Motion optimization method and system and wearable intelligent device

Abstract

The invention relates to the technical field of terminals, and further relates to a motion optimization method and system and wearable intelligent equipment. The method comprises the following steps: acquiring acceleration data, angular velocity data, heart rate and speed of a user in a movement process; according to the acceleration data, the angular velocity data and a six-axis attitude fusion algorithm, motion data of the user in the motion process are determined, and the motion data comprise the motion distance or the swing arm amplitude; when the exercise distance meets the target distance or the swing arm amplitude is smaller than the preset amplitude, data of the user in the exercise process are stopped from being obtained, the running force value of the user in the exercise process is determined according to the heart rate, the set speed and the running force value calculation model, and the exercise plan of the user is adjusted according to the running force value. The exercise plan of the user is adjusted through the running force value, and the exercise ability of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of terminal technology, and further to a motion optimization method, system and wearable smart device. Background Art

[0002] At present, more and more users pay attention to physical exercise. In some solutions, users can wear wearable devices such as watches and bracelets, and collect their own motion parameters through wearable devices. For example, the heart rate and number of steps when walking can be collected through the bracelet. Users can understand their exercise status through these motion parameters. In some high-intensity and highly professional training, such as when using the Daniels Running Power Value (VDOT) theory for training, it is difficult to make targeted adjustments to the user's exercise plan by only recording the above-mentioned motion parameters, so that the user's athletic ability cannot be improved. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a motion optimization method, system and wearable smart device, which adjust the user's exercise plan through the running force value and improve the user's athletic ability.

[0004] In a first aspect, the present application provides a motion optimization method, comprising: obtaining acceleration data, angular velocity data, heart rate and pace of a user during exercise; determining the motion data of the user during exercise based on the acceleration data, the angular velocity data and a six-axis posture fusion algorithm, wherein the motion data includes movement distance or arm swing amplitude; when the movement distance meets the target distance or the arm swing amplitude is less than a preset amplitude, stop obtaining the data of the user during exercise, determine the running force value of the user during exercise based on the heart rate, the pace and the running force value calculation model, and adjust the user's exercise plan according to the running force value.

[0005] The above motion optimization method determines the user's motion data during the exercise according to the acceleration data, angular velocity data and six-axis posture fusion algorithm of the user during the exercise, and when the motion data meets the preset conditions, determines the user's running force value during the exercise according to the heart rate and pace running force value calculation model, and adjusts the user's exercise plan in a targeted manner through the running force value, thereby improving the user's athletic ability.

[0006] In one implementation, when the motion data is the motion distance, determining the motion data of the user during the motion process based on the acceleration data, the angular velocity data, and the six-axis posture fusion algorithm specifically includes: determining the arm swing amplitude of the user based on the acceleration data, the angular velocity data, and the six-axis posture fusion algorithm; determining the stride and number of steps of the user based on the arm swing amplitude and the motion posture model; and determining the motion distance based on the stride and number of steps.

[0007] In one implementation, it also includes: acquiring speed data and time data of the user during the exercise process; and determining the movement distance of the user during the exercise process based on the speed data and the time data.

[0008] The above motion optimization method can not only obtain user speed data through GPS, and determine the user's movement distance based on the speed data and time data; it can also obtain acceleration data through the acceleration sensor, obtain angular velocity data through the gyroscope, and then determine the movement distance based on the acceleration data and angular velocity data, and determine the user's movement distance during the movement based on the six-axis attitude fusion algorithm, thereby improving the compatibility of this application and the user's experience.

[0009] In one implementation, when the motion data is the arm swing amplitude, it also includes: when the arm swing amplitude of the user is not less than the preset amplitude, repeatedly executing the steps of obtaining the acceleration data, the angular velocity data, the heart rate and the pace, and determining the arm swing amplitude.

[0010] In one implementation, adjusting the user's exercise plan according to the running effort value specifically includes: comparing the running effort value with the user's historical records, and if the user's exercise ability decreases, adjusting the user's exercise plan according to the running effort value.

[0011] In a second aspect, the present application also provides a motion optimization system, comprising: a sensor for obtaining acceleration data, angular velocity data, heart rate and pace of a user during exercise; a computing unit for determining the motion data of the user during exercise based on the acceleration data, the angular velocity data and a six-axis posture fusion algorithm, wherein the motion data includes movement distance or arm swing amplitude; the sensor is also used to stop obtaining the data of the user during exercise when the movement distance meets the target distance or the arm swing amplitude is less than a preset amplitude; the computing unit is also used to determine the running force value of the user during exercise based on the heart rate, the pace and the running force value calculation model; an adjustment unit is used to adjust the user's exercise plan according to the running force value.

[0012] In one implementation, when the motion data is the motion distance, the calculation unit is also used to determine the arm swing amplitude of the user based on the acceleration data, the angular velocity data and the six-axis posture fusion algorithm; the calculation unit is also used to determine the stride and number of steps of the user based on the arm swing amplitude and the motion posture model; the calculation unit is also used to determine the motion distance based on the stride and number of steps.

[0013] In one implementation, when the motion data is the arm swing amplitude, it also includes: an execution unit, which is used to repeat the steps of obtaining the acceleration data, the angular velocity data, the heart rate and the pace, and determining the arm swing amplitude when the arm swing amplitude of the user is not less than the preset amplitude.

[0014] In one implementation, the adjustment unit is further used to compare the running effort value with the historical record of the user, and if the user's athletic ability decreases, adjust the user's exercise plan according to the running effort value.

[0015] In a third aspect, the present application also provides a wearable smart device, comprising any of the above-described motion optimization systems.

