a computer readable storage medium. The method includes the following. A plurality of image frames related to a human body is obtained. A plurality of joint coordinates are detected in each image frame, and a plurality of specific image frames are accordingly found out. An image region height corresponding to the human body in each specific image frame is obtained. A first joint coordinate of a first joint in each specific image frame is obtained. A second joint coordinate of a second joint in each specific image frame is obtained. An actual length between the first joint and the second joint is estimated based on a height of the human body and the image region height, the first joint coordinate, and the second joint coordinate in each specific image frame.

Abstract: A posture comparison method, an electronic device, and a computer-readable storage medium are provided. The method includes: obtaining M first image frames of a first video stream within a specific time interval, and obtaining N second image frames of a second video stream within the specific time interval; determining K first joint points corresponding to K specified joints in each first image frame, and determining K second joint points corresponding to the K specified joints in each second image frame; obtaining an m-th first image frame among the M first image frames and an n-th second image frame among the N second image frames; and determining a difference between each first joint point and each corresponding second joint point in the m-th first image frame and the n-th second image frame, and determining a posture difference degree and providing a posture correction recommendation corresponding to the specific time interval accordingly.

Abstract: The embodiments of the disclosure provide a method for adjusting a noise cancellation mode, an electronic device, and a computer readable storage medium. The method includes: estimating a noise perception indicator corresponding to an environmental noise; and adjusting a noise cancellation mode of an wearable audio device according to the noise perception indicator.

Abstract: An embodiment of this disclosure provides an exercise data estimation method, device, and a computer-readable storage medium. The method includes that an exercise data set corresponding to a tth time point is obtained, where the exercise data set corresponding to the tth time point includes an exercise heart rate and an exercise power corresponding to the tth time point; a heart rate ratio corresponding to the tth time point is determined based on the exercise heart rate corresponding to the tth time point; in response to determining that the exercise data set and the heart rate ratio corresponding to the tth time point match multiple predetermined conditions, the exercise data set is determined to be a valid exercise data set, and a power estimation model is updated based on the exercise data set and the heart rate ratio corresponding to the tth time point; and a maximum aerobic power corresponding to the tth time point is estimated based on the power estimation model.

Abstract: The present disclosure provides an exercise assistive device. The present disclosure also provides a method for estimating stamina level and a method for adjusting the method according to a user's anaerobic energy level and aerobic energy level. The present disclosure further provides the several types and applications of the exercise assistive device.

Abstract: The present invention discloses a method for monitoring an exercise. The method comprises: building up an energy metabolism system having an energy metabolism feature, wherein the energy metabolism feature comprises a first feature factor, wherein the first feature factor is associated with a first biological system being one of a plurality of biological systems of a human body and not associated with the complete human body; building up a mathematical model describing that an energy expenditure depends on at least one exercise-associated parameter based on the energy metabolism feature for the energy metabolism system; estimating the energy expenditure based on the at least one exercise-associated parameter measured in the exercise by the mathematical model of the energy metabolism system; and monitoring the exercise based on the energy expenditure.

Abstract: An embodiment of the invention provides a sleep quality assessment method, a device, and a computer-readable storage medium. The method includes the following. Multiple sleep indicators associated with a sleep process of a subject are obtained, and a first sleep assessment result of the subject is determined based on the multiple sleep indicators. A sleep signal corresponding to the sleep process is obtained, and a second sleep assessment result of the subject is determined based on the sleep signal. A sleep quality of the sleep process is determined based on the first sleep assessment result and the second sleep assessment result.

Abstract: The embodiments of the disclosure provide a method for evaluating a health condition indicator of a subject, a host, and a computer readable storage medium. The method includes: obtaining a reference heart rate of the subject and determining a heart rate indicator of the subject according to the reference heart rate; determining a fitness indicator of the subject based on physiological information of the subject; determining an age indicator of the subject based on an age of the subject; and determining the health condition indicator of the subject at least based on the heart rate indicator, the fitness indicator, and the age indicator.

Abstract: The disclosure provides a wearing detection method, a wearable device, and a computer readable storage medium. The method includes: in response to determining that the wearable device is in a static state, emitting a plurality of first photoplethysmography (PPG) signals, and detecting a plurality of second PPG signals corresponding to the first PPG signals; obtaining a zero-crossing rate corresponding to the second PPG signals; and in response to determining that the zero-crossing rate of the second PPG signals is higher than a reference threshold, determining that the wearable device is in an unworn state.

