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

Abstract

The invention discloses a data processing method, first wearable equipment and a computer readable storage medium. The data processing method is applied to the wearable system, the wearable system comprises at least two wearable devices worn on the body of a user, and each wearable device comprises a sensor module. The data processing method comprises the steps that at least two pieces of wearable equipment start a same motion type; acquiring motion data acquired by a sensor module in each wearable device; and in combination with the motion type, based on a preset strategy, obtaining a motion index of the user according to the motion data of the at least two wearable devices.

Description

Technical Field

[0001] The present 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 Art

[0002] With the popularity of sports wearable devices, such as smart watches, heart rate belts, sports headphones, etc., the types of data recorded during exercise are becoming more and more abundant. After being processed by algorithms, these data can be derived into advanced sports indicators, such as heart rate range, exercise load, etc. Although the use or wearing of multiple smart devices during exercise has been reflected in professional athletes, such as the combination of heart rate belts and smart watches, or the combination of sports headphones and smart watches. However, at present, the acquisition of sports indicators usually relies on the data source of a single device, resulting in low accuracy of sports indicators. Summary of the invention

[0003] Embodiments of the present application provide a data processing method, a first wearable device, and a computer-readable storage medium.

[0004] The present application provides a data processing method. The data processing method is applied to a wearable system, wherein the wearable system includes at least two wearable devices worn on the user's body, and each of the wearable devices includes a sensor module. The data processing method includes: at least two of the wearable devices start a same type of exercise; obtaining the exercise data collected by the sensor module in each of the wearable devices; and combining the exercise type, based on a preset strategy, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices.

[0005] In certain embodiments, in combination with the exercise type and based on a preset strategy, the exercise index of the user is obtained according to the exercise data of at least two of the wearable devices, including: determining the attributes of the exercise index, the attributes of the exercise index include exclusive indicators and common indicators, the exclusive indicator is a exercise index that is exclusive to one of the wearable devices output under the exercise type, and the common indicator is a exercise index that can be output by at least two of the wearable devices under the exercise type, and the exercise indicators of different attributes have corresponding preset strategies; selecting the preset strategy corresponding to the attribute of the exercise index; and based on the selected preset strategy, obtaining the user's exercise index according to the exercise data of at least two wearable devices.

[0006] In some embodiments, when the motion index is the proprietary index, the selected preset strategy is a supplementary strategy. Based on the selected preset strategy, the motion index of the user is obtained according to the motion data of at least two of the wearable devices, including: filtering out the motion data used to obtain the proprietary index from the motion data of one of the wearable devices as the proprietary data; based on the supplementary strategy, filtering out the motion data used to assist in obtaining the proprietary index from the motion data of the other wearable devices as the supplementary data; and obtaining the proprietary index according to the dedicated data and the supplementary data.

[0007] In some embodiments, the wearable device includes headphones and smart watches, and when the motion index is the proprietary index, the selected preset strategy includes a supplementary strategy. In combination with the motion type, based on the preset strategy, the user's motion index is obtained according to the motion data of at least two of the wearable devices, including: when the headphones need to output the proprietary index, the motion data used to obtain the proprietary index is filtered out from the motion data of the headphones as the proprietary data; based on the supplementary strategy, the motion data used to assist in obtaining the proprietary index is filtered out from the motion data of the smart watch as the supplementary data; and the proprietary index is obtained according to the dedicated data and the supplementary data.

[0008] In some embodiments, the sport type includes swimming, the sensor module includes an accelerometer and a gyroscope, the sport data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary indicator includes a freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data of the headset, and the supplementary data includes acceleration data and / or angular velocity data of the smart watch. Acquiring the proprietary indicator based on the dedicated data and the supplementary data includes: when the swimming style is freestyle, acquiring the occurrence time and frequency of the user's slides based on the acceleration data and / or angular velocity data of the smart watch; acquiring the gliding stage of the freestyle based on the occurrence time of the slides and the frequency of the slides; and acquiring the freestyle roll angle based on the acceleration data and / or angular velocity data of the gliding stage.

[0009] In some embodiments, when the motion index is the common index, the selected preset strategy is an overlay strategy. Based on the selected preset strategy, the motion index of the user is obtained according to the motion data of at least two of the wearable devices, including: selecting the motion data used to obtain the common index from the motion data of one of the wearable devices as the first data; obtaining the first indicator according to the first data; selecting the motion data used to obtain the common index from the motion data of the other wearable devices as the second data; obtaining the second indicator according to the second data; determining which of the first indicator or the second indicator is more accurate according to the type of motion; and overwriting the more accurate one of the first indicator and the second indicator with the other one as the common indicator.

[0010] In some embodiments, when the motion index is the shared index, the selected preset strategy is a preferred strategy. Based on the selected preset strategy, the motion index of the user is obtained according to the motion data of at least two of the wearable devices, including: according to the type of exercise, determining which of the motion data of the one of the wearable devices and the other wearable devices is more accurate in calculating the shared index; and using the motion data of the wearable device that can more accurately calculate the shared index to calculate the shared index.

[0011] In some embodiments, when the motion index is the common index, the selected preset strategy is a fusion strategy. Based on the selected preset strategy, the motion index of the user is obtained according to the motion data of at least two of the wearable devices, including: selecting motion data for obtaining the common index from the motion data of one of the wearable devices as first data; selecting motion data for obtaining the common index from the motion data of the other wearable devices as second data; and obtaining the common index according to 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 smart watch, the type of exercise includes rope skipping, the common index includes the number of rope skipping, and the selected preset strategy is a fusion strategy. Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two of the 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 smart watch as second data; and obtaining the number of rope skipping according to the first data and the second data.

[0013] In some embodiments, obtaining the number of rope jumps based on the first data and the second data includes: processing the acceleration data of the earphone to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone; processing the acceleration data of the smart watch to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch; and obtaining the number of rope jumps based on the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone and the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch.

[0014] In certain embodiments, the data processing method is further used to: evaluate the user's athletic ability based on the athletic index to output an evaluation result; and output exercise recommendations based on the evaluation result.

[0015] The present application provides a first wearable device, which is applied to a wearable system. The wearable system includes a plurality of the wearable devices worn on the user's body, and 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 start a type of exercise, and at least one second wearable device in the wearable system synchronously starts the same type of exercise, and the second wearable device includes a second sensor module; the control module is also used to: obtain the motion data collected by the first sensor module and the second sensor module; and in combination with the type of exercise, based on a preset strategy, obtain the user's exercise index according to the motion data of the first wearable device and the second wearable device.

[0016] In some embodiments, the first wearable device is a headset, and the second wearable device is a smart watch.

[0017] In some embodiments, the first wearable device is a smart watch, and the second wearable device is a headset.

[0018] In some embodiments, the control module is also used to: determine the attributes of the motion indicators, the attributes of the motion indicators include exclusive indicators and common indicators, the exclusive indicators are motion indicators that are exclusive to one of the wearable devices under the motion type, and the common indicators are motion indicators that can be output by at least two of the wearable devices under the motion type, and the motion indicators of different attributes have corresponding preset strategies; select the preset strategy corresponding to the attributes of the motion indicators; and based on the selected preset strategy, obtain the user's motion indicators according to the motion data of at least two of the wearable devices.

