Data processing method, first wearable device and computer-readable storage medium

Abstract

This application discloses a data processing method, a first wearable device, and a computer-readable storage medium. The data processing method is applied to a wearable system, which includes at least two wearable devices worn on a user's body, each wearable device including a sensor module. The data processing method includes: initiating the same type of exercise on the at least two wearable devices; acquiring motion data collected by the sensor modules in each wearable device; and, based on the exercise type and a preset strategy, acquiring the user's exercise metrics from the motion data of the at least two wearable devices.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a data processing method, a first wearable device, and a computer-readable storage medium. Background Technology

[0002] With the widespread adoption of wearable sports devices such as smartwatches, heart rate monitors, and sports headphones, the types of data recorded during exercise are becoming increasingly diverse. This data, after being processed by algorithms, can generate advanced sports metrics, such as heart rate zones and exercise load. While the use or wearing of multiple smart devices during exercise is already evident among professional athletes—for example, the combination of a heart rate monitor and a smartwatch, or a sports headphone and a smartwatch—currently, the acquisition of sports metrics typically relies on a single device's data source, resulting in relatively low accuracy. Summary of the Invention

[0003] This application provides a data processing method, a first wearable device, and a computer-readable storage medium.

[0004] This application provides a data processing method. The data processing method is applied to a wearable system, which includes at least two wearable devices worn on a user's body, each wearable device including a sensor module. The data processing method includes: at least two wearable devices activating the same type of exercise; acquiring motion data collected by the sensor module in each wearable device; and, based on the exercise type and a preset strategy, acquiring the user's exercise metrics from the motion data of the at least two wearable devices.

[0005] In some implementations, in conjunction with the exercise type and based on a preset strategy, the user's exercise index is obtained from the exercise data of at least two wearable devices. This includes: determining the attributes of the exercise index, wherein the attributes of the exercise index include proprietary indicators and shared indicators, the proprietary indicators being those output by one of the wearable devices under the exercise type, and the shared indicators being those output by at least two wearable devices under the exercise type, with different attributes of the exercise index having corresponding preset strategies; selecting a preset strategy corresponding to the attributes of the exercise index; and obtaining the user's exercise index based on the selected preset strategy and the exercise data of at least two wearable devices.

[0006] In some implementations, when the exercise metric is the proprietary metric, the selected preset strategy is a supplementary strategy. Based on the selected preset strategy, obtaining the user's exercise metric from the exercise data of at least two wearable devices includes: filtering exercise data from the exercise data of one of the wearable devices for obtaining the proprietary metric as dedicated data; filtering exercise data from the exercise data of the other wearable devices to assist in obtaining the proprietary metric as supplementary data based on the supplementary strategy; and obtaining the proprietary metric based on the dedicated data and the supplementary data.

[0007] In some embodiments, the wearable device includes headphones and a smartwatch. When the exercise metric is the proprietary metric, the selected preset strategy includes a supplementary strategy. Based on the preset strategy and the exercise type, the user's exercise metric is obtained from the exercise data of at least two wearable devices, including: when the headphones need to output the proprietary metric, filtering exercise data from the headphones' exercise data for obtaining the proprietary metric as dedicated data; filtering exercise data from the smartwatch's exercise data to assist in obtaining the proprietary metric as supplementary data based on the supplementary strategy; and obtaining the proprietary metric based on the dedicated data and the supplementary data.

[0008] In some embodiments, the type of exercise includes swimming, the sensor module includes an accelerometer and a gyroscope, the motion data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary metric includes the freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data from the headphones, and the supplementary data includes acceleration data and / or angular velocity data from the smartwatch. Obtaining the proprietary metric based on the dedicated data and the supplementary data includes: when the swimming style is freestyle, obtaining the occurrence time and frequency of the user's glides based on the acceleration data and / or angular velocity data from the smartwatch; obtaining the gliding phase of the freestyle stroke based on the occurrence time and frequency of the glides; and obtaining the freestyle roll angle based on the acceleration data and / or angular velocity data during the gliding phase.

[0009] In some implementations, when the exercise metric is the shared metric, the selected preset strategy is a coverage strategy. Based on the selected preset strategy, obtaining the user's exercise metric from the exercise data of at least two wearable devices includes: filtering exercise data from the exercise data of one of the wearable devices to obtain the shared metric as first data; obtaining a first metric based on the first data; filtering exercise data from the exercise data of the other wearable devices to obtain the shared metric as second data; obtaining a second metric based on the second data; determining which of the first metric or the second metric is more accurate based on the exercise type; and overwriting the more accurate one of the first metric and the second metric with the other to obtain the shared metric.

[0010] In some implementations, when the exercise metric is a shared metric, the selected preset strategy is a selection strategy. Based on the selected preset strategy, obtaining the user's exercise metric from the exercise data of at least two wearable devices includes: determining, based on the exercise type, which of the exercise data from one of the wearable devices and the other wearable devices is more accurate for calculating the shared metric; and calculating the shared metric using the exercise data from the wearable device that can more accurately calculate the shared metric.

[0011] In some implementations, when the exercise metric is the shared metric, the selected preset strategy is a fusion strategy. Based on the selected preset strategy, obtaining the user's exercise metric from the exercise data of at least two wearable devices includes: filtering exercise data from the exercise data of one of the wearable devices to obtain the shared metric as first data; filtering exercise data from the exercise data of the other wearable devices to obtain the shared metric as second data; and obtaining the shared metric based on the first data and the second data.

[0012] In some embodiments, the sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the wearable device includes headphones and a smartwatch, the type of exercise includes rope skipping, the shared metric includes the number of rope skips, and the selected preset strategy is a fusion strategy. Based on the selected preset strategy, the user's motion metric is obtained from the motion data of at least two wearable devices, including: filtering acceleration data from the motion data of the headphones as first data; filtering acceleration data from the motion data of the smartwatch as second data; and obtaining the number of rope skips based on the first data and the second data.

[0013] In some embodiments, obtaining the number of jump ropes based on the first data and the second data includes: processing the acceleration data of the headphones to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones; processing the acceleration data of the smartwatch to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the smartwatch; and obtaining the number of jump ropes based on the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones and the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the smartwatch.

[0014] In some embodiments, the data processing method is further used to: assess the user's exercise ability based on the exercise index to output an assessment result; and output exercise suggestions based on the assessment result.

[0015] This application provides a first wearable device applied in a wearable system. The wearable system includes multiple wearable devices worn on a user's body. The first wearable device includes a first sensor module and a control module. The first sensor module is used to collect motion data. The control module is used to control the first wearable device to activate a type of exercise, and at least one second wearable device in the wearable system simultaneously activates the same type of exercise. The second wearable device includes a second sensor module. The control module is further used to: acquire motion data collected by the first sensor module and the second sensor module; and, based on the exercise type and a preset strategy, obtain the user's exercise indicators according to the motion data from the first wearable device and the second wearable device.

[0016] In some implementations, the first wearable device is an earphone, and the second wearable device is a smartwatch.

[0017] In some implementations, the first wearable device is a smartwatch, and the second wearable device is headphones.

[0018] In some embodiments, the control module is further configured to: determine the attributes of the exercise index, the attributes of the exercise index including proprietary indexes and shared indexes, the proprietary indexes being exercise indexes that are specific to one of the wearable devices under the exercise type, the shared indexes being exercise indexes that can be output by at least two wearable devices under the exercise type, and the exercise indexes with different attributes having corresponding preset strategies; select a preset strategy corresponding to the attributes of the exercise index; and based on the selected preset strategy, obtain the user's exercise index according to the exercise data of at least two wearable devices.

[0019] In some implementations, when the motion index is the proprietary index, the selected preset strategy is a supplementary strategy, and the control module is further configured to: filter motion data from the motion data of the first wearable device for obtaining the proprietary index as dedicated data; based on the supplementary strategy, filter motion data from the motion data of the second wearable device for assisting in obtaining the proprietary index as supplementary data; and obtain the proprietary index according to the dedicated data and the supplementary data.

