Headphone control method, headphone, and computer-readable storage medium

Abstract

The present application discloses a method for controlling headphones, headphones, and a computer-readable storage medium. The headphones include a sensor module, and the method includes: identifying motion behaviors of a user wearing the headphones within a preset time window, each motion behavior corresponding to a preset motion parameter calculation algorithm; obtaining, based on the motion parameter calculation algorithm, motion parameters corresponding to the motion behaviors from detection data collected by the sensor module while the user is performing the motion; and outputting a fatigue level of the user wearing the headphones within the time window based on the motion parameters corresponding to all motion behaviors within the time window and a preset fatigue detection model.

Description

Technical Field

[0001] The present application relates to the technical field of headphones, and more specifically, to a method for controlling headphones, headphones, and a computer-readable storage medium. Background Art

[0002] As a new type of smart device, headphones accompany users during many activities, such as exercise and rest, and can effectively record their activity trajectory and activity status. However, current headphones cannot detect the degree of fatigue, limiting their application scenarios. Summary of the Invention

[0003] Embodiments of the present application provide a method for controlling an earphone, an earphone, and a computer-readable storage medium.

[0004] The earphones of the embodiment of the present application include a sensor module, and the control method of the earphones includes: identifying the movement behavior of a user wearing the earphones in a preset time window, each movement behavior corresponding to a preset behavior parameter calculation algorithm; based on the behavior parameter calculation algorithm, obtaining the behavior parameters corresponding to the movement behavior according to the detection data collected by the sensor module when the user performs the movement behavior; outputting the fatigue level of the user wearing the earphones in the time window according to the behavior parameters corresponding to all the movement behaviors in the time window and a preset fatigue detection model.

[0005] In some embodiments, identifying the movement behavior of the user wearing the headset in the time window includes: identifying the movement behavior of the user wearing the headset in the time window based on detection data collected by the sensor module in the time window.

[0006] In certain embodiments, the sensor module includes an accelerometer, and the detection data includes three-axis acceleration detected by the accelerometer.

[0007] In some embodiments, the sensor module includes a gyroscope, and the detection data includes three-axis angular velocity detected by the gyroscope.

[0008] In some embodiments, the headset further includes an instruction receiving module. Identifying the movement behavior of the user wearing the headset in the time window includes: determining the movement behavior of the user wearing the headset in the time window based on the movement behavior instruction received by the instruction receiving module.

[0009] In some embodiments, the headset further includes a control box assembly and a battery box assembly, wherein the control box assembly includes a control box, and the battery box assembly includes a battery box. The command receiving module includes an input module disposed on the control box or the battery box, wherein the input module is configured to input user commands and includes at least one of a keypad and a touch screen.

[0010] In some embodiments, the headset further includes a control box assembly and a battery box assembly, wherein the control box assembly includes a control box, and the battery box assembly includes a battery box. The headset further includes a communication module, wherein the instruction receiving module includes the communication module disposed in the control box or the battery box, and the communication module is configured to receive instructions sent by an external device, wherein the external device includes at least one of a mobile phone, a watch, a server, a computer, and a tablet computer.

[0011] In some embodiments, the control method further includes: analyzing the fatigue level in each of the time windows within a predetermined period moving forward from the time window to obtain a fatigue trend of the user wearing the headset within the predetermined period.

[0012] In certain embodiments, the time window comprises days and the predetermined period comprises years.

[0013] In some embodiments, the control method further includes: evaluating the fatigue condition of the user's body parts when performing the exercise behavior and / or the user's exercise ability based on the fatigue level in the time window and the behavioral parameters corresponding to each of the exercise behaviors, so as to output a first evaluation result; and providing a first exercise recommendation in the next time window based on the first evaluation result and the fatigue trend within the predetermined period.

[0014] In some embodiments, the first exercise suggestion includes at least one of the type of exercise behavior to be performed, the exercise duration for performing each exercise behavior, the exercise intensity, and the exercise interval.

[0015] In some embodiments, the control method further includes: evaluating the fatigue condition of the user's body parts when performing the exercise behavior and / or the user's exercise ability based on the fatigue trend within the predetermined period and the behavioral parameters corresponding to each of the exercise behaviors, so as to output a second evaluation result; and providing a second exercise recommendation within the next predetermined period based on the second evaluation result and the fatigue trend within the predetermined period.

[0016] In some embodiments, the second exercise suggestion includes at least one of the type of exercise behavior to be performed, the total exercise duration, and the intensity and duration distribution of each exercise behavior.

[0017] In some embodiments, the movement behavior includes at least one of swimming, cervical spine activity, jumping, cycling, running, skipping, skiing, and mountain climbing; the behavior parameter calculation algorithm includes at least one of a swimming algorithm, a cervical spine activity algorithm, a jumping algorithm, a cycling algorithm, a running algorithm, a skipping algorithm, a skiing algorithm, and a mountain climbing algorithm.

[0018] In certain embodiments, when the movement behavior includes swimming, the behavior parameter calculation algorithm includes a swimming algorithm, and the behavior parameters include at least one of swimming stroke, proportion of each swimming stroke, breathing frequency, freestyle breathing angle, freestyle maximum breathing angle, freestyle pitch angle, breaststroke breathing angle, breaststroke maximum breathing angle, breaststroke total gliding time and total duration.

[0019] In certain embodiments, when the movement behavior includes moving the cervical spine, the behavior parameter calculation algorithm includes a cervical spine mobility algorithm, and the behavior parameters include at least one of the head left rotation angle, head right rotation angle, head forward tilt angle, head backward angle, head left tilt angle, and head right tilt angle.

[0020] In some embodiments, when the movement behavior includes jumping, the behavior parameter calculation algorithm includes a jumping algorithm, and the behavior parameter includes jumping height.

[0021] The present application also provides an earphone comprising a sensor module and a control module. The sensor module is configured to collect detection data. The control module is communicatively connected to the sensor module and configured to execute the control method described in any one of the above embodiments.

[0022] In some embodiments, the earphones in the above embodiments are bone conduction earphones or air conduction earphones.

[0023] The present application also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the control method described in any one of the above embodiments.