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

[0020] In some embodiments, the first wearable device includes an earphone, the second wearable device includes a smartwatch, and when the motion index is the proprietary index, the selected preset strategy includes a supplementary strategy. The control module is further configured to: when the earphone needs to output the proprietary index, filter out motion data used to obtain the proprietary index from the motion data of the earphone as the dedicated data; based on the supplementary strategy, filter out motion data used to assist in obtaining the proprietary index from the motion data of the smartwatch as the supplementary data; and obtain the proprietary index 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 exercise data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary index includes a freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data of the headset, and the supplementary data includes acceleration data and / or angular velocity data of the smart watch. The control module is also used to: when the swimming style is freestyle, obtain the occurrence time of the user's slide and the frequency of the slide according to the acceleration data and / or angular velocity data of the smart watch; obtain the gliding stage of the freestyle according to the occurrence time of the slide and the frequency of the slide; and obtain the freestyle roll angle according to the acceleration data and / or angular velocity data of the gliding stage.

[0022] In some embodiments, when the motion index is the shared index, the selected preset strategy is the overwriting strategy. The control module is also used to: filter out the motion data used to obtain the shared index from the motion data of the first wearable device as the first data; obtain the first index according to the first data; filter out the motion data used to obtain the shared index from the second wearable device as the second data; obtain the second index according to the second data; determine which of the first index or the second index is more accurate according to the motion type and the shared index; and overwrite the more accurate one of the first index and the second index with the other as the shared index.

[0023] In some embodiments, when the motion index is the shared index, the selected preset strategy is a preferred strategy. The control module is also used to: determine, based on the motion type and the shared index, which of the first wearable device and the second wearable device has the motion data that calculates the shared index more accurately; and calculate the shared index using the motion data of the wearable device that can more accurately calculate the shared index.

[0024] In some embodiments, when the motion index is the common index, the selected preset strategy is a fusion strategy. The control module is also used to: filter out motion data for obtaining the common index from the motion data of one of the wearable devices as first data; filter out motion data for obtaining the common index from the motion data of the other wearable devices as second data; and obtain the common index according to 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 smart watch, the type of exercise includes rope skipping, the common indicator includes the number of rope skipping, and the selected preset strategy is a fusion strategy. The control module 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 smart watch as second data; and obtain the number of rope skipping according to the first data and the second data.

[0026] In some embodiments, the control module is also used to: process the acceleration data of the earphone to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone; process the acceleration data of the smart watch to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch; and obtain the number of rope skipping according to the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone and the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch.

[0027] In some embodiments, the control module is further used to: evaluate the user's exercise ability according to the exercise index to output an evaluation result; and output exercise recommendations according to the evaluation result.

[0028] The present application also provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the data processing method described in any one of the above-mentioned embodiments is implemented.

[0029] In the data processing method, the first wearable device and the computer-readable storage medium provided by the present application, the data processing method obtains the motion data collected by the sensor modules in the two wearable devices. Compared with the motion data collected by the sensor module in a single wearable device, the data is more comprehensive; the motion data in the two wearable devices can also be used as a redundant design to ensure the source of the motion data; the data processing method can obtain the user's motion indicators based on the motion data of at least two wearable devices based on a preset strategy, and the motion data of the two wearable devices can be verified with each other to reduce errors and improve the accuracy of the motion indicators.

[0030] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0033] Figure 2 It is a schematic diagram of the structure of wearable device interaction in a wearable system according to some embodiments of the present application;

[0034] Figure 3 is an algorithm diagram of a data processing method of some embodiments of the present application;

[0035] Figure 4 It is a flowchart of a data processing method of some implementation modes of the present application;

[0036] Figure 5 It is a flowchart of a data processing method of some implementation modes of the present application;

[0037] Figure 6 It is a flowchart of a data processing method of some implementation modes of the present application;

[0038] Figure 7 It is a flowchart of a data processing method of some implementation modes of the present application;

[0039] Figure 8 It is a flowchart of a data processing method of some implementation methods of the present application;

[0040] Fig. 9 It is a flowchart of a data processing method of some implementation methods of the present application;

[0041] Fig.10 It is a flowchart of a data processing method of some implementation methods of the present application;

[0042] Fig.11 It is a flowchart of a data processing method of some implementation methods of the present application;

[0043] Fig.12 It is a flowchart of a data processing method of some implementation methods of the present application;

[0044] Fig.13 It is a flowchart of a data processing method of some implementation methods of the present application;

[0045] Fig.14 It is a schematic diagram of the connection status of a computer-readable storage medium and a processor in certain embodiments of the present application.

[0046] Description of main 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 DESCRIPTION

[0051] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and cannot be understood as limiting the embodiments of the present application.

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

[0053] See also Figures 1 to 3Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothes or accessories. Wearable devices can realize various functions such as data transmission and data analysis through software support, data interaction, and cloud interaction. Wearable devices include but are not limited to headphones, smart watches, smart sports bands, smart glasses, and smart helmets. The wearable system 100 includes multiple wearable devices worn on the user's body. Multiple wearable devices in the wearable system 100 can communicate with each other to realize data transmission.

[0054] The wearable system 100 includes a plurality of sensor modules (e.g., a first sensor module 11 and a second sensor module 31), which are arranged in a wearable device (e.g., a first wearable device 10 and a second wearable device 30), and the sensor module can monitor and collect signals of the user in the process of wearing the wearable device in real time, and transmit the motion data formed by the signal to the control module 13. When the user wears the wearable device for activities, the sensor module can contact the user's body to collect relevant motion data. For example, the sensor module (e.g., myoelectric sensor) on the wearable device can contact different parts of the human body, such as arms, abdomen, back, etc., to capture the myoelectric activity signals of these parts. In addition, the sensor module (e.g., accelerometer or gyroscope) on the wearable device can contact multiple limbs (e.g., upper arms, forearms, thighs, calves, etc.) or joints (e.g., wrists, ankles, knees, etc.) of the human body to obtain motion data representing the motion posture and dynamic changes of these parts.

[0055] The wearable system 100 includes a control module 13. The control module 13 is a module responsible for processing various data and coordinating various functions (including but not limited to audio processing, connecting devices, power management, user interaction, etc.). The control module 13 is connected to the sensor modules of multiple wearable devices. The control module 13 and the sensor modules can form a wired communication connection through a data line, or a wireless communication connection through a wireless signal.

[0056] In the wearable system 100, each wearable device is provided with a sensor module. The control module 13 may be provided in a certain wearable device, that is, in the wearable system 100, at least one wearable device is provided with a control module 13 and a sensor module. Among them, in the embodiment of the present application, the wearable device provided 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 not provided with the control module 13 is the second wearable device 30, and the corresponding sensor module is the second sensor module 31. It can be understood that the wearable system 100 may include multiple second wearable devices.

[0057] For example, see Figure 2In an embodiment in which the wearable system 100 includes two wearable devices, the first wearable device 10 is an earphone, the earphone includes a first sensor module 11 and a control module 13, and the second wearable device 30 is a smart watch, the smart watch includes a second sensor module 31. In some embodiments, the first wearable device 10 is a smart watch, and the second wearable device 30 is an earphone. Among them, the earphone is an audio device, which is mainly used for sound-to-electric 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. The design of the earphone is to provide a private listening environment so that users can enjoy the audio content alone without affecting the people around them. The earphone can be worn on the user's ear and can be used in conjunction with devices such as mobile phones, computers, wearable devices, head-mounted display devices and virtual reality devices. The earphone includes an air conduction earphone and a bone conduction earphone. An air conduction earphone, that is, an air conduction earphone, is an earphone that transmits sound through air vibration. Bone conduction headphones, that is, bone conduction headphones, are headphones that convert sound into different mechanical vibrations and transmit sound waves through the human skull, bony labyrinth, inner ear lymph, spiral organ, auditory center, etc. Air conduction headphones and bone conduction headphones can be used in various scenarios, increasing the applicability of headphones.