[0020] In some embodiments, the first wearable device includes headphones, and the second wearable device includes a smartwatch. When the exercise metric is the proprietary metric, the selected preset strategy includes a supplementary strategy. The control module is further configured to: when the headphones need to output the proprietary metric, filter out exercise data from the headphones' exercise data for obtaining the proprietary metric as dedicated data; based on the supplementary strategy, filter out exercise data from the smartwatch's exercise data for assisting in obtaining the proprietary metric as supplementary data; and obtain the proprietary metric based on the dedicated data and the supplementary data.

[0021] In some embodiments, the type of exercise includes swimming, the sensor module includes an accelerometer and a gyroscope, the motion data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary indicator includes the freestyle roll angle, the proprietary data includes acceleration data and / or angular velocity data from the headphones, and the supplementary data includes acceleration data and / or angular velocity data from the smartwatch. The control module is further configured to: when the swimming stroke is freestyle, obtain the occurrence time and frequency of the user's gliding strokes based on the acceleration data and / or angular velocity data from the smartwatch; obtain the gliding phase of the freestyle stroke based on the occurrence time and frequency of the gliding strokes; and obtain the freestyle roll angle based on the acceleration data and / or angular velocity data of the gliding phase.

[0022] In some implementations, when the exercise indicator is the common indicator, the selected preset strategy is a coverage strategy. The control module is further configured to: filter exercise data from the exercise data of the first wearable device for obtaining the common indicator, as first data; obtain a first indicator based on the first data; filter exercise data from the second wearable device for obtaining the common indicator, as second data; obtain a second indicator based on the second data; determine which of the first indicator or the second indicator is more accurate based on the exercise type and the common indicator; and overwrite the other with the more accurate first indicator, as the common indicator.

[0023] In some implementations, when the motion index is the common index, the selected preset strategy is an optimization strategy. The control module is further configured to: determine, based on the motion type and the common index, which of the motion data from the first wearable device and the second wearable device is more accurate in calculating the common index; and calculate the common index using the motion data from the wearable device that can more accurately calculate the common index.

[0024] In some implementations, when the motion index is the common index, the selected preset strategy is a fusion strategy. The control module is further configured to: filter motion data for obtaining the common index from the motion data of one of the wearable devices as first data; filter motion data for obtaining the common index from the motion data of the other wearable devices as second data; and obtain the common index based on the first data and the second data.

[0025] In some embodiments, the sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the first wearable device includes headphones, the second wearable device includes a smartwatch, the type of exercise includes rope skipping, the common indicator includes the number of rope skips, and the selected preset strategy is a fusion strategy. The control module is further configured to: filter acceleration data from the motion data of the headphones as first data; filter acceleration data from the motion data of the smartwatch as second data; and obtain the number of rope skips based on the first data and the second data.

[0026] In some embodiments, the control module is further configured to: process the acceleration data of the headphones to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones; process the acceleration data of the smartwatch to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the smartwatch; and obtain the number of jump ropes based on the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones and the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the smartwatch.

[0027] In some implementations, the control module is further configured to: assess the user's exercise ability based on the exercise index, and output an assessment result; and output exercise suggestions based on the assessment result.

[0028] This application also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the data processing method described in any of the above embodiments.

[0029] The data processing method, the first wearable device, and the computer-readable storage medium provided in this application acquire motion data collected by sensor modules in two wearable devices, which is more comprehensive than motion data collected by sensor modules in a single wearable device. The motion data from the two wearable devices can also be used as a redundancy design to ensure the source of the motion data. The data processing method can acquire the user's motion indicators based on a preset strategy and the motion data from at least two wearable devices. The motion data from the two wearable devices can be mutually verified to reduce errors and improve the accuracy of the motion indicators.

[0030] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:

[0032] Figure 1 This is a flowchart illustrating the data processing method of this application;

[0033] Figure 2 This is a schematic diagram of the interaction structure of wearable devices in a wearable system according to certain embodiments of this application;

[0034] Figure 3 This is a schematic diagram of the algorithm for the data processing method of some embodiments of this application;

[0035] Figure 4 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0036] Figure 5 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0037] Figure 6 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0038] Figure 7 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0039] Figure 8 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0040] Figure 9 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0041] Figure 10 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0042] Figure 11 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0043] Figure 12 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0044] Figure 13 This is a flowchart illustrating some embodiments of the data processing method of this application;

[0045] Figure 14 This is a schematic diagram illustrating the connection state of a computer-readable storage medium and a processor according to certain embodiments of this application.

[0046] Explanation of key component symbols:

[0047] Wearable System 100

[0048] First wearable device 10; first sensor module 11; control module 13; second wearable device 30; second sensor module 31

[0049] Processor 40;

[0050] Computer-readable storage medium 200; program 202. Detailed Implementation

[0051] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0052] This application provides a first wearable device 10 (such as...) Figure 2 As shown), data processing methods (such as...) Figure 1 , Figures 3 to 13 (as shown), and computer-readable storage medium 200 (such as...) Figure 14 (As shown).

[0053] Please see Figures 1 to 3Wearable devices are portable devices worn directly on the body or integrated into a user's clothing or accessories. Wearable devices can achieve various functions such as data transmission and analysis through software support and data interaction, including but not limited to headphones, smartwatches, smart sports straps, smart glasses, and smart helmets. Wearable system 100 includes multiple wearable devices worn on the user's body. The multiple wearable devices in wearable system 100 can communicate with each other to achieve data transmission.

[0054] The wearable system 100 includes multiple sensor modules (e.g., a first sensor module 11 and a second sensor module 31), which are located within wearable devices (e.g., a first wearable device 10 and a second wearable device 30). These sensor modules can monitor and collect signals from the user while wearing the wearable device in real time, and transmit the resulting motion data to the control module 13. When the user is active while wearing the wearable device, the sensor modules can come into contact with the user's body to collect relevant motion data. For example, sensor modules on the wearable device (such as electromyography sensors) can come into contact with different parts of the body, such as the arms, abdomen, and back, to capture electromyographic activity signals from these areas. Additionally, sensor modules on the wearable device (such as accelerometers or gyroscopes) can come into contact with multiple limbs (such as the upper arm, forearm, thigh, and calf) or joints (such as the wrist, ankle, and knee) to obtain motion data representing the posture and dynamic changes of these areas.

[0055] The wearable system 100 includes a control module 13. The control module 13 is responsible for processing various data and coordinating various functions (including but not limited to audio processing, device connectivity, power management, and user interaction). The control module 13 communicates with sensor modules of multiple wearable devices. The control module 13 and the sensor modules can establish a wired communication connection via data lines or a wireless communication connection via wireless signals.

[0056] In the wearable system 100, each wearable device is equipped with a sensor module. The control module 13 can be located in one of the wearable devices; that is, in the wearable system 100, at least one wearable device is equipped with both a control module 13 and a sensor module. In the embodiments of this application, the wearable device equipped with the control module 13 is the first wearable device 10, and the corresponding sensor module is the first sensor module 11. The wearable device without the control module 13 is the second wearable device 30, and the corresponding sensor module is the second sensor module 31. It is understood that the wearable system 100 may include multiple second wearable devices.

[0057] For example, please refer to Figure 2In one embodiment of the wearable system 100, which includes two wearable devices, the first wearable device 10 is an earphone, which includes a first sensor module 11 and a control module 13. The second wearable device 30 is a smartwatch, which includes a second sensor module 31. In some embodiments, the first wearable device 10 is a smartwatch and the second wearable device 30 is an earphone. An earphone is an audio device primarily used for sound-to-electrical conversion, such as converting audio signals into sound so that users can hear music, movies, games, or other audio content, or converting sound into electrical signals. Earphones are designed to provide a private listening environment, allowing users to enjoy audio content alone without disturbing those around them. Earphones can be worn on the user's ears and can be used with devices such as mobile phones, computers, wearable devices, head-mounted displays, and virtual reality devices. Earphones include air-conduction earphones and bone-conduction earphones. Air-conduction earphones, also known as air-conduction headphones, are earphones that transmit sound through air vibrations. Bone conduction headphones, also known as bone conduction headphones, are headphones that convert sound into different mechanical vibrations and transmit sound waves through the skull, bony labyrinth, inner ear fluid, cochlea, and auditory center. Air conduction headphones and bone conduction headphones can be used in various scenarios, increasing the versatility of headphones.