Abstract: The disclosure provides a movement determination method, a movement determination device, and a computer-readable storage medium. The method includes the following. In response to determining that a user has presented an initial movement corresponding to a to-be-detected movement, an anchor joint point among multiple joint points of the user is decided based on the to-be-detected movement. A movable range of each of the anchor joint points is decided. In response to determining that each of the anchor joint points leaves the corresponding movable range, it is determined that the user has stopped performing the to-be-detected movement.

Abstract: An exercise monitoring method, an exercise monitoring device, and a computer-readable storage medium are provided. The method includes the following: obtaining an exercise course input by a user; detecting a reference respiratory pattern of the user performing the exercise course through a wearable device within a reference time interval; detecting a first respiratory pattern of the user performing the exercise course through the wearable device within a first time interval; and providing a first exercise adjustment suggestion based on a first comparison result between the first respiratory pattern and the reference respiratory pattern.

Abstract: Embodiments of the disclosure provide a heart rate estimation method and device and a computer-readable storage medium. The method includes: obtaining a reference photoplethysmography (PPG) spectrum corresponding to a t-th time point; obtaining a previous heart rate and a motion energy parameter corresponding to the t-th time point, and accordingly determining a predicted heart rate corresponding to the t-th time point; determining a reference mask based on the predicted heart rate and the motion energy parameter of the t-th time point; determining a specific PPG spectrum based on the reference mask and the reference PPG spectrum; and estimating a heart rate corresponding to the t-th time point based on the specific PPG spectrum.

Abstract: A method and a device for predicting sports performance, and a computer readable storage medium are provided. The method includes: collecting motion data of a user in a process of the user performing a specific action through a wearable device; and in response to determining that the motion data conforms to action characteristics of the specific action, predicting at least one sports performance of the user performing at least one other action based on the motion data.

Abstract: The present invention discloses a method for determining a maximum value of a heart rate data of a user performing a physical activity. Acquire first heart rate data in a first duration of the physical activity performed by the user. Acquire motion data in the first duration of the physical activity performed by the user. Calculate second heart rate data based on the motion data in the first duration of the physical activity performed by the user by a mathematical model and estimate the maximum value of the heart rate data of the user based on a comparison between the first first heart rate data and the second heart rate data.

Abstract: The present invention discloses a method for determining a degree of response to a physical activity. Acquire a physical activity signal measured by a sensing unit in the physical activity. Determine first data of a first physical activity feature set based on the physical activity signal. Determine a recognition of the degree of response to the physical activity based on the first data of the first physical activity feature set by a mathematical model describing a relationship between the first physical activity feature set and the degree of response to a physical activity. A portion of a first mechanism of the mathematical model adopts at least one portion of a second mechanism of a first neural network model associated with the second physical activity feature set.

Abstract: The embodiments of the disclosure provide a method for adjusting a noise cancellation mode, an electronic device, and a computer readable storage medium. The method includes: estimating a noise perception indicator corresponding to an environmental noise; and adjusting a noise cancellation mode of an wearable audio device according to the noise perception indicator.

Abstract: The disclosure provides a movement determination method, a movement determination device, and a computer-readable storage medium. The method includes the following. In response to determining that a user has presented an initial movement corresponding to a to-be-detected movement, an anchor joint point among multiple joint points of the user is decided based on the to-be-detected movement. A movable range of each of the anchor joint points is decided. In response to determining that each of the anchor joint points leaves the corresponding movable range, it is determined that the user has stopped performing the to-be-detected movement.

Abstract: Embodiments of the disclosure provide a method, a device, and a computer-readable storage medium for arranging exercise intensity. The method includes: obtaining a user’s personal information and an exercise session, where the exercise session corresponds to an exercise fatigue; and providing a recommended exercise intensity for the exercise session based on at least the personal information and the exercise fatigue.

Abstract: The disclosure provides a wearing detection method, a wearable device, and a computer readable storage medium. The method includes: in response to determining that the wearable device is in a static state, emitting a plurality of first photoplethysmography (PPG) signals, and detecting a plurality of second PPG signals corresponding to the first PPG signals; obtaining a zero-crossing rate corresponding to the second PPG signals; and in response to determining that the zero-crossing rate of the second PPG signals is higher than a reference threshold, determining that the wearable device is in an unworn state.