[0058] See also Figures 1 to 3 The data processing method of the present application is applied to a wearable system 100, wherein the wearable system 100 includes at least two wearable devices worn on a user's body, and each wearable device includes a sensor module. The data processing method includes:

[0059] 02: At least two wearable devices start the same sport type;

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

[0061] 06: In combination with the exercise type, based on a preset strategy, the user's exercise indicators are obtained according to the exercise data of at least two wearable devices.

[0062] The above data processing method can be applied to the wearable system 100. The wearable system 100 of the embodiment of the present application includes at least two wearable devices worn on the user's body. The 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 start a type of exercise, and at least one second wearable device 30 in the wearable system 100 synchronously starts the same type of exercise, and the second wearable device 30 includes a second sensor module 31; the control module 13 is also used to: obtain the motion data collected by the first sensor module 11 and the second sensor module 31; and in combination with the type of exercise, based on a preset strategy, obtain the user's exercise indicators according to the motion data of the first wearable device 10 and the second wearable device 30.

[0063] Specifically, in the method of 02, the type of exercise started by the wearable device includes at least one of swimming, cervical vertebra activity, bouncing, cycling, running, skipping, skiing, and mountaineering. After the exercise is started, the sensor module (i.e., the first sensor module 11 and the second sensor module 31) starts to collect motion data. The sensor module includes but is not limited to one or more of an inertial measurement unit (IMU), a pressure sensor, an electrocardiogram sensor, an electrocardiogram sensor, and a temperature sensor. The motion data includes but is not limited to one or more of three-axis acceleration, three-axis angular acceleration, pressure, heart rate, and temperature. The motion data collected by the sensor module can be relevant data of the human body (such as heart rate) or relevant data of the external environment (such as ambient temperature). The wearable device of the present application includes at least one sensor module. In an embodiment in which 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 current state of the user. The way in which the sensor module collects motion data includes but is not limited to sampling at a constant sampling rate, sampling at a variable sampling rate, and sampling at a time window. In this application, the sensor samples at a constant sampling rate. More specifically, this application obtains a set of sampled data at intervals of one second. The constant sampling rate is simple and convenient, and can continuously collect motion data, achieve continuous monitoring, and is not easy to miss state changes in motion data.

[0064] In the method of 04, the control module 13 may obtain the detection data collected by the sensor module by directly reading the detection data collected by the sensor module; the control module 13 may periodically send a request to poll the sensor module to obtain the latest detection data; or the sensor module and the control module 13 may be wirelessly connected, and the control module 13 obtains the detection data collected by the sensor module through a wireless communication protocol. The control module 13 may obtain the motion data collected by the first sensor module 11 and the second sensor module 31 in different ways.

[0065] In the method of 06, the preset strategy is an algorithm for obtaining the motion index according to the motion type and motion data in the control module 13. The motion index is a parameter that characterizes the user's athletic ability. For example, when the motion type is swimming, the motion index includes at least one of the swimming style, the proportion of each swimming style, the breathing frequency, the freestyle breathing angle, the freestyle maximum breathing angle, the freestyle pitch angle, the breaststroke breathing angle, the breaststroke maximum breathing angle, the breaststroke total gliding time and the total duration. For example, when the motion type is the active cervical vertebra, the motion index includes at least one of the head left rotation angle, the head right rotation angle, the head forward tilt angle, the head backward tilt angle, the head left tilt angle and the head right tilt angle. For another example, when the motion type is jumping, the motion index includes the jumping height. The user's motion index is obtained according to the motion data of at least two wearable devices, that is, the motion data of at least two wearable devices participate in the acquisition of the motion index. It can be understood that for different motion indicators, the corresponding preset strategies can be the same or different.

[0066] The data processing method of the present application obtains motion data collected by sensor modules in two wearable devices. Compared with the motion data collected by the sensor module in a single wearable device, the data is more comprehensive; the motion data in the two wearable devices can also be used as a redundant design to ensure the source of the motion data; the data processing method can obtain the user's motion indicators based on the motion data of at least two wearable devices based on a preset strategy, and the motion data of the two wearable devices can verify each other to reduce errors and improve the accuracy of the motion indicators.

[0067] See also Figures 1 to 4 In some embodiments, 06: combining the exercise type, based on a preset strategy, and obtaining the user's exercise index according to the exercise data of at least two wearable devices, including:

[0068] 061: Determine the attributes of the sports index. The attributes of the sports index include exclusive indicators and shared indicators. Exclusive indicators are sports indicators that are output exclusively by one of the wearable devices under the sports type. Shared indicators are sports indicators that can be output by at least two wearable devices under the sports type. Sports indicators with different attributes have corresponding preset strategies.

[0069] 063: Selecting a preset strategy corresponding to the attribute of the motion index; and

[0070] 065: Based on the selected preset strategy, the user's exercise index is obtained according to the exercise data of at least two wearable devices.

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

[0072] Exemplarily, for two wearable devices, earphones and smart watches, proprietary indicators include freestyle roll angle, which is output by earphones, and common indicators include human body temperature, which can be output by earphones and watches. It is understandable that the first wearable device 10 and the second wearable device 30 obtain a variety of motion data. When the control module 13 selects a preset strategy corresponding to the attribute of the motion index, the preset strategy is used to obtain the user's motion index according to the motion data of the two wearable devices. Among them, the preset strategy is at least used to filter the motion data of the user's motion index. The preset strategy is a method for obtaining motion indicators formulated according to historical data sets and motion behavior standard data. The historical data set is a data set formed by the motion data of different users collected by the sensor modules of different wearable devices. The data volume is large, the data samples (i.e., users) are many, and the coverage is wide, which can improve the accuracy of obtaining motion indicators. The motion behavior standard data comes from the reference standards of different types of sports, such as using the performance data of professional athletes as the reference standard. For example, in the type of swimming, different swimming styles correspond to different reference standards, and the reference standards include but are not limited to freestyle breathing angle, breaststroke breathing angle, breaststroke gliding time and breathing frequency. In the running movement behavior, reference standards include but are not limited to step frequency, stride, heart rate, oxygen consumption, etc.

[0073] The control module 13 can select corresponding preset strategies for sports indicators with different attributes by distinguishing sports indicators into proprietary indicators and common indicators, thereby improving the pertinence and efficiency of sports indicator processing.

[0074] See also Figure 2 and Figure 5In some embodiments, when the motion index is a proprietary index, the selected preset strategy is a supplementary strategy. 065: Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two wearable devices, including:

[0075] 0651: Filtering the motion data for obtaining the proprietary index from the motion data of one of the wearable devices as the dedicated data;

[0076] 0652: Based on the supplementary strategy, the sports data used to assist in obtaining the proprietary indicators are screened out from the sports data of other wearable devices as supplementary data; and

[0077] 0653: Obtain proprietary indicators based on dedicated 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 out motion data used to obtain the proprietary index from the motion data of the first wearable device 10 as the dedicated data; based on the supplementary strategy, filter out motion data used to assist in obtaining the proprietary index from the motion data of the second wearable device 30 as the supplementary data; and obtain the proprietary index according to the dedicated data and the supplementary data.