[0024] In the earphone control method, earphones and computer-readable storage medium provided by the present application, the control module of the earphones can identify the user's motion behavior when the user performs motion behavior within a preset time window, and obtain the behavior parameters corresponding to the motion behavior based on the behavior parameter calculation algorithm corresponding to the motion behavior and the detection data collected by the sensor module, and obtain the fatigue level of the user wearing the earphones in the time window based on the behavior parameters corresponding to all motion behaviors in the time window and the preset fatigue detection model, so that the user can obtain data feedback (i.e., fatigue level) under the motion behavior.

[0025] 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 obvious from the description below, or will be learned through practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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:

[0027] Figure 1 It is a flowchart of the control method of the headset of the present application;

[0028] Figure 2 This is a schematic diagram of the algorithm for controlling the headphones of this application.

[0029] Figure 3 is a schematic perspective view of headphones according to certain embodiments of the present application;

[0030] Figure 4 is a schematic diagram of the structure of the interaction between the headset and the external device in some embodiments of the present application;

[0031] Figure 5 yes Figure 3 A three-dimensional schematic diagram of part of the structure of the middle earphone;

[0032] Figure 6 is a schematic diagram of the breathing angle of freestyle swimming in certain embodiments of the present application;

[0033] Figure 7 is a flowchart of a method for controlling headphones according to other embodiments of the present application;

[0034] Figure 8 is a flowchart of a method for controlling headphones according to other embodiments of the present application;

[0035] Figure 9 is a flowchart of a method for controlling headphones according to other embodiments of the present application;

[0036] Figure 10 is a flowchart of a method for controlling headphones in some embodiments of the present application;

[0037] Figure 11 is a flowchart of a method for controlling headphones in some embodiments of the present application;

[0038] Figure 12 This is a schematic diagram of the connection status of a computer-readable storage medium and a processor in certain embodiments of the present application.

[0039] Description of main component symbols:

[0040] Headphones 10;

[0041] Control module 11; sensor module 12; gyroscope 121; accelerometer 122; instruction receiving module 15;

[0042] Battery box 117; control box 119.

[0043] Processor 20;

[0044] Computer readable storage medium 200 ; program 202 . DETAILED DESCRIPTION

[0045] The embodiments of the present application are described in detail below. 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 throughout. 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 should not be understood as limiting the embodiments of the present application.

[0046] As a new type of smart terminal, earphones will accompany many activities of users, such as exercise and rest, and can effectively record the user's activity trajectory and activity status information. However, current earphones cannot obtain the degree of fatigue of the human body, and their application scenarios are limited. In order to solve this problem, the present application provides an earphone 10 (such as Figures 3 to 5 As shown), earphone control method (as shown Figure 1 、 Figure 2 、 Figures 6 to 11 As shown), and computer readable storage media (such as Figure 12 shown).

[0047] See also Figures 1 to 6 The method for controlling the earphones according to the embodiment of the present application includes:

[0048] 02: Identify the movement behavior of the user wearing the headset 10 in a preset time window, and each movement behavior corresponds to a preset behavior parameter calculation algorithm;

[0049] 03: Based on the behavior parameter calculation algorithm, according to the detection data collected by the sensor module 12 when the user performs the exercise behavior, the behavior parameters corresponding to the exercise behavior are obtained; and

[0050] 04: Output the fatigue level of the user wearing the headset 10 in the time window based on the behavior parameters corresponding to all exercise behaviors in the time window and the preset fatigue detection model.

[0051] The above-mentioned control method of the earphone 10 can be applied to the earphone 10. The earphone 10 of the embodiment of the present application includes a sensor module 12 and a control module 11. The sensor module 12 is used to collect detection data, and the control module 11 is used to: identify the movement behavior of the user wearing the earphone 10 in a preset time window, and each movement behavior corresponds to a preset behavior parameter calculation algorithm; based on the behavior parameter calculation algorithm, according to the detection data collected by the sensor module 12 when the user performs the movement behavior, obtain the behavior parameters corresponding to the movement behavior; output the fatigue level of the user wearing the earphone 10 in the time window according to the behavior parameters corresponding to all movement behaviors in the time window and the preset fatigue detection model.

[0052] Specifically, the earphones 10 are an audio device primarily used for sound-to-electricity conversion, for example, converting audio signals into sound so that the user can listen to music, movies, games, or other audio content, or converting sound into electrical signals. The earphones 10 are designed to provide a private listening environment, allowing the user to enjoy audio content alone without disturbing those around them. The earphones 10 can be worn on the user's ears and can be used with devices such as mobile phones, computers, smart wearable devices (such as smart watches, smart bracelets, smart glasses, and smart helmets), head-mounted displays (HMDs), and virtual reality devices. In some embodiments, the earphones 10 include air conduction earphones and bone conduction earphones. Air conduction earphones, also known as air conduction earphones, are earphones that transmit sound through air vibrations. Bone conduction earphones, also known as bone conduction earphones, are earphones that convert sound into different mechanical vibrations and transmit sound waves through the human skull, bony labyrinth, inner ear lymph, spiracles, auditory centers, and the like. Air conduction earphones and bone conduction earphones can be used in various scenarios, increasing the applicability of the earphones 10.

[0053] The earphones 10 include a battery compartment assembly for storing batteries and / or a control box assembly with control functions. The control box assembly includes a control box 119, and the battery compartment assembly includes a battery compartment 117. The control box 119 can be used to control the earphones 10 on and off and adjust the volume. The battery compartment 117 is used to store batteries, which are used to power the earphones 10 for normal operation.

[0054] The earphone 10 also includes a part to be connected 13, which includes an ear hook 137 and / or a back hook 139. When the user wears the earphone 10, the back hook 139 of the earphone 10 is worn on the head, and the ear hook 137 of the earphone 10 is worn behind the ear, thereby improving the wearing stability of the earphone 10, and the earphone 10 is not easy to fall off when the user is outdoors or exercising. The back hook 139 is used to connect the battery box 117 and the control box 119. The earphone 10 also includes a transducer component 30, and the ear hook 137 is used to connect the battery box 117 and the transducer component 30, and to connect the control box 119 and the transducer component 30. The transducer component 30 is used to fit against the skin of the human body. When the earphone 10 is in use, the transducer component 30 can vibrate mechanically to enable the user to hear the sound.

[0055] More specifically, see Figure 3 The earphone 10 further includes a control module 11 , a sensor module 12 and a command receiving module 15 .