[0058] Please see Figures 1 to 3 The data processing method of this application is applied in a wearable system 100, which includes at least two wearable devices worn on a user's body, each wearable device including a sensor module. The data processing method includes:

[0059] 02: At least two wearable devices must be running the same type of exercise;

[0060] 04: Acquire motion data collected by the sensor modules in each wearable device; and

[0061] 06: Combine the type of exercise and, based on a preset strategy, obtain the user's exercise metrics from exercise data of at least two wearable devices.

[0062] The above data processing method can be applied to a wearable system 100. The wearable system 100 of this embodiment includes at least two wearable devices worn on a user's body. A first wearable device 10 includes a first sensor module 11 and a control module 13. The first sensor module 11 is used to collect motion data. The control module 13 is used to control the first wearable device 10 to activate a certain type of exercise. At least one second wearable device 30 in the wearable system 100 simultaneously activates the same type of exercise. The second wearable device 30 includes a second sensor module 31. The control module 13 is also used to: acquire motion data collected by the first sensor module 11 and the second sensor module 31; and, based on the exercise type and a preset strategy, acquire the user's exercise indicators according to the motion data from the first wearable device 10 and the second wearable device 30.

[0063] Specifically, in method 02, the type of exercise activated by the wearable device includes at least one of swimming, neck exercises, jumping, cycling, running, rope skipping, skiing, and mountain climbing. After the exercise is activated, the sensor modules (i.e., the first sensor module 11 and the second sensor module 31) begin collecting motion data. The sensor modules include, but are not limited to, one or more of inertial measurement units (IMUs), pressure sensors, electrocardiogram (ECG) sensors, and temperature sensors. The motion data includes, but is not limited to, one or more of triaxial acceleration, triaxial angular acceleration, pressure, heart rate, and temperature. The motion data collected by the sensor modules can be data related to the human body (e.g., heart rate) or data related to the external environment (e.g., ambient temperature). The wearable device of this application includes at least one sensor module. In embodiments where the wearable device includes multiple sensor modules, the types of motion data collected by different sensor modules can be the same or different. The motion data formed by the signal can characterize the user's current state. The sensor modules collect motion data in ways including, but not limited to, sampling at a constant sampling rate, sampling at a variable sampling rate, and sampling within a time window. In this application, the sensor samples at a constant sampling rate; more specifically, this application acquires a set of sampled data at one-second intervals. A constant sampling rate is simple and convenient, and can continuously collect motion data, enabling continuous monitoring and minimizing the risk of missing state changes in the motion data.

[0064] In method 04, the control module 13 can acquire the detection data collected by the sensor module in several ways: the control module 13 can directly read the detection data collected by the sensor module; the control module 13 can periodically send requests to poll the sensor module for the latest detection data; or the sensor module and the control module 13 can be wirelessly connected, with the control module 13 acquiring the detection data collected by the sensor module through a wireless communication protocol. The methods by which the control module 13 acquires the motion data collected by the first sensor module 11 and the second sensor module 31 can differ.

[0065] In method 06, the preset strategy is the algorithm in control module 13 that obtains exercise indicators based on exercise type and exercise data. Exercise indicators are parameters characterizing a user's athletic ability. For example, in the case of swimming, the exercise indicators include at least one of the following: swimming stroke, percentage of each stroke, breathing frequency, freestyle breathing angle, freestyle maximum breathing angle, freestyle pitch angle, breaststroke breathing angle, breaststroke maximum breathing angle, total gliding time, and total duration. For example, in the case of cervical spine movement, the exercise indicators include at least one of the following: head left rotation angle, head right rotation angle, head forward tilt angle, head backward tilt angle, head left tilt angle, and head right tilt angle. As another example, in the case of jumping, the exercise indicator includes jump height. The user's exercise indicators are obtained based on exercise data from at least two wearable devices; that is, exercise data from at least two wearable devices participate in the acquisition of exercise indicators. It is understood that different exercise indicators may have the same or different preset strategies.

[0066] The data processing method of this application acquires motion data collected by sensor modules in two wearable devices, which is more comprehensive than motion data collected by sensor modules in a single wearable device. The motion data from the two wearable devices can also be used as a redundancy design to ensure the source of the motion data. The data processing method can acquire the user's motion index based on a preset strategy and the motion data from at least two wearable devices. The motion data from the two wearable devices can be mutually verified to reduce errors and improve the accuracy of the motion index.

[0067] Please see Figures 1 to 4 In some implementations, 06: Combining the type of exercise with a preset strategy, the user's exercise metrics are obtained based on exercise data from at least two wearable devices, including:

[0068] 061: Determine the attributes of the exercise index. The attributes of the exercise index include proprietary indexes and shared indexes. Proprietary indexes are exercise indexes that are exclusive to one wearable device under a certain exercise type. Shared indexes are exercise indexes that can be output by at least two wearable devices under a certain exercise type. Exercise indexes with different attributes have corresponding preset strategies.

[0069] 063: Select a preset strategy corresponding to the attributes of the exercise index; and

[0070] 065: Based on the selected preset strategy, obtain the user's exercise metrics from the exercise data of at least two wearable devices.

[0071] The above data processing method can be applied to the first wearable device 10. The control module 13 is further used to: determine the attributes of the motion index, the attributes of the motion index include proprietary indexes and shared indexes. Proprietary indexes are motion indexes that are exclusive to one wearable device under a certain type of motion, and shared indexes are motion indexes that can be output by at least two wearable devices under a certain type of motion. Motion indexes with different attributes have corresponding preset strategies; select a preset strategy corresponding to the attributes of the motion index; and obtain the user's motion index based on the selected preset strategy and the motion data of at least two wearable devices.

[0072] For example, for both headphones and smartwatches, proprietary metrics include freestyle roll angle, output by the headphones, while common metrics include body temperature, which can be output by both the headphones and the smartwatch. It is understood that the first wearable device 10 and the second wearable device 30 acquire various types of motion data. When the control module 13 selects a preset strategy corresponding to the attributes of the motion metrics, the preset strategy is used to acquire the user's motion metrics based on the motion data from the two wearable devices. The preset strategy is used at least to filter the motion data for acquiring the user's motion metrics. The preset strategy is a method for acquiring motion metrics based on historical datasets and motion behavior standard data. The historical dataset is a dataset formed by the motion data of different users collected by the sensor modules of different wearable devices. It has a large data volume, many data samples (i.e., users), and broad coverage, which can improve the accuracy of acquiring motion metrics. The motion behavior standard data comes from reference standards for different types of sports, such as using the performance data of professional athletes as reference standards. For example, in swimming, different swimming strokes correspond to different reference standards, including but not limited to freestyle breathing angle, breaststroke breathing angle, breaststroke gliding time, and breathing frequency. In running, reference standards include, but are not limited to, cadence, stride length, heart rate, and oxygen consumption.

[0073] By distinguishing between proprietary and shared indicators, the control module 13 can select corresponding preset strategies for motion indicators with different attributes, thereby improving the targeting and efficiency of motion indicator processing.

[0074] Please see Figure 2 and Figure 5In some implementations, when the exercise metric is a proprietary metric, the selected preset strategy is a supplementary strategy. 065: Based on the selected preset strategy, the user's exercise metric is obtained from exercise data from at least two wearable devices, including:

[0075] 0651: Select motion data from the motion data of one of the wearable devices to obtain proprietary metrics, and use it as proprietary data;

[0076] 0652: Based on a supplementary strategy, select motion data from other wearable devices to assist in obtaining proprietary metrics, serving as supplementary data; and

[0077] 0653: Obtain proprietary indicators based on dedicated data and supplementary data.