[0079] Different wearable devices may have different types of sensor modules, and the wearable devices may be worn on different parts of the human body. Therefore, the accuracy of the motion data of various parts of the human body obtained by the sensor is also different. In an implementation mode where the preset strategy selected by the control module 13 is a supplementary strategy, the control module 13 obtains proprietary indicators based on the special data screened out from the motion data of a wearable device, the supplementary strategy, and the type of motion. Among them, the supplementary data screened out from the motion data of another wearable device is used to assist in obtaining the special data. That is, in the supplementary strategy, the control module 13 improves the accuracy of obtaining the proprietary indicators by obtaining the supplementary data to improve the accuracy of the special data screened out from the motion data of a wearable device.

[0080] See also Figure 2 and Figure 6 In some embodiments, the wearable device includes headphones and smart watches. When the sports index is a proprietary index, the selected preset strategy includes a supplementary strategy. 065: In combination with the sports type, based on the preset strategy, the user's sports index is obtained according to the sports data of at least two wearable devices, including:

[0081] 06511: When the headset needs to output a proprietary indicator, the motion data used to obtain the proprietary indicator is filtered out from the motion data of the headset as the dedicated data;

[0082] 06521: Based on the supplementary strategy, the sports data used to assist in obtaining the proprietary indicators are screened out from the sports data of the smart watch as supplementary data; and

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

[0084] The above data processing method can be applied to the first wearable device 10, the first wearable device 10 includes a headset, the second wearable device 30 includes a smart watch, and when the motion index is a proprietary index, the selected preset strategy includes a supplementary strategy. The control module 13 is also used to: when the headset needs to output a proprietary index, filter out the motion data used to obtain the proprietary index from the motion data of the headset as the dedicated data; based on the supplementary strategy, filter out the motion data used to assist in obtaining the proprietary index from the motion data of the smart watch as the supplementary data; and obtain the proprietary index according to the dedicated data and the supplementary data.

[0085] Specifically, the earphones and smart watches are worn on different parts of the human body. Therefore, the accuracy of the motion data of various parts of the human body obtained by the sensor is also different. In an implementation mode where the preset strategy selected by the control module 13 is a supplementary strategy, the control module 13 obtains proprietary indicators based on the special data screened out from the motion data of the earphone, the supplementary strategy and the type of exercise. Among them, the supplementary data screened out from the motion data of the smart watch is used to assist in obtaining the special data. That is, in the supplementary strategy, the control module 13 obtains the supplementary data of the smart watch to improve the accuracy of the special data screened out from the motion data of the earphone, thereby improving the accuracy of obtaining the proprietary indicators. The specific implementation mode is described below.

[0086] See also Figure 2 and Figure 7 In some embodiments, the sport type includes swimming, the sensor module includes an accelerometer and a gyroscope, the sport data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary indicator includes a freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data of the headset, and the supplementary data includes acceleration data and / or angular velocity data of the smart watch. 06531: Obtaining a proprietary indicator based on the dedicated data and the supplementary data, including:

[0087] 06533: When the swimming stroke is freestyle, obtaining the occurrence time and frequency of the user's sliding according to the acceleration data and / or angular velocity data of the smart watch;

[0088] 06535: Obtain the sliding phase of freestyle swimming based on the occurrence time and frequency of sliding;

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

[0090] The above data processing method can be applied to the first wearable device 10, 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, the proprietary index includes the freestyle roll angle, the dedicated data includes the acceleration data and / or angular velocity data of the earphone, and the supplementary data includes the acceleration data and / or angular velocity data of the smart watch. The control module 13 is also used to: when the swimming style is freestyle, obtain the occurrence time of the user's slide and the frequency of the slide according to the acceleration data and / or angular velocity data of the smart watch; obtain the gliding stage of freestyle according to the occurrence time of the slide and the frequency of the slide; obtain the freestyle roll angle according to the acceleration data and / or angular velocity data of the gliding stage.

[0091] Among them, the accelerometer can collect and measure the acceleration of the head in three spatial dimensions (X, Y, and Z axes). That is, when the user wears headphones, the accelerometer can measure the three-axis acceleration (i.e., acceleration data) of the wearing part (such as the user's head), and the three-axis acceleration includes X-axis acceleration, Y-axis acceleration, and Z-axis acceleration. The X-axis acceleration characterizes the change in the movement speed of the head along the X-axis direction, the Y-axis acceleration characterizes the change in the movement speed of the head along the Y-axis direction, and the Z-axis acceleration characterizes the change in the movement speed of the head along the Z-axis direction. Therefore, the three-axis acceleration includes the acceleration of three spatial dimensions. Compared with the single-axis acceleration, the three-axis acceleration can fully express the acceleration of the user.

[0092] The gyroscope can measure the angular velocity (i.e., gyroscope data) of the wearing part (such as the user's head) in three spatial dimensions (X, Y, and Z axes). The three-axis angular velocity includes the X-axis angular velocity, the Y-axis angular velocity, and the Z-axis angular velocity. The X-axis angular velocity represents the angular change of the head rotating around the X-axis, the Y-axis angular velocity represents the angular change of the head rotating around the Y-axis, and the Z-axis angular velocity represents the angular change of the head rotating around the Z-axis. Therefore, the three-axis angular velocity includes the angular velocity of three spatial dimensions. Compared with the single-axis angular velocity, the three-axis angular velocity can fully express the user's angular velocity.

[0093] Specifically, when the control module 13 obtains the freestyle roll angle, the user's swimming process includes multiple sliding stages, and the control module 13 needs to obtain the acceleration data and / or angular velocity data of the earphone in the multiple sliding stages as proprietary data. Therefore, the control module 13 needs to determine the time range corresponding to a single sliding stage, that is, the control module 13 needs to determine the time window corresponding to a single sliding stage.

[0094] Therefore, in an embodiment of the present application, the control module 13 obtains the time window corresponding to a single gliding stage through the acceleration data and / or angular velocity data of the smart watch. In the method of 06533, the control module 13 obtains the occurrence time of the slide and the frequency of the slide through the three-axis acceleration of the acceleration data of the smart watch. In some other embodiments of the present application, the control module 13 can also obtain the occurrence time of the user's slide and the frequency of the slide according to the angular velocity data. In some other embodiments of the present application, the control module 13 can also obtain the occurrence time of the user's slide and the frequency of the slide according to the acceleration data and the angular velocity data. In the method of 06535, the control module 13 obtains the gliding stage of freestyle based on the occurrence time of the slide and the frequency of the slide, thereby, the control module 13 can obtain the time window corresponding to a single gliding stage (during freestyle).

[0095] In the method of 06537, the time window corresponding to a single gliding stage includes multiple groups of three-axis accelerations (i.e., acceleration data), and the time window includes multiple predetermined moments, each group of three-axis accelerations corresponds to a predetermined moment, and each group of three-axis accelerations includes at least X-axis acceleration, Y-axis acceleration, and Z-axis acceleration. The three-axis accelerations at multiple predetermined moments are fused separately to obtain fused acceleration values ​​at multiple predetermined moments. It can be understood that the fusion process includes filtering and denoising, and filtering includes but is not limited to Kalman filtering, Gaussian filtering, and median filtering. Filtering can remove part of the noise in the three-axis acceleration and improve the accuracy of the fused acceleration value.