[0056] The control module 11 is a module inside the earphone 10 that is responsible for processing various data and coordinating various functions (including but not limited to audio processing, device connection, power management, user interaction, etc.). The control module 11 is communicatively connected to the sensor module 12. The control module 11 and the sensor module 12 can form a wired communication connection through a data line, or a wireless communication connection through a wireless signal. The control module 11 can obtain the detection data collected by the sensor module 12 by directly reading the detection data collected by the sensor module 12; the control module 11 can periodically send a request to poll the sensor module 12 to obtain the latest detection data; or the sensor module 12 and the control module 11 can be wirelessly connected, and the control module 11 obtains the detection data collected by the sensor module 12 through a wireless communication protocol.

[0057] The sensor module 12 is a module for collecting detection data. The sensor module 12 can monitor and collect signals from the user wearing the headset 10 in real time, and transmit the detection data generated by the signals to the control module 11. The sensor module 12 includes, but is not limited to, one or more of an inertial measurement unit (IMU), a pressure sensor, an electrocardiogram (ECG) sensor, and a temperature sensor. The detection data includes, but is not limited to, one or more of three-axis acceleration, three-axis angular acceleration, pressure, heart rate, and temperature. The detection data collected by the sensor module 12 can be data related to the human body (e.g., heart rate) or data related to the external environment (e.g., ambient temperature). The headset 10 of the present application includes at least one sensor module 12. In embodiments where the headset 10 includes multiple sensor modules 12, the types of detection data collected by different sensor modules 12 can be the same or different. The detection data generated by the signals can represent the user's current state. The sensor module 12 can collect detection data using, but is not limited to, constant sampling rate, variable sampling rate, and time window sampling. In the present application, the sensor samples at a constant sampling rate. The constant sampling rate is simple and convenient, and can continuously collect detection data to achieve continuous monitoring, and it is not easy to miss state changes in the detection data.

[0058] See also Figure 5 In some embodiments, the sensor module 12 includes a gyroscope 121 , and the detection data includes three-axis angular velocity detected by the gyroscope 121 .

[0059] Specifically, the gyroscope 121 can measure the angular velocity 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 overall angular velocity of the head.

[0060] See also Figure 5 In some embodiments, the sensor module 12 includes an accelerometer 122 , and the detection data includes three-axis acceleration detected by the accelerometer 122 .

[0061] Specifically, the accelerometer 122 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 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 overall acceleration of the head.

[0062] See also Figure 3 and Figure 4 The instruction receiving module 15 is used to receive instructions sent by external devices (such as Figure 3 As shown), the external device includes at least one of a mobile phone, a watch, a server, a computer and a tablet computer.

[0063] In some embodiments, the instruction receiving module 15 includes an input module (not shown) disposed on the control box 119 or the battery box 117. The input module is used to input user instructions and includes at least one of a keypad and a touch screen. The input module is used to input user instructions, and the user instructions include at least a motion behavior instruction. Thus, the control module 11 can determine the current user's motion behavior based on whether the instruction receiving module 15 receives the motion behavior instruction.

[0064] In some embodiments, the headset 10 also includes a communication module (not shown), and the instruction receiving module 15 includes a communication module arranged in the control box 119 or the battery box 117. Specifically, the instruction receiving module 15 can receive instructions sent by an external device and transmit the instructions to the control module 11.

[0065] In the method of 02, the user usually performs a series of sports activities while wearing the headset 10. In some embodiments, the sports activities include at least one of swimming, cervical spine movement, jumping, cycling, running, skipping, skiing, and mountain climbing.

[0066] The control module 11 can identify movement behaviors, and the control module 11 internally stores a preset behavior parameter calculation algorithm corresponding to each movement behavior, so as to match the detection data collected by the sensor module 12 with the behavior parameter calculation algorithm, thereby obtaining the behavior parameters corresponding to the movement behavior.

[0067] In certain embodiments, the behavior parameter calculation algorithm includes at least one of a swimming algorithm, a cervical vertebrae range of motion algorithm, a jumping algorithm, a cycling algorithm, a running algorithm, a rope skipping algorithm, a skiing algorithm, and a mountain climbing algorithm. It is understood that when the athletic behavior includes swimming, the behavior parameter calculation algorithm includes the swimming algorithm; when the athletic behavior includes jumping, the behavior parameter calculation algorithm includes the jumping algorithm. The same applies to the corresponding relationships between other athletic behaviors and behavior parameter calculation algorithms, which are not detailed here.

[0068] Among them, the preset time window is the time range for the control module 11 to obtain detection data once and identify the movement behavior. In the method of 03, the control module 11 can analyze the detection data (such as three-axis acceleration and three-axis angular velocity) collected by the sensor module 12 when the user performs the movement behavior based on the preset behavior parameter calculation algorithm, and obtain the behavior parameters corresponding to the movement behavior (such as freestyle breathing angle) after processing and converting the detection data. The behavior parameters can characterize the performance and characteristics of the user in the movement behavior.

[0069] This application uses the sensor module 12 as an accelerometer 122 and a gyroscope 121. The detection data collected by the accelerometer 122 is three-axis acceleration, and the detection data collected by the gyroscope 121 is three-axis angular velocity, corresponding to the implementation methods of three movement behaviors: swimming, moving the cervical spine, and bouncing. It describes the process of the control module 11 obtaining behavior parameters based on the behavior parameter calculation algorithm and detection data.

[0070] For example, in some embodiments, when the exercise behavior includes swimming, the behavior parameter calculation algorithm includes a swimming algorithm, and the behavior parameters include at least one of swimming strokes, the proportion of each swimming stroke, breathing frequency, freestyle breathing angle, freestyle maximum breathing angle, freestyle pitch angle, breaststroke breathing angle, breaststroke maximum breathing angle, breaststroke total gliding time and total duration.

[0071] Among them, the freestyle breathing angle α (such as Figure 6 (as shown) refers to the relative position and direction of the head and the water surface when the head is above the water surface for breathing during freestyle swimming. Therefore, it is crucial for the user to maintain a correct breathing angle during swimming to maintain body balance, improve swimming efficiency and avoid choking. The pitch angle generally refers to the angular change of the swimmer's body posture and head position underwater relative to the water surface. Therefore, it is crucial for the user to maintain a good pitch angle during swimming to improve swimming efficiency, reduce resistance, maintain a correct breathing method and enhance overall performance. Therefore, the swimming behavioral parameters obtained by the control module 11 based on the detection data and the behavioral parameter calculation algorithm can more intuitively characterize the user's motion characteristics and performance in swimming situations compared to unprocessed detection data.