[0078] The above data processing method can be applied to the first wearable device 10. When the motion index is a proprietary index, the selected preset strategy is a supplementary strategy. The control module 13 is also used to: filter motion data from the motion data of the first wearable device 10 for obtaining proprietary indexes as dedicated data; based on the supplementary strategy, filter motion data from the motion data of the second wearable device 30 for assisting in obtaining proprietary indexes as supplementary data; and obtain proprietary indexes based on dedicated data and supplementary data.

[0079] Different wearable devices may use different types of sensor modules, and the parts of the body they are worn on may also differ. Therefore, the accuracy of motion data acquired by the sensors for different parts of the body will vary. In an implementation where the preset strategy selected by the control module 13 is a supplementary strategy, the control module 13 acquires proprietary indicators based on dedicated data filtered from the motion data of one wearable device, the supplementary strategy, and the motion type. The supplementary data filtered from the motion data of another wearable device is used to assist in acquiring the dedicated data. That is, in the supplementary strategy, the control module 13 improves the accuracy of acquiring proprietary indicators by acquiring supplementary data to enhance the accuracy of the dedicated data filtered from the motion data of one wearable device.

[0080] Please see Figure 2 and Figure 6 In some implementations, the wearable devices include headphones and smartwatches. When the exercise metric is a proprietary metric, the selected preset strategy includes a supplementary strategy. 065: Combining the exercise type and based on a preset strategy, the user's exercise metric is obtained from exercise data from at least two wearable devices, including:

[0081] 06511: When the headphones need to output proprietary metrics, filter out the motion data used to obtain the proprietary metrics from the headphones' motion data and use it as dedicated data.

[0082] 06521: Based on a supplementary strategy, select exercise data from the smartwatch's exercise data to assist in obtaining proprietary metrics, serving as supplementary data; and

[0083] 06531: Obtain proprietary indicators based on dedicated data and supplementary data.

[0084] The above data processing method can be applied to a first wearable device 10, which includes headphones, and a second wearable device 30, which includes a smartwatch. When the motion indicator is a proprietary indicator, the selected preset strategy includes a supplementary strategy. The control module 13 is further configured to: when the headphones need to output proprietary indicators, filter motion data from the headphones' motion data to obtain the proprietary indicators, as dedicated data; based on the supplementary strategy, filter motion data from the smartwatch's motion data to assist in obtaining the proprietary indicators, as supplementary data; and obtain the proprietary indicators based on the dedicated data and the supplementary data.

[0085] Specifically, headphones and smartwatches are worn on different parts of the body. Therefore, the accuracy of motion data acquired by sensors for different parts of the body also varies. In an implementation where the preset strategy selected by control module 13 is a supplementary strategy, control module 13 acquires proprietary indicators based on dedicated data filtered from the headphone's motion data, the supplementary strategy, and the motion type. The supplementary data filtered from the smartwatch's motion data is used to assist in acquiring dedicated data. That is, in the supplementary strategy, control module 13 improves the accuracy of the dedicated data filtered from the headphone's motion data by acquiring the supplementary data from the smartwatch, thereby improving the accuracy of acquiring proprietary indicators. Specific implementation details are described below.

[0086] Please see Figure 2 and Figure 7 In some implementations, the type of exercise includes swimming; the sensor module includes an accelerometer and a gyroscope; the motion data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope; proprietary metrics include freestyle roll angle; dedicated data includes acceleration data and / or angular velocity data from the headphones; and supplementary data includes acceleration data and / or angular velocity data from the smartwatch. 06531: Proprietary metrics are obtained based on dedicated data and supplementary data, including:

[0087] 06533: When swimming in freestyle, the timing and frequency of the user's swipes are obtained based on the acceleration and / or angular velocity data of the smartwatch;

[0088] 06535: The gliding phase of freestyle swimming is determined by the timing and frequency of the glides.

[0089] 06537: Obtain the freestyle roll angle based on acceleration and / or angular velocity data during the gliding phase.

[0090] The above data processing method can be applied to the first wearable device 10, where the sports type includes swimming, the sensor module includes an accelerometer and a gyroscope, the motion data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, proprietary indicators include freestyle roll angle, dedicated data includes acceleration data and / or angular velocity data from the headphones, and supplementary data includes acceleration data and / or angular velocity data from the smartwatch. The control module 13 is also used to: when the swimming style is freestyle, obtain the occurrence time and frequency of the user's glides based on the acceleration data and / or angular velocity data from the smartwatch; obtain the gliding phase of freestyle swimming based on the occurrence time and frequency of the glides; and obtain the freestyle roll angle based on the acceleration data and / or angular velocity data of the gliding phase.

[0091] The accelerometer can collect and measure the acceleration of the head in three spatial dimensions (X, Y, and Z axes). That is, when a user is wearing headphones, the accelerometer can measure the three-axis acceleration (i.e., acceleration data) of the wearing part (e.g., the user's head). The three-axis acceleration includes X-axis acceleration, Y-axis acceleration, and Z-axis acceleration. X-axis acceleration represents the change in head velocity along the X-axis, Y-axis acceleration represents the change in head velocity along the Y-axis, and Z-axis acceleration represents the change in head velocity along the Z-axis. Therefore, three-axis acceleration includes acceleration in three spatial dimensions, and compared to single-axis acceleration, it can more comprehensively represent the user's acceleration.

[0092] A gyroscope measures the angular velocity (i.e., gyroscope data) of a worn part (such as a user's head) in three spatial dimensions (X, Y, and Z axes). The three-axis angular velocity includes the X-axis angular velocity, Y-axis angular velocity, and Z-axis angular velocity. The X-axis angular velocity represents the change in angle of head rotation around the X-axis, the Y-axis angular velocity represents the change in angle of head rotation around the Y-axis, and the Z-axis angular velocity represents the change in angle of head rotation around the Z-axis. Therefore, the three-axis angular velocity encompasses angular velocities in three spatial dimensions, and compared to single-axis angular velocity, it can comprehensively represent the user's angular velocity.

[0093] Specifically, when the control module 13 acquires the freestyle roll angle, the user's swimming process includes multiple gliding phases. The control module 13 needs to acquire the acceleration data and / or angular velocity data of the headphones during these multiple gliding phases as proprietary data. Therefore, the control module 13 needs to determine the time range corresponding to a single gliding phase, that is, the control module 13 needs to determine the time window corresponding to a single gliding phase.

[0094] Therefore, in the embodiments of this application, the control module 13 obtains the time window corresponding to a single gliding phase through the acceleration data and / or angular velocity data of the smartwatch. In the method of 06533, the control module 13 obtains the occurrence time and frequency of the gliding through the triaxial acceleration data of the smartwatch. In some other embodiments of this application, the control module 13 can also obtain the occurrence time and frequency of the user's gliding based on the angular velocity data. In still some embodiments of this application, the control module 13 can also obtain the occurrence time and frequency of the user's gliding based on both acceleration data and angular velocity data. In the method of 06535, the control module 13 further obtains the gliding phase of freestyle swimming based on the occurrence time of the gliding and the frequency of the gliding, thereby allowing the control module 13 to obtain the time window corresponding to a single gliding phase (during freestyle swimming).

[0095] In the method of 06537, the time window corresponding to a single gliding phase includes multiple sets of triaxial accelerations (i.e., acceleration data). The time window includes multiple predetermined time points, with each set of triaxial accelerations corresponding to one predetermined time point. Each set of triaxial accelerations includes at least X-axis, Y-axis, and Z-axis accelerations. The triaxial accelerations at multiple predetermined time points are fused separately to obtain a fused acceleration value at multiple predetermined time points. It is understood that the fusion process includes filtering and noise reduction, including but not limited to Kalman filtering, Gaussian filtering, and median filtering. Filtering can remove some noise from the triaxial accelerations, improving the accuracy of the fused acceleration value.