[0096] The time window T corresponding to a single sliding stage includes the three-axis acceleration a1 at the predetermined time t1 (which is the set of X, Y, Z three-axis accelerations ax1, ay1 and az1), the three-axis acceleration a2 at the predetermined time t2 (which is the set of X, Y, Z three-axis accelerations ax2, ay2 and az2), the three-axis acceleration a3 at the predetermined time t3 (which is the set of X, Y, Z three-axis accelerations ax3, ay3 and az3)... the three-axis acceleration an at the predetermined time tn (which is the set of X, Y, Z three-axis accelerations axn, ayn and azn), and after the three-axis accelerations are fused respectively, the three-axis acceleration a1 is fused to obtain the fused acceleration value A1 at the predetermined time t1, the three-axis acceleration a2 is fused to obtain the fused acceleration value A2 at the predetermined time t2, the three-axis acceleration a3 is fused to obtain the fused acceleration value A3 at the predetermined time t3... the three-axis acceleration an is fused to obtain the fused acceleration value An at the predetermined time tn. The fused acceleration value An carries more information and can more accurately represent the user's freestyle roll angle information than the non-fused acceleration value. The fusion process also helps to eliminate occasional outliers or instantaneous large fluctuations, making the 06531 method more resistant to interference and robust.

[0097] The time window corresponding to a single sliding stage includes multiple groups of three-axis angular velocities (i.e., angular velocity data), and the time window includes multiple predetermined moments. Each group of three-axis accelerations corresponds to a predetermined moment, and each group of three-axis angular velocities includes at least X-axis angular velocity, Y-axis angular velocity, and Z-axis angular velocity. The three-axis angular velocities at multiple predetermined moments are fused separately to obtain fused angular velocity values ​​at multiple predetermined moments. It can be understood that the fusion process includes filtering and denoising, and filtering includes but is not limited to Kalman filtering, Gaussian filtering, and median filtering. Filtering can remove part of the noise in the three-axis angular velocity and improve the accuracy of the fused angular velocity value.

[0098] The time window T corresponding to a single sliding stage includes the three-axis angular velocity ω1 at the predetermined time t1 (which is the set of X, Y, Z three-axis acceleration ωx1, ωy1 and ωz1), the three-axis angular velocity ω2 at the predetermined time t2 (which is the set of X, Y, Z three-axis acceleration ωx2, ωy2 and ωz2), the three-axis angular velocity ω3 at the predetermined time t3 (which is the set of X, Y, Z three-axis acceleration ωx3, ωy3 and ωz3)... The three-axis angular velocity at the predetermined time tm After the three-axis angular velocity is fused, the three-axis angular velocity ω1 is fused to obtain the fused angular velocity value Ω1 at the predetermined time t1, the three-axis angular velocity ω2 is fused to obtain the fused angular velocity value Ω2 at the predetermined time t2, the three-axis angular velocity ω3 is fused to obtain the fused angular velocity value Ω3 at the predetermined time t3... After the three-axis angular velocity ωm is fused, the fused angular velocity value Ωm at the predetermined time tm is obtained. The fused angular velocity value Ωm carries more information and can more accurately characterize the user's freestyle roll angle information than the non-fused acceleration value. The fusion process also helps to eliminate occasional abnormal values ​​or instantaneous large fluctuations, making the 06531 method more anti-interference and robust. Further, the control module 13 performs complementary filtering fusion processing based on the fused angular velocity value Ωm and the fused acceleration value An, thereby obtaining quaternion data. The quaternion consists of a real part and three imaginary parts, and can be used to represent the mathematical concepts of rotation and direction in three-dimensional space. The control module 13 obtains the freestyle roll angle by solving the quaternion data. Therefore, the supplementary strategy adopted by the control module 13 can improve the accuracy of the proprietary data by supplementing the data, thereby improving the accuracy of the proprietary index.

[0099] See also Figure 2 and Figure 8 In some embodiments, when the motion index is a common index, the selected preset strategy is an overlay strategy. 065: Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two wearable devices, including:

[0100] 06512: Filtering motion data for obtaining a common indicator from the motion data of one of the wearable devices as first data;

[0101] 06522: obtaining a first indicator according to the first data;

[0102] 06532: Filtering the motion data for obtaining the common index from the motion data of other wearable devices as the second data;

[0103] 06542: obtaining a second indicator according to the second data;

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

[0105] 06562: The more accurate one of the first and second indicators is overwritten 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 selected preset strategy is the overlay strategy. The control module 13 is also used to: filter out motion data for obtaining the common index from the motion data of the first wearable device 10 as the first data; obtain the first index according to the first data; filter out motion data for obtaining the common index from the second wearable device 30 as the second data; obtain the second index according to the second data; determine which one of the first index and the second index is more accurate according to the motion type and the common index; and cover the more accurate one of the first index and the second index with the other as the common index.

[0107] Specifically, the common indicator is an indicator that can be measured by the first wearable device 10 and the second wearable device 30. For example, both a smart watch and a heart rate belt can obtain the user's heart rate, and the heart rate is a common indicator of the smart watch and the heart rate belt. In an implementation where the user's exercise type is running, the common indicator is a running posture indicator. One of the ways to measure the running posture indicator is the stability of the user's torso in the Z-axis direction, that is, the user's movement in the Z-axis direction is small, which indicates that the user's running posture is more standard. On the contrary. If the user has a large movement and rotation in the Z-axis direction during running, it indicates that the user's running posture is not standard and needs to stabilize the torso while running. In addition, the measurement methods of the running posture indicator also include cadence, stride, hip delivery angle, etc.

[0108] In the method of 06512, the first wearable device 10 is a headset, and the first data includes the user's Z-axis acceleration obtained by the headset, and the Z-axis acceleration can represent the speed of the user's movement in the vertical direction.

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

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

[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 smart watch, and the second indicator is a running posture indicator obtained by the smart watch.

[0112] In the method of 06552, the control module 13 determines which of the first indicator or the second indicator is more accurate according to the type of exercise. Among them, the type of exercise is running, the common indicator is the running posture indicator, and the control module 13 determines the common indicator through the coverage strategy. It can be understood that when the user is running, the smart watch is usually worn on the user's arm, and the arm needs to constantly swing the arm when running, that is, the smart watch is constantly moving in the vertical direction (Z axis), and it is impossible to characterize the movement of the user's torso in the vertical direction. When the user is running, the earphones are usually worn on the user's head, and the head and torso are relatively still, that is, the movement of the head in the vertical direction is basically similar to the movement of the torso in the vertical direction. Therefore, the coverage strategy determines that when the type of exercise is running, the running posture indicator obtained by the earphone is more accurate than the running posture indicator obtained by the earphone.

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

[0114] In the case where the motion index is a common index, the control module 13 determines the common index. The 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 motion data with higher accuracy from multiple wearable devices, which can avoid the noisy motion data of other wearable devices affecting the determination result of the common index. For example, if a wearable device fails or its motion data is interfered with, the coverage strategy allows the control module 13 to use the motion data of other wearable devices to ensure the reliability and continuity of the data.

[0115] See also Figure 2 and Fig. 9 In some embodiments, when the motion index is a common index, the selected preset strategy is a preferred strategy. 065: Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two wearable devices, including:

[0116] 06513: Determine, according to the type of exercise, which common index calculated based on the exercise data of the one wearable device or the other wearable device is more accurate; 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 selected preset strategy is a preferred strategy. The control module 13 is also used to: determine which of the first wearable device 10 and the second wearable device 30 has a more accurate common index calculated based on the motion data according to the type of motion; and calculate the common index using the motion data of the wearable device that can more accurately calculate the common index.

