EP-4740842-A1 - RESTING METABOLIC RATE MEASUREMENT METHOD, SYSTEM, AND MEDIUM

EP4740842A1EP 4740842 A1EP4740842 A1EP 4740842A1EP-4740842-A1

Abstract

Provided are a resting metabolic rate detection method and system, and a medium, to detect a resting metabolic rate more accurately by using a wearable device. In the method, the wearable device can collect at least one parameter among exercise data, a physiological parameter, a psychological parameter, and a sleep parameter, so that a more accurate resting metabolic rate can be obtained through detection based on impact of the collected parameter on the resting metabolic rate. In addition, a user interface can be further displayed based on the resting metabolic rate obtained through detection, and a user can be notified of change information about the resting metabolic rate through the user interface, so that the user can be guided to adjust an exercise plan, a dietary plan, a fat loss plan, or the like, thereby implementing more accurate and timely adjustment.

Inventors

YUAN, Jili
ZHAO, SHUAI
REN, Huichao
CAO, YU
QI, Biao

Assignees

Huawei Technologies Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20240528

Claims (20)

A resting metabolic rate detection method, comprising: collecting, by a wearable device, at least one parameter among exercise data, a physiological parameter, a psychological parameter, and a sleep parameter of a user; performing, by the wearable device, detection based on the at least one parameter, to obtain a resting metabolic rate; and displaying, by the wearable device, a first user interface based on the resting metabolic rate, wherein the first user interface is configured to indicate change information of the resting metabolic rate.
The method according to claim 1, wherein the wearable device collects the exercise data of the user; and performing, by the wearable device, detection based on the exercise data, to obtain the resting metabolic rate comprises: determining exercise duration and an average heart rate during exercise based on the exercise data; determining, based on the exercise duration, the average heart rate, and a preset matching relationship, exercise intensity that matches the exercise data, wherein the preset matching relationship indicates an exercise duration range and an average heart rate range that match different exercise intensity; and determining the resting metabolic rate based on the exercise intensity and the exercise duration, wherein the resting metabolic rate indicates a resting metabolic rate after the exercise.
The method according to claim 2, wherein determining the resting metabolic rate based on the exercise intensity and the exercise duration comprises: determining incremental information of the resting metabolic rate by using a pre-trained multi-parameter regression model with the exercise intensity and the exercise duration as inputs; and determining the resting metabolic rate after the exercise based on a resting metabolic rate before the exercise and the incremental information.
The method according to any one of claims 1 to 3, wherein the exercise data comprises at least one of a photoplethysmography PPG signal, an accelerometer ACC signal, and an inertial measurement unit IMU.
The method according to any one of claims 1 to 4, wherein the first user interface comprises an hourly, daily, weekly, monthly, or yearly resting metabolic rate and change information thereof, wherein in a case that the first user interface comprises the hourly resting metabolic rate, when the wearable device collects the exercise data, the resting metabolic rate is higher than a resting metabolic rate obtained before the exercise data is collected.
The method according to any one of claims 1 to 4, wherein the first user interface comprises at least one of daily calorie expenditure, daily calorie intake, and a daily calorie deficit, and the daily calorie deficit is determined based on a difference between the daily calorie expenditure and the daily calorie intake.
The method according to any one of claims 1 to 6, wherein before performing detection to obtain the resting metabolic rate, the method further comprises: displaying, by the wearable device, a second user interface, wherein the second user interface is configured to guide the user to input body composition information, and the second user interface comprises a first control and a second control; and obtaining, by the wearable device in response to a first operation on the first control, the body composition information measured by a body fat scale; or displaying, by the wearable device in response to a second operation on the second control, at least one input interface, wherein the at least one input interface is configured to manually input at least one body composition parameter comprised in the body composition information; and performing, by the wearable device, detection based on the at least one parameter, to obtain the resting metabolic rate comprises: performing, by the wearable device, detection based on the at least one parameter and the body composition parameter, to obtain the resting metabolic rate.
The method according to any one of claims 1 to 7, wherein after performing detection to obtain the resting metabolic rate, the method further comprises: displaying a third user interface based on the resting metabolic rate and a historical resting metabolic rate when a decrease of the resting metabolic rate meets a preset condition, wherein the third user interface is configured to alert the user to the decrease of the resting metabolic rate.
The method according to claim 8, wherein the wearable device stores daily calorie intake of the user, and when the daily calorie intake is less than a calorie intake threshold, the third user interface is further configured to make an alert for an excessive dieting state.
The method according to claim 8 or 9, wherein when the wearable device detects that there is an ongoing fat loss plan, the third user interface is further configured to make an alert for a fat loss plateau.
The method according to any one of claims 1 to 10, wherein after performing detection to obtain the resting metabolic rate, the method further comprises: sending the resting metabolic rate to a terminal device, wherein the terminal device is configured to display a fourth user interface based on the resting metabolic rate, and the fourth user interface is configured to indicate the change information of the resting metabolic rate.
The method according to any one of claims 1 to 11, wherein the physiological parameter comprises at least one of the following parameters: a resting heart rate, resting heart rate variability, a body temperature, a skin temperature, and a respiratory rate.