[0096] Within the time window T corresponding to a single glide phase, there are three-axis accelerations: a1 (a set of X, Y, and Z-axis accelerations ax1, ay1, and az1) at predetermined time t1, a2 (a set of X, Y, and Z-axis accelerations ax2, ay2, and az2) at predetermined time t2, a3 (a set of X, Y, and Z-axis accelerations ax3, ay3, and az3) at predetermined time t3, and so on, and an (a set of X, Y, and Z-axis accelerations axn, ayn, and azn) at predetermined time tn. After fusing the three-axis accelerations, the fused acceleration value A1 at predetermined time t1 is obtained, the fused acceleration value A2 at predetermined time t2 is obtained, the fused acceleration value A3 at predetermined time t3 is obtained, and so on, the fused acceleration value An at predetermined time tn is obtained. The fused acceleration value An carries more information and more accurately represents the user's freestyle roll angle information compared to the non-fused acceleration value. The fusion process also helps to eliminate occasional outliers or sudden large fluctuations, making the 06531 method more resistant to interference and robust.

[0097] A single gliding phase corresponds to a time window containing multiple sets of triaxial angular velocities (i.e., angular velocity data). The time window includes multiple predetermined time points, with each set of triaxial acceleration corresponding to one predetermined time point. Each set of triaxial angular velocities includes at least X-axis, Y-axis, and Z-axis angular velocities. The triaxial angular velocities at multiple predetermined time points are fused to obtain a fused angular velocity value for each predetermined time point. It is understood that the fusion process includes filtering and noise reduction, including but not limited to Kalman filtering, Gaussian filtering, and median filtering. Filtering can remove some noise from the triaxial angular velocities, improving the accuracy of the fused angular velocity value.

[0098] Within the time window T corresponding to a single glide phase, the triaxial angular velocities ω1 (a set of X, Y, and Z axis accelerations ωx1, ωy1, and ωz1) at predetermined time t1, ω2 (a set of X, Y, and Z axis accelerations ωx2, ωy2, and ωz2) at predetermined time t2, ω3 (a set of X, Y, and Z axis accelerations ωx3, ωy3, and ωz3) at predetermined time t3, ..., and ωm at predetermined time tm The angular velocities ωm (a set of X, Y, and Z axis accelerations ωxm, ωym, and ωzm) are fused separately. The fused angular velocities ω1, ω2, ω3, and so on, yield the fused angular velocity value Ωm at a predetermined time tm. The fused angular velocity Ωm carries more information and, compared to the non-fused acceleration values, more accurately represents the user's freestyle roll angle. Fusion processing also helps eliminate occasional outliers or sudden large fluctuations, making the 06531 method more resistant to interference and robust. Furthermore, the control module 13 performs complementary filtering fusion processing based on the fused angular velocity value Ωm and the fused acceleration value An to obtain quaternion data. A quaternion consists of one real part and three imaginary parts, which can be used to represent mathematical concepts of rotation and direction in three-dimensional space. Control module 13 obtains the freestyle roll angle by solving quaternion data. Therefore, the supplementary strategy adopted by control module 13 uses supplementary data to improve the accuracy of proprietary data, thereby improving the accuracy of proprietary indicators.

[0099] Please see Figure 2 and Figure 8 In some implementations, when the exercise metric is a shared metric, the selected preset strategy is a coverage strategy. 065: Based on the selected preset strategy, the user's exercise metric is obtained from exercise data of at least two wearable devices, including:

[0100] 06512: Select the motion data from the motion data of one of the wearable devices to obtain common indicators, and use it as the first data;

[0101] 06522: Obtain the first indicator based on the first data;

[0102] 06532: Select exercise data from other wearable devices to obtain common indicators, and use it as the second data;

[0103] 06542: Obtain the second indicator based on the second data;

[0104] 06552: Based on the type of exercise, determine which of the first or second indicators is more accurate; and

[0105] 06562: Replace the more accurate of the first and second indicators with the other to serve as a common indicator.

[0106] The above data processing method can be applied to the first wearable device 10. When the motion index is a common index, the preset strategy selected is the coverage strategy. The control module 13 is also used to: filter motion data from the motion data of the first wearable device 10 to obtain the common index, as the first data; obtain the first index based on the first data; filter motion data from the second wearable device 30 to obtain the common index, as the second data; obtain the second index based on the second data; determine which of the first index or the second index is more accurate based on the motion type and the common index; and overwrite the other with the more accurate first index and the second index, as the common index.

[0107] Specifically, the shared indicators are those that can be measured by both the first wearable device 10 and the second wearable device 30. For example, if both a smartwatch and a heart rate monitor can acquire the user's heart rate, then the heart rate is a shared indicator for both. In the implementation where the user's exercise type is running, the shared indicator is the running posture indicator. One way to measure the running posture indicator is the stability of the user's torso in the Z-axis direction. Smaller movement in the Z-axis direction indicates a more standard running posture. Conversely, larger movement or rotation in the Z-axis direction indicates an incorrect running posture, requiring the user to stabilize their torso while running. Other methods for measuring running posture indicators include cadence, stride length, and hip extension angle.

[0108] In the method of 06512, the first wearable device 10 is an earphone, and the first data includes the user's Z-axis acceleration acquired by the earphone, which can characterize the speed of the user's movement in the vertical direction.

[0109] In the method of 06522, the control module 13 obtains a first index based on the user's Z-axis acceleration obtained by the earphone, and the first index is the running posture index obtained by the earphone.

[0110] In the method of 06532, the second wearable device 30 is a smartwatch, and the second data includes the user's Z-axis acceleration acquired by the smartwatch.

[0111] In the method of 06542, the control module 13 obtains a second indicator based on the user's Z-axis acceleration obtained by the smartwatch. The second indicator is the running posture indicator obtained by the smartwatch.

[0112] In the method of 06552, the control module 13 determines which indicator, either the first or the second, is more accurate based on the type of exercise. In the case of running, the common indicator is the running posture indicator, which the control module 13 determines through a coverage strategy. It is understandable that when a user is running, the smartwatch is typically worn on the user's arm. During running, the arm needs to constantly swing, meaning the smartwatch moves continuously in the vertical direction (Z-axis), making it unable to represent the vertical movement of the user's torso. However, when a user is running, the headphones are typically worn on the user's head. The head and torso are relatively stationary, meaning the vertical movement of the head is essentially similar to the vertical movement of the torso. Therefore, the coverage strategy determines that, for the running type, the running posture indicator obtained by the headphones is more accurate than that obtained by the running posture indicator obtained by the running posture indicator.

[0113] In the method of 06562, the running posture index obtained by the headphones is used as the running posture index.

[0114] When the motion metric is a shared metric, control module 13 determines the shared metric. Control module 13 selects motion data from multiple data sources (i.e., two wearable devices) through a coverage strategy, providing data redundancy. The coverage strategy directly selects the more accurate motion data from multiple wearable devices, avoiding the influence of noisy motion data from other wearable devices on the determination of the shared metric. For example, if a wearable device malfunctions or its motion data is interfered with, the coverage strategy allows control module 13 to use motion data from other wearable devices, ensuring data reliability and continuity.

[0115] Please see Figure 2 and Figure 9 In some implementations, when the exercise metric is a common metric, the selected preset strategy is a selection strategy. 065: Based on the selected preset strategy, the user's exercise metric is obtained from exercise data from at least two wearable devices, including:

[0116] 06513: Based on the type of exercise, determine which of the other wearable devices' exercise data is more accurate to calculate the shared index; and

[0117] 06523: Calculate common indicators using motion data from wearable devices that can more accurately calculate common indicators.

[0118] The above data processing method can be applied to the first wearable device 10. When the motion index is a common index, the preset strategy selected is the optimal strategy. The control module 13 is also used to: determine which of the motion data from the first wearable device 10 and the second wearable device 30 is more accurate based on the motion type; and use the motion data from the wearable device that can more accurately calculate the common index to calculate the common index.