[0119] Specifically, in an implementation where the preset strategy selected by the control module 13 is a preferential strategy, the first sensor module 11 of the first wearable device 10 collects motion data (i.e., the first motion data), and the second sensor module 31 of the second wearable device 30 collects motion data (i.e., the second motion data). The first motion data and the 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, and in another implementation, the control module 13 selects the second motion data to calculate the common index. Among them, the motion data selected by the control module 13 to calculate the common index is the motion data of the wearable device that can more accurately calculate the common index. The method of judging the motion data of the wearable device that more accurately calculates the common index includes but is not limited to the amount of data, the signal-to-noise ratio, etc. Therefore, the preferential strategy can reduce the amount of calculation of the control module 13 and have higher calculation efficiency.

[0120] See also Figure 2 and Fig.10 In some embodiments, when the motion index is a common index, the selected preset strategy is a fusion strategy. 065: Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two wearable devices, including:

[0121] 06514: Filtering motion data for obtaining a common indicator from the motion data of one of the wearable devices as first data;

[0122] 06524: Filtering out the motion data for obtaining the common index from the motion data of other wearable devices as the second data; and

[0123] 06534: Obtain a common indicator based on the first data and the 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 selected preset strategy is a fusion strategy. The control module 13 is also used to: filter out motion data for obtaining the common index from the motion data of one of the wearable devices as the first data; filter out motion data for obtaining the common index from the motion data of other wearable devices as the second data; and obtain the common index according to the first data and the second data.

[0125] Specifically, in an 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), and both the first data and the second data are used by the control module 13 to calculate common indicators. Thus, on the one hand, the amount of data for obtaining motion data of common indicators can be increased, and the motion data is richer. On the other hand, the calculated indicators are common indicators, so there are data with the same attributes in the first data and the second data. For example, both headphones and watches are equipped with accelerometers, and both can obtain three-axis acceleration as motion data. Therefore, the first data and the second data can be cross-validated during the calculation process to reduce errors and increase the accuracy of motion indicators.

[0126] See also Figure 2 and Fig.11 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 smart watch, the type of exercise includes rope skipping, the common index includes the number of rope skipping, and the selected preset strategy is a fusion strategy. 065: Based on the selected preset strategy, the user's motion index is obtained according to the motion data of at least two wearable devices, including:

[0127] 065141: Filter acceleration data from the motion data of the earphone as the first data;

[0128] 065241: Filtering acceleration data from the motion data of the smart watch to serve as second data; and

[0129] 065341: Obtain the number of rope skipping according to the first data and the second data.

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

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

[0132] The first data and the second data are both used by the control module 13 to calculate the number of rope skipping. Thus, on the one hand, the amount of motion data for obtaining the number of rope skipping can be increased, and the motion data is richer. On the other hand, the number of rope skipping calculated is a common indicator, so there are data with the same attributes in the first data and the second data (i.e., both are three-axis acceleration). Therefore, the first data and the second data can be cross-validated during the calculation process, reduce errors, and increase the accuracy of the number of rope skipping.

[0133] See also Figure 2 and Fig.12 In some embodiments, 065341: obtaining the number of rope skipping according to the first data and the second data, comprising:

[0134] 065343: Process the acceleration data of the headset to obtain the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the headset;

[0135] 065345: Process the acceleration data of the smart watch to obtain the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch;

[0136] 065347: Obtain the number of rope skipping according to the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the headset and the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch.

[0137] The above data processing method can be applied to the first wearable device 10, and the control module 13 is also used to: process the acceleration data of the earphone to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone; process the acceleration data of the smart watch to obtain the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch; obtain the number of rope skipping according to the maximum, minimum, non-extreme, average and variation range of the acceleration data of the earphone and the maximum, minimum, non-extreme, average and variation range of the acceleration data of the smart watch.

[0138] Specifically, in the method of 065343, the control module 13 needs to process the acceleration data of the earphone (i.e., the three-axis acceleration) and obtain the fused acceleration value of the earphone, and then obtain the maximum value, minimum value, non-extreme value, average value and variation range of the earphone acceleration data according to the fused acceleration value of the earphone;

[0139] In the method of 065345, the control module 13 needs to process the acceleration data (i.e., three-axis acceleration) of the smart watch and obtain the fused acceleration value of the smart watch, and then obtain the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch according to the fused acceleration value of the smart watch;

[0140] Among them, the calculation method of the fused acceleration value of the headset and the fused acceleration value of the smart watch is consistent with the process of obtaining the fused acceleration value in the swimming algorithm, which is not repeated here. Further, the control module 13 extracts features of the fused acceleration value of the smart watch, and the extracted features include the average slope of the fused acceleration value (i.e., the variation range of the acceleration data of the smart watch), the maximum value of the fused acceleration value (i.e., the maximum value of the acceleration data of the smart watch), the non-extreme value of the fused acceleration value (i.e., the non-extreme value of the acceleration data of the smart watch), the minimum value of the fused acceleration value (i.e., the minimum value of the acceleration data of the smart watch) and the average value of the fused acceleration value (i.e., the average value of the acceleration data of the smart watch). Further, the control module 13 extracts features of the fused acceleration value of the earphone, and the extracted features include the average slope of the fused acceleration value (i.e., the variation range of the acceleration data of the earphone), the maximum value of the fused acceleration value (i.e., the maximum value of the acceleration data of the earphone), the non-extreme value of the fused acceleration value (i.e., the non-extreme value of the acceleration data of the earphone), the minimum value of the fused acceleration value (i.e., the minimum value of the acceleration data of the earphone) and the average value of the fused acceleration value (i.e., the average value of the acceleration data of the earphone).

[0141] Specifically, the entire body movement process of the user's rope skipping can be divided into four parts, namely 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. Since the user will drive the headphones and smart watches worn by the user to move downward during the squatting process, the fusion acceleration value of the headphones and the fusion acceleration value of the smart watch will change as the user squats, and the control module 13 performs squat detection on the user based on the maximum and minimum values ​​of the fusion acceleration value of the headphones and the fusion acceleration value of the smart watch in the current time window to output the squat detection result. Since the user will drive the headphones and smart watches worn by the user to move upward during the take-off process, the fusion acceleration value of the headphones and the fusion acceleration value of the smart watch will change as the user jumps, therefore, the control module 13 performs take-off detection on the user based on the maximum and minimum values ​​of the fusion acceleration value of the headphones and the fusion acceleration value of the smart watch in the current time window to output the take-off detection result. When the user is leaving the ground, the user will drive the earphones and smart watch worn by the user to move upward, and the fused acceleration value of the earphones and the fused acceleration value of the smart watch will change as the user leaves the ground. At the same time, during the entire landing process, the average slope of the fused acceleration value of the earphones and the fused acceleration value of the smart watch will also change. Therefore, the control module 13 performs lift-off detection on the user based on the average slope of the fused acceleration value of the earphones and the fused acceleration value of the smart watch in the current time window and the minimum value of the fused acceleration value of the earphones and the fused acceleration value of the smart watch in the current time window to output the lift-off time. When the user is landing, the user will drive the earphone and smart watch to move downward, and the fused acceleration value of the earphone and the fused acceleration value of the smart watch will change as the user lands. At the same time, during the entire landing process, the average slope of the fused acceleration value of the earphone and the fused acceleration value of the smart watch will also change. Therefore, the control module 13 performs landing detection on the user based on the average slope of the fused acceleration value of the earphone and the fused acceleration value of the smart watch in the current time window, and the maximum and minimum values ​​of the fused acceleration value of the earphone and the fused acceleration value of the smart watch in the next time window to output the landing time. Therefore, the control module 13 can obtain the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the earphone and the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch according to the squat detection result, the take-off detection result, the lifting time and the landing time, and thereby obtain the number of rope skipping. Therefore, in the process of the control module 13 obtaining the number of rope skipping by means of the fusion strategy, the first data and the second data, the amount of the first data and the second data is larger than the amount of data from a single sensor module, and the accuracy is higher, and the first data and the second data can complement each other, further improving the accuracy of obtaining the number of rope skipping.