The method according to any one of claims 1 to 12, wherein the psychological parameter comprises at least one of the following parameters: a stress score and a stress level.
The method according to any one of claims 1 to 13, wherein the sleep parameter comprises at least one of the following parameters: sleep time, sleep duration, sleep quality, and deep sleep continuity.
The method according to any one of claims 1 to 14, wherein displaying, by the wearable device, the first user interface based on the resting metabolic rate comprises: displaying, by the wearable device, the first user interface based on the resting metabolic rate and the at least one parameter, wherein the first user interface is further configured to indicate a parameter that affects the change information of the resting metabolic rate.
A wearable device, comprising one or more processors and one or more memories, wherein the one or more memories are configured to store one or more computer programs and data information, wherein the one or more computer programs comprise instructions; and when the instructions are executed by the one or more processors, the wearable device is enabled to perform the method according to any one of claims 1 to 15.
A resting metabolic rate detection system, comprising the wearable device according to claim 16 and a terminal device.
The system according to claim 17, further comprising a body fat scale.
A computer-readable storage medium, wherein the computer-readable storage medium is configured to store a computer program, and when the computer program is run on a computer, the computer is enabled to perform the method according to any one of claims 1 to 15.
A computer program product, comprising a computer program, wherein when the computer program is run on a computer, the computer is enabled to perform the method according to any one of claims 1 to 15.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202311156639.4, filed with the China National Intellectual Property Administration on September 7, 2023 and entitled "RESTING METABOLIC RATE DETECTION METHOD AND SYSTEM, AND MEDIUM", which is incorporated herein by reference in its entirety. TECHNICAL FIELD Embodiments of this application relate to the field of smart life technologies, and in particular, to a resting metabolic rate detection method and system, and a medium. BACKGROUND A resting metabolic rate is an amount of energy consumed by a body at rest to sustain daily basic physiological activities (such as heartbeat, breathing, glandular secretion, blood circulation, and body temperature maintenance). The resting metabolic rate accounts for the highest proportion of total daily energy consumption of the body, reaching 60% to 75%. The resting metabolic rate is associated with factors including weight, body composition, gender, age, height, and the like, and is also affected by physical activity levels, exercise, dietary conditions (such as dieting and starvation), disease, medication, ambient temperature, genetic predisposition, and the like. Therefore, the resting metabolic rate actually changes dynamically. How to implement dynamic detection on the resting metabolic rate holds important research significance. SUMMARY Embodiments of this application provide a resting metabolic rate detection method and system, and a medium, to detect a resting metabolic rate more accurately by using a wearable device, so that a change of the resting metabolic rate can be assessed in time, and a user can more accurately know the resting metabolic rate of a body and then perform corresponding adjustment. According to a first aspect, an embodiment of this application provides a resting metabolic rate detection method. The method includes the following steps: A wearable device collects at least one parameter among exercise data, a physiological parameter, a psychological parameter, and a sleep parameter of a user; the wearable device performs detection based on the at least one parameter, to obtain a resting metabolic rate; and the wearable device displays a first user interface based on the resting metabolic rate, where the first user interface is configured to indicate change information of the resting metabolic rate. In the method, the wearable device collects various types of parameters in various scenarios, implementing assessment of a change of the resting metabolic rate from a plurality of aspects, so that a more accurate resting metabolic rate can be obtained through detection. In addition, a user interface can be displayed based on the dynamically changing resting metabolic rate obtained through detection, so that the user can be guided, through the user interface, to adjust an exercise plan, a dietary plan, a fat loss plan, or the like, thereby implementing more accurate and timely adjustment of a physical status. In a possible implementation, the wearable device collects the exercise data of the user; and that the wearable device performs detection based on the exercise data, to obtain the resting metabolic rate includes: determining exercise duration and an average heart rate during exercise based on the exercise data; determining, based on the exercise duration, the average heart rate, and a preset matching relationship, exercise intensity that matches the exercise data, where the preset matching relationship indicates an exercise duration range and an average heart rate range that match different exercise intensity; and determining the resting metabolic rate based on the exercise intensity and the exercise duration, where the resting metabolic rate indicates a resting metabolic rate after the exercise. In this implementation, the wearable device may more accurately detect the resting metabolic rate after the exercise based on the collected exercise data. In this way, based on more accurate data of the resting metabolic rate, the user can be guided more accurately for a scenario such as an exercise plan or a fat loss plan. For example, the user may be alerted to additional exercise to increase the resting metabolic rate, thereby promoting achievement of a calorie deficit, and achieving objectives of fat loss and the like. In another example, the user may be alerted to exercise reduction to avoid physical injury and the like. Compared with determining a resting metabolic rate only based on basic information such as age, gender, height, and weight, this scenario can implement more accurate detection of a resting metabolic rate, and can implement more accurate identification of an increase of a resting metabolic rate after exercise. In a possible implementation, determining the resting metabolic rate based on the exercise intensity and the exercise duration includes: determining incremental information of the resting metabolic rate by using a pre-trained