[0119] Specifically, in the implementation where the preset strategy selected by the control module 13 is an optimal selection strategy, the first sensor module 11 of the first wearable device 10 collects motion data (i.e., first motion data), and the second sensor module 31 of the second wearable device 30 collects motion data (i.e., second motion data). The first and second motion data are not both used by the control module 13 to calculate the common index. In one implementation, the control module 13 selects the first motion data to calculate the common index; in another implementation, the control module 13 selects the second motion data to calculate the common index. The motion data selected by the control module 13 for calculating the common index is the motion data of the wearable device that can more accurately calculate the common index. Methods for determining the motion data of the wearable device that more accurately calculates the common index include, but are not limited to, based on data volume and signal-to-noise ratio. Therefore, the optimal selection strategy can reduce the computational load of the control module 13 and improve computational efficiency.

[0120] Please see Figure 2 and Figure 10 In some implementations, when the exercise metric is a shared metric, the selected preset strategy is a fusion strategy. 065: Based on the selected preset strategy, the user's exercise metric is obtained from exercise data from at least two wearable devices, including:

[0121] 06514: Select the motion data from the motion data of one of the wearable devices to obtain common indicators, and use it as the first data;

[0122] 06524: Select exercise data from other wearable devices to obtain common metrics, and use this as the second set of data;

[0123] 06534: Obtain shared indicators based on the first and second data.

[0124] The above data processing method can be applied to the first wearable device 10. When the motion index is a common index, the preset strategy selected is a fusion strategy. The control module 13 is also used to: filter motion data for obtaining common indices from the motion data of one of the wearable devices as first data; filter motion data for obtaining common indices from the motion data of other wearable devices as second data; and obtain common indices based on the first data and the second data.

[0125] Specifically, in the implementation where the preset strategy selected by the control module 13 is a fusion strategy, the first sensor module 11 of the first wearable device 10 collects motion data (i.e., first data), and the second sensor module 31 of the second wearable device 30 collects motion data (i.e., second data). Both the first and second data are used by the control module 13 to calculate common indicators. This increases the amount of motion data acquiring common indicators, resulting in richer motion data. Furthermore, since the calculated indicators are common indicators, the first and second data contain data with the same attributes. For example, both headphones and watches are equipped with accelerometers and can acquire three-axis acceleration as motion data. Therefore, the first and second data can be cross-validated during the calculation process, reducing errors and increasing the accuracy of the motion indicators.

[0126] Please see Figure 2 and Figure 11 In some implementations, the sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the wearable devices include headphones and a smartwatch, the type of exercise includes rope skipping, the shared metric includes the number of rope skips, and the selected preset strategy is a fusion strategy. 065: Based on the selected preset strategy, the user's motion metrics are obtained from motion data from at least two wearable devices, including:

[0127] 065141: Extract acceleration data from the headphone's motion data and use it as the primary data.

[0128] 065241: Extract acceleration data from the smartwatch's motion data for use as secondary data; and

[0129] 065341: Obtain the number of jump ropes based on the first and second data.

[0130] The above data processing method can be applied to the first wearable device 10. The sensor module includes an accelerometer, and the motion data includes acceleration data collected by the accelerometer. The first wearable device 10 includes headphones, and the second wearable device 30 includes a smartwatch. The type of exercise includes rope skipping, and the common indicator includes the number of rope skips. The selected preset strategy is a fusion strategy. The control module 13 is also used to: filter acceleration data from the motion data of the headphones as first data; filter acceleration data from the motion data of the smartwatch as second data; and obtain the number of rope skips based on the first data and the second data.

[0131] Specifically, in the implementation where the preset strategy selected by the control module 13 is a fusion strategy, the first sensor module 11 of the earphone collects motion data including acceleration data (i.e., three-axis acceleration), and uses the three-axis acceleration data obtained by the earphone as the first data; and the second sensor module 31 of the smartwatch collects motion data including acceleration data (i.e., three-axis acceleration), and uses the three-axis acceleration data obtained by the smartwatch as the second data.

[0132] Both the first and second data are used by control module 13 to calculate the number of rope jumps. This increases the amount of motion data acquired regarding the number of rope jumps, making the data richer. Furthermore, since the calculated number of rope jumps is a shared indicator, both the first and second data contain data with the same attribute (i.e., both are triaxial acceleration). Therefore, the first and second data can be cross-validated during the calculation process, reducing errors and increasing the accuracy of the rope jump count.

[0133] Please see Figure 2 and Figure 12 In some implementations, 065341: obtaining the number of jump ropes based on the first data and the second data includes:

[0134] 065343: Processes the acceleration data of the headphones to obtain the maximum, minimum, non-extreme, average, and variation range of the headphone acceleration data;

[0135] 065345: Process the acceleration data of the smartwatch to obtain the maximum, minimum, non-extreme, average, and variation range of the smartwatch's acceleration data;

[0136] 065347: Obtain the number of jump ropes based on the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data from the headphones and the smartwatch.

[0137] The above data processing method can be applied to the first wearable device 10. The control module 13 is also used to: process the acceleration data of the headphones to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the headphones; process the acceleration data of the smartwatch to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smartwatch; and obtain the number of jump ropes based on the maximum, minimum, non-extreme, average and variation range of the acceleration data of the headphones and the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smartwatch.

[0138] Specifically, in the method of 065343, the control module 13 needs to process the acceleration data of the headphones (i.e., triaxial acceleration) and obtain the fused acceleration value of the headphones, and then obtain the maximum value, minimum value, non-extreme value, average value and change range of the headphone acceleration data based on the fused acceleration value of the headphones.

[0139] In the method of 065345, the control module 13 needs to process the acceleration data (i.e., three-axis acceleration) of the smartwatch and obtain the fused acceleration value of the smartwatch. Then, based on the fused acceleration value of the smartwatch, it obtains the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smartwatch.

[0140] The calculation methods for the fusion acceleration values ​​of the headphones and the smartwatch are the same as those for obtaining fusion acceleration values ​​in the swimming algorithm, and will not be elaborated here. Furthermore, the control module 13 extracts features from the fused acceleration values ​​of the smartwatch. The extracted features include the average slope of the fused acceleration values ​​(i.e., the range of change in the acceleration data of the smartwatch), the maximum value of the fused acceleration values ​​(i.e., the maximum value of the acceleration data of the smartwatch), the non-extreme values ​​of the fused acceleration values ​​(i.e., the non-extreme values ​​of the acceleration data of the smartwatch), the minimum value of the fused acceleration values ​​(i.e., the minimum value of the acceleration data of the smartwatch), and the average value of the fused acceleration values ​​(i.e., the average value of the acceleration data of the smartwatch). Further, the control module 13 extracts features from the fused acceleration values ​​of the headphones. The extracted features include the average slope of the fused acceleration values ​​(i.e., the range of change in the acceleration data of the headphones), the maximum value of the fused acceleration values ​​(i.e., the maximum value of the acceleration data of the headphones), the non-extreme values ​​of the fused acceleration values ​​(i.e., the non-extreme values ​​of the acceleration data of the headphones), the minimum value of the fused acceleration values ​​(i.e., the minimum value of the acceleration data of the headphones), and the average value of the fused acceleration values ​​(i.e., the average value of the acceleration data of the headphones).