[0142] See also Figure 2 and Fig.13 In some embodiments, the data processing method is also used to:

[0143] 07: Evaluate the user's athletic ability based on the athletic indicators to 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, and the control module 13 is further used to: evaluate the user's exercise ability according to the exercise index to output the evaluation result; and output exercise suggestions according to the evaluation result.

[0146] Specifically, in the method of 07, the control module 13 can evaluate the user's athletic ability according to the athletic index.

[0147] The control module 13 can evaluate the type of exercise performed by the user. Wherein, the body parts evaluated include but are not limited to the user's head, hands, chest, waist and legs, etc. The user's athletic ability is compared with the pre-stored standard exercise index by the exercise index corresponding to each exercise type. For example, the control module 13 evaluates the exercise index corresponding to the swimming posture of the user when swimming and the standard exercise index corresponding to the swimming posture, and obtains the deviation value between the two groups of exercise indexes. The control module 13 can thus obtain the degree of deviation between the swimming posture of the user and the standard swimming posture, and outputs the evaluation result according to this. For example, the user's freestyle breathing angle and pitch angle are larger than the breathing angle and pitch angle of the standard freestyle, which indicates that the most effective force-generating technique has 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 larger.

[0148] In the method of 09, the exercise suggestion includes: at least one of the type of exercise type, the exercise duration of each exercise type, the exercise intensity and the exercise interval. For example, for the exercise type of swimming, the exercise suggestion includes the exercise duration of swimming, the exercise intensity (for example, represented by the calories consumed) and the interval of the user's swimming time during the exercise type of swimming. The exercise suggestion also includes the deviation value of the exercise parameter of the user's exercise type compared to the standard parameters of the exercise type. For example, when the user's freestyle breathing angle and pitch angle are larger than the breathing angle and pitch angle of the standard freestyle, the exercise suggestion also includes information that the pitch angle is larger, and reminds the user when the user swims next time. The exercise suggestion also includes the user's fatigue trend. For example, if the user's exercise duration of a certain exercise type within a predetermined period is longer, and the exercise intensity exceeds the average level, the exercise suggestion will include reminding the user to pay attention to controlling the exercise intensity to avoid sports injuries caused by excessive fatigue. Therefore, the exercise suggestion output by the control module 13 helps to monitor the user's exercise status and discover chronic fatigue or overtraining problems as early as possible.

[0149] See also Figure 2 and Fig.14 The present application also provides a computer-readable storage medium 200 on which a program 202 is stored. When the program 202 is executed by the processor 40, any one of the data processing methods in the above-mentioned embodiments is implemented.

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

[0151] 02: At least two wearable devices start the same sport type;

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

[0153] 06: In combination with the exercise type, based on a preset strategy, the user's exercise indicators are obtained according to the exercise data of at least two wearable devices.

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

[0155] 0611: When the earphone needs to output a proprietary indicator, the motion data used to obtain the proprietary indicator is filtered out from the motion data of the earphone as the dedicated data;

[0156] 0621: Based on the supplementary strategy, the sports data used to assist in obtaining the proprietary indicators are screened out from the sports data of the smart watch as supplementary data; and

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

[0158] For another 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 the present application obtains motion data collected by sensor modules in two wearable devices through a data processing method. Compared with the motion data collected by the sensor module in a single wearable device, the data is more comprehensive; the motion data in the two wearable devices can also be used as a redundant design to ensure the source of the motion data; the data processing method can obtain the user's motion indicators based on the motion data of at least two wearable devices based on a preset strategy, and the motion data of the two wearable devices can be verified with each other to reduce errors and improve the accuracy of the motion indicators.

[0160] In the description of this specification, the descriptions with reference to the terms "certain embodiments", "in an example", "exemplarily", etc., mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0161] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0162] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present 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 the user's body, each of the wearable devices includes a sensor module; the data processing method includes: At least two of the wearable devices start the same sport type; Acquiring motion data collected by the sensor module in each of the wearable devices; and In combination with the exercise type, based on a preset strategy, the exercise index of the user is obtained according to the exercise data of at least two of the wearable devices.

2. The data processing method according to claim 1, characterized in that: In combination with the exercise type, based on a preset strategy, obtaining the exercise index of the user according to the exercise data of at least two of the wearable devices includes: Determine the attributes of the motion index, the attributes of the motion index include exclusive indicators and shared indicators, the exclusive indicator is a motion index that is exclusive to one of the wearable devices under the motion type, the shared indicator is a motion index that can be output by at least two of the wearable devices under the motion type, and the motion indicators with different attributes have corresponding preset strategies; Selecting a preset strategy corresponding to the attribute of the sports indicator; and Based on the selected preset strategy, the user's exercise index is obtained according to the exercise data of at least two of the wearable devices.

3. The data processing method according to claim 2, characterized in that: In the case where the sports index is the proprietary index, the selected preset strategy is a supplementary strategy; Based on the selected preset strategy, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices includes: Filtering the motion data used to obtain the proprietary indicator from the motion data of one of the wearable devices as the dedicated data; Based on the supplementary strategy, the motion data used to assist in obtaining the proprietary indicator is screened out from the motion data of other wearable devices as supplementary data; and The proprietary indicator is obtained according to the dedicated data and the supplementary data.

4. The data processing method according to claim 2, characterized in that: The wearable device includes a headset and a smart watch. When the sports index is the proprietary index, the selected preset strategy includes a supplementary strategy. Based on the selected preset strategy, the sports index of the user is obtained according to the sports data of at least two of the wearable devices, including: In the case where the earphone needs to output the proprietary index, the motion data used to obtain the proprietary index is screened out from the motion data of the earphone as the dedicated data; Based on the supplementary strategy, selecting the sports data for assisting in obtaining the proprietary indicator from the sports data of the smart watch as supplementary data; and The proprietary indicator is obtained according to the dedicated data and the supplementary data.

5. The data processing method according to claim 4, characterized in that: The sport type includes swimming, the sensor module includes an accelerometer and a gyroscope, the sport data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary indicator includes a freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data of the headset, and the supplementary data includes acceleration data and / or angular velocity data of the smart watch; and obtaining the proprietary indicator according to the dedicated data and the supplementary data includes: When the swimming style is freestyle, obtaining the occurrence time and frequency of the sliding of the user according to the acceleration data and / or angular velocity data of the smart watch; Obtaining the sliding phase of the freestyle swimming according to the occurrence time of the sliding times and the frequency of the sliding; and The freestyle rolling angle is obtained according to the acceleration data and / or angular velocity data in the gliding phase.

6. The data processing method according to claim 2, characterized in that: In the case where the motion index is the common index, the selected preset strategy is the coverage strategy; Based on the selected preset strategy, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices includes: Filtering the motion data used to obtain the common indicator from the motion data of one of the wearable devices as the first data; Acquire a first indicator according to the first data; Filtering the motion data used to obtain the common indicator from the motion data of other wearable devices as the second data; Acquire a second indicator according to the second data; Determining which of the first indicator or the second indicator is more accurate according to the type of exercise; and The more accurate one of the first indicator and the second indicator is overwritten with the other to serve as the common indicator.