[0072] Take the freestyle breathing angle α as an example. The preset time window includes multiple groups of three-axis accelerations and three-axis angular velocities, each group of three-axis accelerations includes at least X-axis acceleration, Y-axis acceleration and Z-axis acceleration, and each group of three-axis angular velocities includes at least X-axis angular velocity, Y-axis angular velocity and Z-axis angular velocity. Multiple groups of three-axis accelerations and three-axis angular velocities at predetermined moments are fused separately to obtain fused acceleration values ​​and fused angular velocity values ​​at multiple predetermined moments. It is understandable 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 some of the noise in the three-axis acceleration and three-axis angular velocity, and improve the accuracy of the fused acceleration value and the fused angular velocity value.

[0073] In the swimming algorithm, there are multiple scheduled moments in the time window T, and the scheduled moment t1, the scheduled moment t2, the scheduled moment t3, ..., the scheduled moment tn form a time sequence. Within the preset time window T, the three-axis acceleration a1 at the predetermined time t1 (which is the set of X, Y, and 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, and 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, and 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, and Z three-axis accelerations axn, ayn, and azn), are included. After the three-axis accelerations are fused separately, the three-axis acceleration a1 is fused to obtain a fused acceleration value A1 at the predetermined time t1, the three-axis acceleration a2 is fused to obtain a fused acceleration value A2 at the predetermined time t2, the three-axis acceleration a3 is fused to obtain a fused acceleration value A3 at the predetermined time t3... and the three-axis acceleration an is fused to obtain a fused acceleration value An at the predetermined time tn. The fused acceleration value carries more information and more accurately represents the current state of the head of the user wearing the headset 10 than the non-fused acceleration value. The fusion process also helps eliminate occasional outliers or large momentary fluctuations, making the method 03 more resistant to interference and robust.

[0074] Similarly, within the preset time window T, the three-axis angular velocity ω1 at the predetermined time t1 (which is the set of X, Y, and Z three-axis accelerations ωx1, ωy1, and ωz1), the three-axis angular velocity ω2 at the predetermined time t2 (which is the set of X, Y, and Z three-axis accelerations ωx2, ωy2, and ωz2), the three-axis angular velocity ω3 at the predetermined time t3 (which is the set of X, Y, and Z three-axis accelerations ωx3, ωy3, and ωz3)... the three-axis angular velocity ω at the predetermined time tm m (is the set of X, Y, and Z three-axis accelerations ωxm, ωym, and ωzm). After the three-axis angular velocities are fused separately, 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... and the three-axis angular velocity ωm is fused to obtain the fused angular velocity value Ωm at the predetermined time tm.

[0075] Furthermore, the control module 11 performs complementary filtering and fusion processing based on the fused angular velocity value Ωm and the fused acceleration value An, thereby obtaining quaternion data. A 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. By solving the quaternion data q, the control module 11 obtains the roll angle Roll. The freestyle breathing angle α is then determined based on the fused acceleration value An, the fused angular velocity value Ωm, and the roll angle Roll.

[0076] Among them, the solution formula for the roll angle is:

[0077] Roll=atan2(2*q2*q3+2*q0*q1,-2*q1*q1-2*q2*q2);

[0078] In the above formula, quaternion data q=[q0,q1,q2,q3] T, Roll is the roll angle. After obtaining the roll angle Roll, the control module 11 obtains the peak maximum value and trough average value of the fused acceleration value An and the fused angular velocity value Ωm. If, within a first preset period, the roll angle first increases and then decreases, and both the fused acceleration value An and the fused angular velocity value Ωm have at least two peak maximum values ​​greater than a first preset peak threshold and at least two trough average values ​​less than a first preset trough threshold, the control module 11 determines the maximum roll angle within the first preset period to be the freestyle breathing angle α. The first preset period is a signal detection period preset within the earphone 10. If, within the first preset period, the roll angle first increases and then decreases, and both the fused acceleration value An and the fused angular velocity value Ωm have at least two peak maximum values ​​greater than the first preset peak threshold and at least two trough average values ​​less than the first preset trough threshold, the control module 11 determines the maximum roll angle within the first preset period to be the freestyle breathing angle α.

[0079] For another example, in some embodiments, when the movement behavior includes moving the cervical spine, the behavior parameter calculation algorithm includes a cervical spine mobility algorithm, and the behavior parameters include at least one of the head left rotation angle, head right rotation angle, head forward angle, head backward angle, head left tilt angle, and head right tilt angle.

[0080] The left rotation angle, right rotation angle, forward tilt angle, backward tilt angle, left tilt angle, and right tilt angle of the active cervical vertebra, as well as the freestyle breathing angle during swimming, are all posture angles. Therefore, the process by which control module 11 obtains the fused acceleration value Aq and fused angular velocity value Ωq of the behavioral parameters of the active cervical vertebra is consistent with the swimming algorithm and will not be repeated here. The difference is that the freestyle breathing angle is obtained based on the formula for calculating the roll angle, while the left rotation angle, right rotation angle, forward tilt angle, backward tilt angle, left tilt angle, and right tilt angle of the head can be obtained based on their respective corresponding formulas.

[0081] For another example, in some embodiments, when the movement behavior includes bouncing, the behavior parameter calculation algorithm includes a bouncing algorithm, and the behavior parameter includes a bouncing height.

[0082] In the bouncing algorithm, the process for obtaining the fused acceleration value Ap by fusing multiple triaxial accelerations within time window T is identical to the process for obtaining the fused acceleration value An in the swimming algorithm and is not detailed here. Furthermore, the control module 11 extracts features from the fused acceleration value An. The extracted features include the average slope of the fused acceleration value An, the maximum value of the fused acceleration value An, and the minimum value of the fused acceleration value An.