[0141] Specifically, the entire body movement process of a user jumping rope can be divided into four parts: squatting, jumping, leaving the ground, and landing. In this application, squatting, jumping, and leaving the ground are divided into the current time window, and landing is divided into the next time window. During the squatting process, the user moves their headphones and smartwatch downwards, and the fusion acceleration values ​​of the headphones and smartwatch change as the user squats. The control module 13 performs squatting detection based on the maximum and minimum values ​​of the fusion acceleration values ​​of the headphones and smartwatch within the current time window, and outputs the squatting detection result. Similarly, during the jumping process, the user moves their headphones and smartwatch upwards, and the fusion acceleration values ​​of the headphones and smartwatch change as the user jumps. Therefore, the control module 13 performs jump detection based on the maximum and minimum values ​​of the fusion acceleration values ​​of the headphones and smartwatch within the current time window, and outputs the jump detection result. As the user leaves the ground, they move their headphones and smartwatch upwards. The fusion acceleration values ​​of the headphones and smartwatch change as the user leaves the ground. Simultaneously, the average slope of the fusion acceleration values ​​of the headphones and smartwatch changes throughout the landing process. Therefore, the control module 13 performs ground departure detection based on the average slope of the fusion acceleration values ​​of the headphones and smartwatch within the current time window and the minimum value of the fusion acceleration values ​​of the headphones and smartwatch within the current time window, and outputs the ground departure time. During landing, the user moves their headphones and smartwatch downwards. The fusion acceleration values ​​of the headphones and smartwatch change as the user lands. Simultaneously, the average slope of the fusion acceleration values ​​of the headphones and smartwatch changes throughout the landing process. Therefore, the control module 13 performs landing detection based on the average slope of the fusion acceleration values ​​of the headphones and smartwatch within the current time window, and the maximum and minimum values ​​within the next time window, to output the landing time. Thus, the control module 13 can obtain the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data of the headphones and smartwatch, as well as the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data of the smartwatch, based on the squat detection results, jump detection results, take-off time, and landing time, and thereby obtain the number of jump ropes. Therefore, in the process of obtaining the number of jump ropes by the control module 13 through the fusion strategy, the first data and the second data, the amount of data of the first data and the second data is larger and the accuracy is higher than that of a single sensor module. Moreover, the first data and the second data can complement each other, further improving the accuracy of obtaining the number of jump ropes.

[0142] Please see Figure 2 and Figure 13 In some implementations, the data processing method is also used for:

[0143] 07: Evaluate the user's athletic ability based on exercise metrics and output the evaluation results;

[0144] 09: Output exercise suggestions based on the evaluation results.

[0145] The above data processing method can be applied to the first wearable device 10. The control module 13 is also used to: evaluate the user's exercise ability based on exercise indicators to output the evaluation results; and output exercise suggestions based on the evaluation results.

[0146] Specifically, in the method of 07, the control module 13 is able to assess the user's athletic ability based on motion indicators.

[0147] The control module 13 can evaluate the types of movements performed by the user. The body parts evaluated include, but are not limited to, the user's head, hands, chest, waist, and legs. The user's athletic ability is assessed by comparing the corresponding movement indicators for each movement type with pre-stored standard movement indicators. For example, the control module 13 evaluates the movement indicators corresponding to the user's swimming stroke with the corresponding standard movement indicators, obtaining the deviation value between the two sets of movement indicators. The control module 13 can then determine the degree of deviation between the user's swimming stroke and the standard swimming stroke, and output the evaluation result accordingly. For instance, if the user's freestyle breathing angle and pitch angle are larger than those of standard freestyle, it indicates that the most effective power generation techniques have not been learned during the current freestyle breathing and gliding, and the output evaluation result is that the freestyle breathing angle and pitch angle are too large.

[0148] In the method described in 09, exercise recommendations include at least one of the following: the type of exercise performed, the duration of each exercise type, the intensity of the exercise, and the intervals between exercises. For example, for swimming, the exercise recommendations include the duration of swimming, the intensity of the exercise (e.g., represented by calories burned), and the intervals between swims during the exercise. The exercise recommendations also include deviations in the user's exercise parameters compared to standard parameters for the exercise type. For example, if the user's breathing angle and pitch angle in freestyle swimming are larger than those in standard freestyle swimming, the exercise recommendations will include information about the larger pitch angle and remind the user next time they swim. The exercise recommendations also include information about the user's fatigue trends. For example, if the user's duration of a certain type of exercise is long and the intensity exceeds the average level within a predetermined period, the exercise recommendations will include reminders to control the intensity of the exercise to avoid injury due to over-fatigue. Therefore, the exercise recommendations output by control module 13 help monitor the user's exercise status and detect chronic fatigue or overtraining problems early.

[0149] Please see Figure 2 and Figure 14 This application also provides a computer-readable storage medium 200 storing a program 202 thereon, which, when executed by a processor 40, implements the data processing method of any one of the above embodiments.

[0150] For example, when program 202 is executed by processor 40, the following data processing method is implemented:

[0151] 02: At least two wearable devices must be running the same type of exercise;

[0152] 04: Acquire motion data collected by the sensor modules in each wearable device; and

[0153] 06: Combine the type of exercise and, based on a preset strategy, obtain the user's exercise metrics from exercise data of at least two wearable devices.

[0154] For example, when program 202 is executed by processor 40, the following data processing method is implemented:

[0155] 0611: When the headphones need to output proprietary metrics, filter out the motion data used to obtain the proprietary metrics from the headphones' motion data and use it as dedicated data;

[0156] 0621: Based on a supplementary strategy, select exercise data from the smartwatch's exercise data to assist in obtaining proprietary metrics, serving as supplementary data; and

[0157] 0631: Obtain proprietary indicators based on dedicated data and supplementary data.

[0158] For example, when program 202 is executed by processor 40, it can also implement the data processing methods in 02, 04, 06, 061, 063, 065, 0651, 0652, 0653, 06511, 06521, 06531, 06533, 06535, 06537, 06512, 06522, 06532, 06542, 06552, 06562, 06513, 06523, 06514, 06524, 06534, 065141, 065241, 065341, 065343, 065345, 065347, 07, and 09.

[0159] The computer-readable storage medium 200 of this application acquires motion data collected by sensor modules in two wearable devices through a data processing method. Compared with motion data collected by sensor modules in a single wearable device, the data is more comprehensive. The motion data from the two wearable devices can also be used as a redundancy design to ensure the source of the motion data. The data processing method can acquire the user's motion index based on a preset strategy and the motion data from at least two wearable devices. The motion data from the two wearable devices can be mutually verified to reduce errors and improve the accuracy of the motion index.

[0160] In the description of this specification, the references to terms such as "some embodiments," "in one example," "exemplarily," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0161] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0162] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A data processing method applied to a wearable system, characterized in that, The wearable system includes at least two wearable devices worn on a user's body, including headphones and a smartwatch, and each wearable device includes a sensor module; the data processing method includes: At least two of the wearable devices are activated for the same type of exercise; Acquire motion data collected by the sensor modules in each of the wearable devices; and In conjunction with the aforementioned exercise type, and based on a preset strategy, the user's exercise metrics are obtained from exercise data from at least two of the wearable devices, including: Based on the selected preset strategy, the user's exercise index is obtained from the exercise data of at least two wearable devices. The attributes of the exercise index are determined. The attributes of the exercise index include proprietary indicators and shared indicators. The proprietary indicators are exercise indicators that are specific to one of the wearable devices under the exercise type. The shared indicators are exercise indicators that can be output by at least two wearable devices under the exercise type. Exercise indicators with different attributes have corresponding preset strategies. The preset strategy corresponding to the attributes of the exercise index is selected. Based on the selected preset strategy, the user's exercise index is obtained from the exercise data of at least two wearable devices. The sports type includes swimming, the sensor module includes an accelerometer and a gyroscope, the sports data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, when the sports index is the proprietary index, the selected preset strategy includes a supplementary strategy, when the swimming style is freestyle, the proprietary index includes freestyle roll angle, when the headphones need to output the proprietary index, the acceleration data and / or angular velocity data of the headphones are filtered from the sports data of the headphones as proprietary data, and based on the supplementary strategy, the acceleration data and angular velocity data of the smartwatch are filtered from the sports data of the smartwatch; The timing and frequency of the user's gliding strokes are obtained based on the acceleration and / or angular velocity data from the smartwatch. The gliding phase of the freestyle stroke is obtained based on the timing and frequency of the gliding strokes, and the gliding phase is used as supplementary data. Each gliding phase corresponds to a time window that includes multiple predetermined moments and multiple sets of acceleration and angular velocity data from the headphones. Based on the acceleration data of the headphones at each predetermined moment, a fused acceleration value is obtained for each time window; based on the angular velocity data of the headphones at each predetermined moment, a fused angular velocity value is obtained for each time window. Complementary filtering and fusion processing are performed based on the fused angular velocity and fused acceleration values ​​to obtain quaternion data, and the freestyle roll angle is obtained based on the quaternion data.