7. The data processing method according to claim 2, characterized in that: When the sports index is the common index, the selected preset strategy is a preferential strategy; Based on the selected preset strategy, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices includes: According to the exercise type, determining which of the exercise data of the one of the wearable devices and the other wearable device is used to calculate the common index is more accurate; and The shared index is calculated using the motion data of the wearable device that can more accurately calculate the shared index.

8. The data processing method according to claim 2, 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, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices includes: Filtering the motion data used to obtain the common indicator from the motion data of one of the wearable devices as the first data; Filtering the motion data used to obtain the common indicator from the motion data of other wearable devices as the second data; and The common index is acquired according to the first data and the second data.

9. The data processing method according to claim 2, characterized in that: The sensor module includes an accelerometer, the motion data includes acceleration data collected by the accelerometer, the wearable device includes headphones and a smart watch, the type of exercise includes rope skipping, the common indicator includes the number of rope skipping, and the selected preset strategy is a fusion strategy; Based on the selected preset strategy, obtaining the user's exercise index according to the exercise data of at least two of the wearable devices includes: Filtering acceleration data from the motion data of the earphone as first data; Filtering acceleration data from the motion data of the smart watch as second data; and The rope skipping quantity is obtained according to the first data and the second data.

10. The data processing method according to claim 9, characterized in that: The obtaining the number of rope skipping according to the first data and the second data includes: Processing the acceleration data of the earphone to obtain a maximum value, a minimum value, a non-extreme value, an average value and a variation range of the acceleration data of the earphone; Processing the acceleration data of the smart watch to obtain a maximum value, a minimum value, a non-extreme value, an average value, and a variation range of the acceleration data of the smart watch; and The number of rope skipping is obtained according to the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the headset and the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch.

11. The data processing method according to claim 1, characterized in that: Also used for: According to the exercise index, the user's exercise ability is evaluated to output an evaluation result; and Outputting exercise recommendations based on the evaluation results.

12. A first wearable device, applied to a wearable system, characterized in that: The wearable system includes a plurality of wearable devices worn on a user's body, wherein the first wearable device includes: A first sensor module, used to collect motion data; and A control module, wherein the control module is used to control the first wearable device to start a sport type, and at least one second wearable device in the wearable system to synchronously start the same sport type, and the second wearable device includes a second sensor module; the control module is also used to: obtain the sport data collected by the first sensor module and the second sensor module; and in combination with the sport type, based on a preset strategy, obtain the user's sport indicators according to the sport data of the first wearable device and the second wearable device.

13. The first wearable device according to claim 12, characterized in that: The first wearable device is a headset, and the second wearable device is a smart watch; or, The first wearable device is a smart watch, and the second wearable device is a headset.

14. The first wearable device according to claim 12, characterized in that: The control module is also used for: Determine the attributes of the motion index, the attributes of the motion index include exclusive indicators and shared indicators, the exclusive indicator is a motion indicator that is exclusive to one of the wearable devices under the motion type, and the shared indicator is a motion indicator that can be output by at least two of the wearable devices under the motion type, and the motion indicators with different attributes have corresponding preset strategies; Selecting a preset strategy corresponding to the attribute of the sports index; and Based on the selected preset strategy, the user's exercise index is obtained according to the exercise data of at least two of the wearable devices.

15. The first wearable device according to claim 14, characterized in that: In the case where the sports index is the proprietary index, the selected preset strategy is a supplementary strategy; and the control module is further used for: Filtering the motion data used to obtain the proprietary indicator from the motion data of the first wearable device as the dedicated data; Based on the supplementary strategy, the motion data used to assist in obtaining the proprietary indicator is screened out from the motion data of the second wearable device as supplementary data; and The proprietary indicator is obtained according to the dedicated data and the supplementary data.

16. The first wearable device according to claim 14, characterized in that: The first wearable device includes a headset, the second wearable device includes a smart watch, and when the sports index is the proprietary index, the selected preset strategy includes a supplementary strategy; the control module is further used for: In the case where the earphone needs to output the proprietary index, the motion data used to obtain the proprietary index is screened out from the motion data of the earphone as the dedicated data; Based on the supplementary strategy, the sports data used to assist in obtaining the proprietary indicator is screened out from the sports data of the smart watch as supplementary data; and The proprietary indicator is obtained according to the dedicated data and the supplementary data.

17. The first wearable device according to claim 16, characterized in that: The sport type includes swimming, the sensor module includes an accelerometer and a gyroscope, the sport data includes acceleration data collected by the accelerometer and gyroscope data collected by the gyroscope, the proprietary index includes a freestyle roll angle, the dedicated data includes acceleration data and / or angular velocity data of the headset, and the supplementary data includes acceleration data and / or angular velocity data of the smart watch; the control module is further used for: When the swimming style is freestyle, obtaining the occurrence time and frequency of the sliding of the user according to the acceleration data and / or angular velocity data of the smart watch; Obtaining the sliding phase of the freestyle according to the occurrence time of the sliding and the frequency of the sliding; and The freestyle rolling angle is obtained according to the acceleration data and / or angular velocity data in the gliding phase.

18. The first wearable device according to claim 14, characterized in that: When the motion index is the common index, the selected preset strategy is the coverage strategy; and the control module is further used for: Filtering the motion data used to obtain the common indicator from the motion data of the first wearable device as the first data; Acquire a first indicator according to the first data; Filtering the motion data used to obtain the common indicator from the second wearable device as the second data; Acquire a second indicator according to the second data; Determining which of the first indicator or the second indicator is more accurate according to the type of exercise; and The more accurate one of the first indicator and the second indicator is overwritten with the other to serve as the common indicator.

19. The first wearable device according to claim 14, characterized in that: When the motion index is the common index, the selected preset strategy is a preferential strategy; the control module is further used for: According to the exercise type, determining which of the first wearable device and the second wearable device has the more accurate common index calculated based on the exercise data; and The shared index is calculated using the motion data of the wearable device that can more accurately calculate the shared index.

20. The first wearable device according to claim 14, characterized in that: When the motion index is the common index, the selected preset strategy is a fusion strategy; and the control module is further used for: Filtering the motion data used to obtain the common indicator from the motion data of one of the wearable devices as the first data; Filtering the motion data used to obtain the common indicator from the motion data of other wearable devices as the second data; and The common index is acquired according to the first data and the second data.

21. The first wearable device according to claim 14, characterized in that: 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 smart watch, the type of exercise includes rope skipping, the common indicator includes the number of rope skipping, and the selected preset strategy is a fusion strategy; the control module is also used for: Filtering acceleration data from the motion data of the earphone as first data; Filtering acceleration data from the motion data of the smart watch as second data; and The rope skipping quantity is obtained according to the first data and the second data.

22. The first wearable device according to claim 21, characterized in that: The control module is also used for: Processing the acceleration data of the earphone to obtain a maximum value, a minimum value, a non-extreme value, an average value and a variation range of the acceleration data of the earphone; Processing the acceleration data of the smart watch to obtain a maximum value, a minimum value, a non-extreme value, an average value, and a variation range of the acceleration data of the smart watch; and The number of rope skipping is obtained according to the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the headset and the maximum value, minimum value, non-extreme value, average value and variation range of the acceleration data of the smart watch.

23. The first wearable device according to claim 12, characterized in that: The control module is also used for: According to the exercise index, the user's exercise ability is evaluated to output an evaluation result; and Outputting exercise recommendations based on the evaluation results.

24. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the data processing method described in any one of claims 1 to 11 is implemented.

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