[0083] Specifically, the entire body movement process of the user performing a bounce 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. In the process of squatting, the user will drive the earphones 10 worn by the user to move downward, and the fused acceleration value An of the earphones 10 will change as the user squats. The control module 11 performs squat detection on the user based on the maximum and minimum values ​​of the fused acceleration value An in the current time window to output the squat detection result. Since the user will drive the earphones 10 worn by the user to move upward during the take-off process, the fused acceleration value An of the earphones 10 will change as the user jumps. Therefore, the control module 11 performs take-off detection on the user based on the maximum and minimum values ​​of the fused acceleration value An in the current time window to output the take-off detection result. When a user lifts off the ground, they cause their headset 10 to move upward. The fused acceleration value An of the headset 10 changes as the user lifts off the ground. Simultaneously, the average slope of the fused acceleration value An also changes throughout the landing process. Therefore, the control module 11 performs liftoff detection on the user based on the average slope of the fused acceleration value An within the current time window and the minimum value of the fused acceleration value An within the current time window to output the liftoff time. When a user lands, they cause their headset 10 to move downward. The fused acceleration value An of the headset 10 changes as the user lands. Simultaneously, the average slope of the fused acceleration value An also changes throughout the landing process. Therefore, the control module 11 performs landing detection on the user based on the average slope of the fused acceleration value An within the current time window and the maximum and minimum values ​​of the fused acceleration value An within the next time window to output the landing time. Thus, the control module 11 can obtain the flight time based on the squat detection results, the jump detection results, the liftoff time, and the landing time. The flight time is the interval between the moment the user leaves the ground and the moment they land. The jump height is obtained based on the flight time and gravity acceleration.

[0084] Thus, the control module 11 can determine the user's flight time during a jump based on the time interval between the user's liftoff and landing. During the jump and landing process, the user's center of gravity first leaves the ground and moves upward to the highest point due to inertia. The user's instantaneous velocity at the highest point in the air is zero. During this process, the user is only affected by gravity. Afterwards, under the influence of gravity, the user enters a free fall until landing. Therefore, the ascent time in the air is the same as the landing time. The free fall height can be directly calculated based on the flight time and gravity acceleration. The free fall height is the jump height.

[0085] In method 04, the fatigue detection model determines the fatigue level of the user wearing headphones 10 during the time window based on the behavioral parameters corresponding to all exercise behaviors during the time window. More specifically, the fatigue detection model is a fatigue level determination criterion pre-set in control module 11. In some embodiments, the fatigue detection model can be a trained algorithm model.

[0086] For example, in this application, the fatigue detection model is a trained machine learning model. The training database for the fatigue detection model training is derived from at least a historical dataset and standard motion behavior data. The historical dataset is a dataset formed by the detection data of different users collected by the sensor modules 12 of different headphones 10. It has a large data volume, many data samples (i.e., users), and covers a wide range of data, which can improve the accuracy of the fatigue detection model and avoid model overfitting. The standard motion behavior data is derived from reference standards for different motion behaviors, such as using the performance data of professional athletes as a reference standard. For example, in the motion behavior of swimming, different swimming styles correspond to different reference standards, including but not limited to freestyle breathing angle, breaststroke breathing angle, breaststroke glide time and breathing frequency. In the motion behavior of running, the reference standards include but are not limited to cadence, stride, heart rate, oxygen consumption, etc. In this way, the behavioral parameters corresponding to all motion behaviors in the time window can be compared with the reference standards to determine whether the behavioral parameters deviate from the reference standards. In addition, model training may also combine common physiological health standards and sports science research results, such as maximum heart rate, heart rate recovery time, post-exercise lactate threshold, etc., to provide a benchmark for the model to assess fatigue level.

[0087] During the training process of the fatigue detection model based on the training database, the data in the training database will undergo preprocessing, such as filtering and normalization to reduce data noise. The data in the training database will then be subjected to feature information extraction, and machine learning algorithms such as linear regression, support vector machine, random forest, gradient boosting decision tree or deep neural network will be used to learn and recognize these feature information, thereby constructing a fatigue detection model. During the training process, feature information of different fatigue levels will be labeled accordingly, such as mild fatigue, moderate fatigue and severe fatigue. These labeled feature information will be used to train the fatigue detection model so that it can output the fatigue level of the user wearing the headset 10 in the time window based on the behavioral parameters corresponding to all motion behaviors in the input time window.

[0088] In addition, the fatigue detection model can also be personalized by considering individual differences of users, such as according to user instructions regarding the user's age, gender, height and weight received by the instruction receiving module 15, so that the results output by the fatigue detection model are more suitable for the user and more accurate.

[0089] In the earphones 10 and the earphone control method of the present application, the control module 11 of the earphones 10 can identify the user's movement behavior when the user performs movement behavior within a preset time window, and obtain the behavior parameters corresponding to the movement behavior based on the behavior parameter calculation algorithm corresponding to the movement behavior and the detection data collected by the sensor module 12, and obtain the fatigue level of the user wearing the earphones 10 in the time window based on the behavior parameters corresponding to all movement behaviors in the time window and the preset fatigue detection model, so that the user can obtain data feedback (i.e., fatigue level) under the movement behavior.

[0090] See also Figure 2 、 Figure 3 and Figure 7 In certain embodiments, the method of 02 comprises:

[0091] 021: Based on the detection data collected by the sensor module 12 in the time window, identify the movement behavior of the user wearing the headset 10 in the time window.

[0092] The above-mentioned earphone control method can be applied to the earphone 10, and the control module 11 is further used to: identify the movement behavior of the user wearing the earphone 10 in the time window based on the detection data collected by the sensor module 12 in the time window.

[0093] Particularly, control module 11 can identify motor behavior according to the detection data that sensor module 12 gathers in time window.The mode of identification includes but not limited to that the mode of identification can be threshold value judgment, and promptly control module 11 sets specific threshold value, and when the detection data of sensor module 12 exceeds or is lower than these threshold values, is identified as specific motor behavior; The mode of identification can be to use machine learning algorithm, as classification algorithm, and detection data is trained and identified, to distinguish different motor behaviors. The mode of identification can be feature extraction, and control module 11, based on the frequency characteristics, temporal characteristics or time-frequency characteristics of detection data, determines motor behavior if the feature of the data of certain specific motor behavior is met. The mode of identification can be that detection data is carried out time series analysis, to identify dynamic change and the duration of motor behavior. Control module 11 identifies motor behavior based on real-time detection data, and accuracy is higher, more can reflect the motor behavior of the user in time window.

[0094] See also Figure 3 、 Figure 4 and Figure 8 In some embodiments, the method of 02 further includes:

[0095] 023: Based on the exercise behavior instruction received by the instruction receiving module 15, determine the exercise behavior of the user wearing the headset 10 in the time window.