2. The data processing method according to claim 1, characterized in that, When the motion index is the common index, the selected preset strategy is the coverage strategy; Based on the selected preset strategy, the user's exercise metrics are obtained from the exercise data of at least two wearable devices, including: The motion data used to obtain the common index is filtered from the motion data of one of the wearable devices and used as the first data; The first indicator is obtained based on the first data; Motion data for obtaining the common indicators is filtered from motion data of other wearable devices and used as the second data; The second indicator is obtained based on the second data; Based on the type of exercise, determine which of the first or second indicators is more accurate; and The more accurate of the first and second indicators is used to override the other, serving as the common indicator.

3. The data processing method according to claim 1, characterized in that, When the motion index is a common index, the preset strategy selected is the optimal strategy. Based on the selected preset strategy, the user's exercise metrics are obtained from the exercise data of at least two wearable devices, including: Based on the type of exercise, determine which of the exercise data from one of the wearable devices and the other wearable devices is more accurate for calculating the shared metric; and The common index is calculated using motion data from the wearable device, which can more accurately calculate the common index.

4. The data processing method according to claim 1, characterized in that, When the motion index is the common index, the selected preset strategy is a fusion strategy; Based on the selected preset strategy, the user's exercise metrics are obtained from the exercise data of at least two wearable devices, including: The motion data used to obtain the common index is filtered from the motion data of one of the wearable devices and used as the first data; Motion data for obtaining the common indicators is filtered from motion data of other wearable devices to serve as the second data; and The shared indicators are obtained based on the first data and the second data.

5. The data processing method according to claim 1, characterized in that, The sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the motion type includes rope skipping, the common index includes the number of rope skips, and the selected preset strategy is a fusion strategy. Based on the selected preset strategy, the user's exercise metrics are obtained from the exercise data of at least two wearable devices, including: Acceleration data is selected from the motion data of the headphones and used as the first data. Acceleration data is filtered from the motion data of the smartwatch to serve as the second data; and The number of jump ropes is obtained based on the first data and the second data.

6. The data processing method according to claim 5, characterized in that, The step of obtaining the number of jump ropes based on the first data and the second data includes: The acceleration data of the headphones is processed to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones. Process the acceleration data of the smartwatch to obtain the maximum, minimum, non-extreme, average, and variation range of the smartwatch's acceleration data; and The number of jump ropes is obtained based on the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data from the headphones and the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data from the smartwatch.

7. The data processing method according to claim 1, characterized in that, Also used for: Based on the aforementioned exercise indicators, the user's exercise ability is assessed, and an assessment result is output; and Based on the evaluation results, exercise suggestions are output.

8. A first wearable device, applied in a wearable system, characterized in that, The wearable system includes a plurality of wearable devices worn on a user's body, the first wearable device including: The first sensor module is used to collect motion data; and A control module is configured to control the first wearable device to activate a specific exercise type, and at least one second wearable device in the wearable system to simultaneously activate the same exercise type. The second wearable device includes a second sensor module. The first wearable device includes headphones, and the second wearable device includes a smartwatch. The control module is further configured to: acquire motion data collected by the first sensor module and the second sensor module; and In conjunction with the aforementioned exercise type, and based on a preset strategy, the user's exercise metrics are obtained from exercise data of at least two wearable devices. This includes: determining the attributes of the exercise metrics, which include proprietary and shared metrics. Proprietary metrics are those specific to the first or second wearable device under the given exercise type, while shared metrics are those that can be output by both the first and second wearable devices under the given exercise type. Different attributes of the exercise metrics have corresponding preset strategies; selecting a preset strategy corresponding to the attributes of the exercise metrics; and obtaining the user's exercise metrics based on the selected preset strategy and the exercise data collected by the first and second sensor modules. The sports type includes swimming; the sensor module includes an accelerometer and a gyroscope; the sports data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope; when the sports indicator is the proprietary indicator, the selected preset strategy includes a supplementary strategy; when the swimming style is freestyle, the proprietary indicator includes freestyle roll angle; when the headphones need to output the proprietary indicator, the acceleration data and / or angular velocity data of the headphones are filtered from the headphones' sports data as dedicated data; based on the supplementary strategy, the acceleration data and angular velocity data of the smartwatch are filtered from the smartwatch's sports data; according to the acceleration data of the smartwatch... The occurrence time and frequency of the user's glides are obtained based on the glides and / or angular velocity data. The gliding phase of the freestyle stroke is obtained based on the occurrence time and frequency of the glides, and the gliding phase is used as supplementary data. Each gliding phase corresponds to a time window that includes multiple predetermined moments and multiple sets of acceleration and angular velocity data from the headphones. Based on the acceleration data from the headphones corresponding to each predetermined moment, a fused acceleration value is obtained for each time window. Based on the angular velocity data from the headphones corresponding to each predetermined moment, a fused angular velocity value is obtained for each time window. Complementary filtering and fusion processing are performed based on the fused angular velocity value and fused acceleration value to obtain quaternion data. The freestyle roll angle is obtained based on the quaternion data.

9. The first wearable device according to claim 8, characterized in that, When the motion index is the common index, the selected preset strategy is a coverage strategy; the control module is further configured to: The motion data used to obtain the common indicators is filtered from the motion data of the first wearable device and used as the first data; The first indicator is obtained based on the first data; Motion data used to obtain the common indicators are filtered from the second wearable device and used as the second data; The second indicator is obtained based on the second data; Based on the type of exercise, determine which of the first or second indicators is more accurate; and The more accurate of the first and second indicators is used to override the other, serving as the common indicator.

10. The first wearable device according to claim 8, characterized in that, When the motion index is the common index, the selected preset strategy is the optimal strategy; the control module is further configured to: Based on the type of exercise, determine which of the first and second wearable devices has a more accurate shared index calculated from exercise data; and The common index is calculated using motion data from the wearable device, which can more accurately calculate the common index.

11. The first wearable device according to claim 8, characterized in that, When the motion index is the common index, the selected preset strategy is a fusion strategy; the control module is further configured to: Motion data for obtaining the common indicators is selected from the motion data of one of the wearable devices and used as the first data; Motion data for obtaining the common indicators is filtered from motion data of other wearable devices and used as the second data; and The shared indicators are obtained based on the first data and the second data.

12. The first wearable device according to claim 8, characterized in that, The sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the motion type includes rope skipping, the common index includes the number of rope skips, and the selected preset strategy is a fusion strategy; the control module is further used for: Acceleration data is selected from the motion data of the headphones and used as the first data. Acceleration data is filtered from the motion data of the smartwatch and used as the second data. and The number of jump ropes is obtained based on the first data and the second data.

13. The first wearable device according to claim 12, characterized in that, The control module is also used for: The acceleration data of the headphones is processed to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the headphones. The acceleration data of the smartwatch is processed to obtain the maximum, minimum, non-extreme, average, and variation range of the acceleration data of the smartwatch. and The number of jump ropes is obtained based on the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data from the headphones and the maximum, minimum, non-extreme, average, and variation values ​​of the acceleration data from the smartwatch.

14. The first wearable device according to claim 8, characterized in that, The control module is also used for: Based on the aforementioned exercise indicators, the user's exercise ability is assessed, and an assessment result is output; and Based on the evaluation results, exercise suggestions are output.

15. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by a processor, it implements the data processing method according to any one of claims 1-7.

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