[0096] The above-mentioned earphone control method can be applied to the earphone 10 , and the control module 11 is further used to determine the exercise behavior of the user wearing the earphone 10 in the time window based on the exercise behavior instruction received by the instruction receiving module 15 .

[0097] Specifically, the motion behavior instruction can come from other modules of the earphone 10 or from an external device. The external device includes at least one of a mobile phone, a watch, a server, a computer, and a tablet computer. For example, a user can turn on the riding mode through the button or touch screen of the watch, and the watch sends the instruction to the instruction receiving module 15, so that the earphone 10 can recognize the motion behavior. The control module 11 determines the motion behavior of the user wearing the earphone 10 in the time window through the motion behavior instruction efficiently and accurately, which can improve the accuracy of motion behavior recognition, and does not require the control module 11 to make a judgment, which can save the memory of the control module 11.

[0098] It is understandable that control module 11 can carry out the method for 021 and the method for 023, that is, control module 11 can carry out the determination of motor behavior jointly by motor behavior instruction and detection data, increases the definite accuracy of motor behavior thus.That is, the determination of motor behavior can be real-time feedback adjustment.For example, at the motor behavior instruction that control module 11 receives based on instruction receiving module 15, after determining the motor behavior of user, the user might interrupt or changed motor behavior in the time window.In this case, the motor behavior that control module 11 determines based on the detection data of sensor module 12 gathering in the time window may be inconsistent with the motor behavior determined based on the motor behavior instruction, at this moment, earphone 10 can send abnormal reminder (for example vibration prompt), reminds the user to confirm the motor behavior of user, whether consistent with the motor behavior of the determined user of motor behavior instruction.Thus, can improve the definite accuracy of control module 11 motor behavior.

[0099] In addition, improve the accuracy that control module 11 is determined to motor behavior, can improve the accuracy of the behavioral parameter that motor behavior is corresponding.More specifically, motor behavior instruction and detection data carry out the determination of motor behavior jointly, can more accurately divide the motor behavior that different time windows are corresponding, thus, can give detection data classification better, under different time windows, the detection data that motor behavior is corresponding, thereby reduce the noise of the behavioral parameter that obtains based on detection data and motor behavior.For example, after the motor behavior instruction of user input riding, the user changed to running during riding, but the user does not re-enter the running motor behavior instruction.In this case, if control module 11 continues to be determined as riding by motor behavior, then the detection data under this time window will be continuously identified as the detection data of the motor behavior of riding by control module 11, contradict with the actual motor behavior of running of the user, cause the behavioral parameter of the subsequent riding that obtains, will produce larger deviation with normal value, cause the determination of follow-up fatigue level to deviate.Therefore, control module 11 carries out the determination of motor behavior jointly by motor behavior instruction and detection data, can also improve the accuracy of the behavioral parameter that hereinafter motor behavior is corresponding.

[0100] See also Figure 3 and Figure 9 In some embodiments, the method for controlling the headset further includes:

[0101] 05: Analyze the fatigue level of each time window within a predetermined period pushed forward from the time window to obtain the fatigue trend of the user wearing the headset 10 within the predetermined period.

[0102] The above-mentioned earphone control method can be applied to earphone 10, and the control module 11 is further used to: analyze the fatigue level of each time window within a predetermined period from the time window forward to obtain the fatigue trend of the user wearing the earphone 10 within the predetermined period.

[0103] Specifically, the fatigue level of each time window within a predetermined period of time, i.e., a time window of fixed length that scrolls over time, is analyzed. The control module 11 acquires detection data collected by the sensor module 12 at constant time intervals. For example, if the time window is fixed at 5 minutes, the control module 11, with the current moment as the center, acquires the detection data collected by the sensor module 12 at the current moment, the detection data collected by the sensor module 12 2 minutes before the current moment, and the detection data collected by the sensor module 12 2 minutes after the current moment. At the next moment, the control module 11, with the next moment as the center, acquires the detection data collected by the sensor module 12 at the next moment, the detection data collected by the sensor module 12 2 minutes before the next moment, and the detection data collected by the sensor module 12 2 minutes after the next moment. Using a time window to collect detection data can reduce interference from related data, improve the accuracy of the control module 11 in determining the current state of the head of the user wearing the headset 10, and reduce the computational burden of the control module 11, avoiding excessive detection data that would otherwise burden the control module 11. The scrolling time window can provide continuous and time-sequential detection data, which can more accurately determine exercise behavior. Thus, the control module 11 can analyze the fatigue level of each time window. That is, the control module 11 can analyze the fatigue level of the user in different time windows during the user's exercise behavior within a certain period of time (i.e., a predetermined period), thereby characterizing the fatigue level of the user at different stages of the exercise behavior.

[0104] Based on the fatigue levels of multiple time windows, the fatigue trend of the user in the interval (i.e., the predetermined period) where the multiple time windows are located can be reflected. That is, the multiple time windows have a certain time sequence. Therefore, the fatigue levels of the multiple time windows occur according to the time series. By analyzing the fatigue levels of the multiple time windows, the fatigue trend within this predetermined period can be obtained. The analysis of the time series within the predetermined period includes but is not limited to autoregressive models, moving average models, autoregressive moving average models, autoregressive integral sliding average models, and seasonal autoregressive integral sliding average models. The fatigue trend can be used to predict the fatigue level of the next time window, and can also be stored as historical data in the control module 11 for use in correcting behavioral parameters.

[0105] See also Figure 3 and Figure 9 In some embodiments, the time window includes days; the predetermined period includes years.

[0106] Specifically, the time window includes a daily time window, allowing the sensor module 12 to obtain complete detection data for the user's entire day, which is beneficial for analyzing the user's fatigue level. The predetermined period includes a yearly time window, allowing the control module 11 to obtain the user's fatigue trend over the entire year. This long timeline and large analysis sample size can provide more accurate fatigue trend analysis.

[0107] See also Figure 2 、 Figure 3 and Figure 10 In some embodiments, the method for controlling the headset further includes:

[0108] 06: Evaluate the fatigue of the user's body parts when performing the exercise behavior and / or the user's exercise ability based on the fatigue level in the time window and the behavior parameters corresponding to each exercise behavior, and output a first evaluation result; and

[0109] 07: Provide a first exercise recommendation in the next time window based on the first assessment result and the fatigue trend in the predetermined period.

[0110] The above-mentioned earphone control method can be applied to the earphone 10, and the control module 11 is also used to: evaluate the fatigue of the user's body parts and / or the user's exercise ability when performing exercise behaviors based on the fatigue level in the time window and the behavioral parameters corresponding to each exercise behavior, so as to output a first evaluation result; and provide a first exercise recommendation in the next time window based on the first evaluation result and the fatigue trend within a predetermined period.

[0111] Specifically, in the method of 06, the control module 11 can evaluate the user's motor behavior. Among them, the fatigue level can characterize the fatigue of the user's body parts. Body parts include but are not limited to the user's head, hands, chest, waist and legs. The user's motor ability is compared with the pre-stored standard parameters by the behavioral parameters corresponding to each motor behavior. For example, the control module 11 evaluates the behavioral parameters corresponding to the user's swimming style when swimming and the standard parameters corresponding to the swimming style, and obtains the deviation value between the two groups of parameters. The control module 11 can thus obtain the degree of deviation between the user's swimming style and the standard swimming style, and output the first evaluation result based on this. For example, the user's freestyle breathing angle and pitch angle are larger than the breathing angle and pitch angle of standard freestyle, which indicates that the most effective power-generating technique has not been learned during breathing and gliding in the current freestyle.

[0112] In the method of 07, in certain embodiments, the first motion suggestion includes: at least one of the type of motor behavior, the duration of movement, the intensity of movement and the interval of movement of each motor behavior. For example, for the motor behavior of swimming, the first motion suggestion includes the duration of movement, the intensity of movement (such as represented by the calories consumed) of swimming and the interval of the user's swimming time during the motor behavior of swimming. The first motion suggestion also includes the motion parameter deviation value of the user's motor behavior compared to the standard parameters of the motor behavior. For example, when the breathing angle and pitch angle of the freestyle swimming of the above user are larger than the breathing angle and pitch angle of the standard freestyle swimming, the first motion suggestion also includes the information that the pitch angle is larger, and reminds the user when the user's motor behavior of swimming in the next time window. The first motion suggestion also includes the fatigue trend of the user, for example, if the duration of movement of a certain motor behavior of the user in a predetermined period is longer, and the intensity of movement exceeds the average level, then the first motion suggestion of earphone 10 can include reminding the user to carry out the motor behavior of swimming in the next time window, or when other behaviors, pay attention to control the intensity of movement to avoid causing sports injuries due to excessive fatigue.

[0113] See also Figure 2 、 Figure 3 and Figure 11 In some embodiments, the method for controlling the headset further includes:

[0114] 08: Evaluate the fatigue status of the user's body parts and / or the user's exercise ability when performing the exercise behavior based on the fatigue trend within the predetermined period and the behavior parameters corresponding to each exercise behavior, and output a second evaluation result; and

[0115] 09: Providing a second exercise recommendation within the next predetermined period based on the second evaluation result and the fatigue trend within the predetermined period.

[0116] The above-mentioned earphone control method can be applied to the earphone 10, and the control module 11 is also used to: evaluate the fatigue status of the user's body parts and / or the user's exercise ability when performing exercise behaviors based on the fatigue trend within a predetermined period and the behavioral parameters corresponding to each exercise behavior, so as to output a second evaluation result; and provide a second exercise recommendation within the next predetermined period based on the second evaluation result and the fatigue trend within the predetermined period.

[0117] Specifically, in the method of 08, the control module 11 can evaluate all the motor behaviors performed by the user within a predetermined period. Among them, the fatigue trend can characterize the fatigue of the user's body parts within the predetermined period. The predetermined period has a certain time length. That is, the fatigue trend within the predetermined period can reflect the fatigue of the body parts embodied by the user's motor behaviors over a long period of time and / or the user's athletic ability. The accumulated fatigue trend helps monitor the status of the user's long-term motor behaviors and detect chronic fatigue or overtraining problems early.

[0118] Body parts include but are not limited to the user's head, hands, chest, waist and legs. The user's athletic ability is determined by comparing the behavioral parameters corresponding to each movement behavior with the pre-stored standard parameters. For example, the control module 11 evaluates the behavioral parameters corresponding to the user's swimming posture when swimming with the standard parameters corresponding to the swimming posture to obtain a deviation value between the two sets of parameters. The control module 11 can thereby obtain the degree of deviation between the user's swimming posture and the standard swimming posture, and output a second evaluation result based on this. For example, the user's current breaststroke breathing angle is smaller than the historical average, indicating that the current breaststroke breathing has become more stable; the current gliding time is longer than the historical average, indicating that the body is more stretched in the current breaststroke gliding stage and the resistance is smaller when gliding. By comparing the current behavioral parameters generated by the user's current swimming with the historical behavioral parameters, the user can track his or her own swimming posture learning progress at any time, thereby further broadening the use scenarios of the earphones 10.

[0119] In the method of 09, in certain embodiments, the second motion suggestion includes: at least one of the type of motor behavior, total duration of motion, and intensity and duration distribution of each motor behavior. For example, for the motor behavior of swimming, the second motion suggestion includes the duration of motion of swimming, exercise intensity (such as represented by the calories consumed) and the interval of the user's swimming time during the motor behavior of swimming. The second motion suggestion also includes the motion parameter deviation value of the user's motor behavior compared to the standard parameter of the motor behavior. For example, when the gliding time of the current breaststroke of the above user is longer than the historical average, the second motion suggestion also includes the information of increasing arm swing, strengthening leg kicking frequency, and reminding the user when the motor behavior of swimming is carried out in the next predetermined period of the user. The second motion suggestion also includes the fatigue trend of the user, such as if the motion duration of a certain motor behavior of the user in a certain predetermined period is longer, and the exercise intensity exceeds the average level, then the second motion suggestion of earphone 10 can include reminding the user to carry out the motor behavior of swimming in the next predetermined period, or when other behaviors, pay attention to control exercise intensity, avoid causing sports injuries due to excessive fatigue.

[0120] See also Figure 1 、 Figure 2 、 Figure 3 、 Figure 10 and Figure 12 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 20, the control method of any of the above-mentioned embodiments is implemented.

[0121] For example, when the program 202 is executed by the processor 20, the following control method is implemented:

[0122] 02: Identify the movement behavior of the user wearing the headset 10 in a preset time window, and each movement behavior corresponds to a preset behavior parameter calculation algorithm;

[0123] 03: Based on the behavior parameter calculation algorithm, the behavior parameters corresponding to the exercise behavior are obtained according to the detection data collected by the sensor module 12 when the user performs the exercise behavior;

[0124] 04: Output the fatigue level of the user wearing the headset 10 in the time window based on the behavior parameters corresponding to all exercise behaviors in the time window and the preset fatigue detection model.

[0125] For another example, when the program 202 is executed by the processor 20, the following control method is implemented:

[0126] 06: Evaluate the fatigue of the user's body parts and / or the user's exercise ability when performing the exercise behavior based on the fatigue level in the time window and the behavior parameters corresponding to each exercise behavior, and output a first evaluation result;

[0127] 07: Provide a first exercise recommendation in the next time window based on the first assessment result and the fatigue trend in the predetermined period.

[0128] For another example, when program 202 is executed by processor 20, the control methods in 02, 021, 023, 03, 04, 05, 06, 07, 08, and 09 can also be implemented.

[0129] In the computer-readable storage medium 200 in the present application, the control module 11 of the headset 10 can identify the user's motion behavior when the user performs motion behavior within a preset time window, and obtain the behavior parameters corresponding to the motion behavior based on the behavior parameter calculation algorithm corresponding to the motion behavior and the detection data collected by the sensor module 12, and obtain the fatigue level of the user wearing the headset 10 in the time window based on the behavior parameters corresponding to all motion behaviors in the time window and the preset fatigue detection model, so that the user can obtain data feedback under the motion behavior.

[0130] In the description of this specification, the reference 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 mutually inconsistent.

[0131] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising 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 be performed out of 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 those skilled in the art to which the embodiments of the present application belong.

[0132] 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 method for controlling headphones, characterized in that: The earphone includes a sensor module, and the control method includes: Identifying movement behaviors of a user wearing the headset in a preset time window, where each movement behavior corresponds to a preset behavior parameter calculation algorithm; Based on the behavior parameter calculation algorithm, according to the detection data collected by the sensor module when the user performs the exercise behavior, the behavior parameter corresponding to the exercise behavior is obtained; Outputting the fatigue level of the user wearing the headset in the time window according to the behavior parameters corresponding to all the exercise behaviors in the time window and a preset fatigue detection model; evaluating the fatigue condition of a body part of the user when performing the exercise behavior and / or the exercise ability of the user according to the fatigue degree in the time window and the behavior parameter corresponding to each exercise behavior, so as to output a first evaluation result; and Analyze the fatigue level in each time window within a predetermined period moving forward from the time window to obtain the fatigue trend of the user wearing the headset within the predetermined period, and provide a first exercise recommendation in the next time window based on the first evaluation result and the fatigue trend within the predetermined period.

2. The control method according to claim 1, characterized in that: The identifying the movement behavior of the user wearing the headset in the time window includes: Based on the detection data collected by the sensor module in the time window, the movement behavior of the user wearing the headset in the time window is identified.

3. The control method according to claim 2, characterized in that: The sensor module includes an accelerometer, and the detection data includes three-axis acceleration detected by the accelerometer; and / or, The sensor module includes a gyroscope, and the detection data includes three-axis angular velocity detected by the gyroscope.

4. The control method according to claim 1, wherein: The headset further includes a command receiving module; and the identifying of the movement behavior of the user wearing the headset in the time window includes: Based on the exercise behavior instruction received by the instruction receiving module, the exercise behavior of the user wearing the headset in the time window is determined.

5. The control method according to claim 4, characterized in that: The headset further comprises a control box assembly and a battery box assembly, wherein the control box assembly comprises a control box, and the battery box assembly comprises a battery box; The instruction receiving module includes an input module provided on the control box or the battery box, the input module is used to input user instructions, and the input module includes at least one of a button and a touch screen; or, The headset also includes a communication module, and the instruction receiving module includes the communication module arranged in the control box or the battery box. The communication module is used to receive instructions sent by an external device, and the external device includes at least one of a mobile phone, a watch, a server, a computer and a tablet computer.

6. The control method according to claim 1, characterized in that: The time window may be measured in days; the predetermined period may be measured in years.

7. The control method according to claim 1, characterized in that: The first exercise suggestion includes at least one of the type of exercise behavior to be performed, the exercise duration of each exercise behavior, the exercise intensity, and the exercise interval.

8. The control method according to claim 1, characterized in that: The control method further includes: evaluating the fatigue condition of a body part of the user when performing the exercise behavior and / or the exercise ability of the user based on the fatigue trend within the predetermined period and the behavior parameters corresponding to each exercise behavior, so as to output a second evaluation result; and A second exercise suggestion is provided within the next predetermined period according to the second evaluation result and the fatigue trend within the predetermined period.

9. The control method according to claim 8, characterized in that: The second exercise suggestion includes at least one of the type of exercise behavior to be performed, the total exercise duration, and the intensity and duration distribution of each exercise behavior.

10. The control method according to any one of claims 1 to 9, characterized in that: The sports behavior includes at least one of swimming, cervical spine activity, jumping, cycling, running, skipping, skiing, and mountain climbing; the behavior parameter calculation algorithm includes at least one of a swimming algorithm, a cervical spine activity algorithm, a jumping algorithm, a cycling algorithm, a running algorithm, a skipping algorithm, a skiing algorithm, and a mountain climbing algorithm.

11. The control method according to any one of claims 1 to 9, characterized in that: When the exercise behavior includes swimming, the behavior parameter calculation algorithm includes a swimming algorithm, and the behavior parameters include at least one of a swimming style, a proportion of each swimming style, a breathing frequency, a freestyle breathing angle, a freestyle maximum breathing angle, a freestyle pitch angle, a breaststroke breathing angle, a breaststroke maximum breathing angle, a total gliding time, and a total duration of the breaststroke; In the case where the movement behavior includes moving the cervical vertebra, the behavior parameter calculation algorithm includes a cervical vertebra mobility algorithm, and the behavior parameter includes at least one of a head left rotation angle, a head right rotation angle, a head forward tilt angle, a head backward tilt angle, a head left tilt angle, and a head right tilt angle; In the case where the movement behavior includes bouncing, the behavior parameter calculation algorithm includes a bouncing algorithm, and the behavior parameter includes a bouncing height.

12. A headset, characterized in that: The earphones include: A sensor module for collecting detection data; and A control module is communicatively connected to the sensor module and is used to execute the control method described in any one of claims 1-11.

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

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