[0016] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0017] 1. Determine the user's motion data during exercise based on the acceleration data, angular velocity data and six-axis posture fusion algorithm during exercise, and when the motion data meets the preset conditions, determine the user's running force value during exercise based on the heart rate and pace running force value calculation model. The user's exercise plan is adjusted in a targeted manner through the running force value, thereby improving the user's athletic ability.

[0018] 2. Not only can the user's speed data be obtained through GPS, and the user's movement distance can be determined based on the speed data and time data; the acceleration data can also be obtained through the acceleration sensor, and the angular velocity data can be obtained through the gyroscope, and then the movement distance can be determined based on the acceleration data and angular velocity data, and the six-axis attitude fusion algorithm can be used to determine the user's movement distance during the movement, thereby improving the compatibility of this application and the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0020] Figure 1 A flow chart of a motion optimization method provided by an embodiment of the present application is shown;

[0021] Figure 2A flow chart for determining a movement distance provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0023] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0024] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

[0028] VDOT means speed at maximum oxygen uptake. VDOT is a quantitative indicator of running ability. It combines the runner's maximum oxygen uptake (VO2max) and running economy to predict the runner's performance at different distances and set training intensity. The higher the VDOT, the stronger the running ability. VDOT can not only help runners predict competition results, but also be used to develop personalized training plans, including the allocation of training pace and training intensity. For example, a runner with a VDOT of 60 should have a 1.6 km race time of about 5 minutes, which is about 75 seconds per 400 meters. With VDOT, runners can determine all appropriate training paces.

[0029] Wearable smart devices refer to portable electronic devices that can be worn directly on the body or integrated into the user's clothes or accessories. These devices provide a variety of functions such as health monitoring, sports and fitness, navigation, communication and entertainment through built-in intelligent systems and Internet connections. Users can operate through intuitive interactive methods such as touch, voice and gestures. In addition, wearable smart devices can realize corresponding applications according to user needs in different fields. For example, wearable smart devices in the field of sports and fitness monitor the user's physiological indicators and sports data in real time, such as heart rate, number of steps, speed, calorie consumption, etc., by integrating heart rate sensors, GPS positioning and other technologies, and use intelligent algorithms to tailor training plans and fitness guidance for users. The embodiment of the present application integrates multiple sensors in the wearable smart device, collects user sports data through multiple sensors and calculates the running force value, which can achieve at least one of the following beneficial effects: adjust the user's exercise plan through the running force value to improve the user's athletic ability; or obtain the exercise distance in a variety of ways, the compatibility of the present application and the user's experience.

[0030] The following is explained with reference to the accompanying drawings:

[0031] Reference Figure 1 , which shows a flow chart of a motion optimization method provided in an embodiment of the present application.

[0032] like Figure 1 As shown, including:

[0033] S100, obtaining acceleration data, angular velocity data, heart rate and pace of the user during exercise.

[0034] S110, determining the motion data of the user during the motion process according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm, wherein the motion data includes the motion distance or the arm swing amplitude.

[0035] S120, when the exercise distance meets the target distance or the arm swing amplitude is less than the preset amplitude, stop obtaining the data of the user during the exercise, determine the running force value of the user during the exercise according to the heart rate, pace and running force value calculation model, and adjust the user's exercise plan according to the running force value.

[0036] Wearable smart devices have built-in accelerometers and gyroscopes. The accelerometer is used to obtain acceleration data of the user during exercise (or running), and the gyroscope is used to obtain angular velocity data of the user during exercise. The user's exercise process can be divided into two exercise modes, where the first exercise mode is an outdoor running competition mode in which the user exercises outdoors, and the second exercise mode is an indoor running competition mode in which the user exercises on an indoor treadmill. The judgment criteria for recording the user's heart rate and pace in the two exercise modes are different.

[0037] Taking the first exercise mode as an example, the wearable smart device obtains the user's heart rate, pace, acceleration data and angular velocity data in real time. According to the acceleration data, angular velocity data and the six-axis attitude fusion algorithm, the user's exercise distance during exercise is calculated. It is judged whether the exercise distance meets the target distance, wherein the target distance is given according to the user's exercise plan, and the exercise plan shows the user's exercise frequency and target distance within a week. When the exercise distance does not meet the target distance, the wearable smart device repeatedly obtains the user's heart rate, pace, acceleration data and angular velocity data during exercise; when the exercise distance meets the target distance, the first exercise mode stops and substitutes the user's heart rate and pace during exercise, the user's age, height and other data into the running force value calculation model to calculate the user's running force value during exercise. According to the calculated running force value, the user's exercise plan is adjusted, and the user's predicted performance at different milestones is obtained by looking up the table.

[0038] Taking the second exercise mode as an example, the wearable smart device obtains the user's heart rate, pace, acceleration data and angular velocity data in real time. According to the acceleration data, angular velocity data and the six-axis posture fusion algorithm, the swing amplitude of the user during exercise is calculated. It is determined whether the swing amplitude is less than the preset amplitude. When the swing amplitude is not less than the preset amplitude, the wearable smart device repeats the steps of obtaining the user's heart rate, pace, acceleration data, angular velocity data during exercise and determining the swing amplitude; when the swing amplitude gradually decreases and is less than the preset amplitude, the second exercise mode stops, and the user manually enters the treadmill distance in the wearable smart device. At the same time, the wearable smart device stops obtaining the user's acceleration data, angular velocity data, heart rate and pace during exercise, and substitutes the user's heart rate and pace, the user's age, height and other data obtained before the second exercise mode stops into the running force value calculation model to calculate the user's running force value during exercise. According to the calculated running force value, the user's exercise plan is adjusted, and the user's predicted performance at different milestones is obtained by looking up the table.

[0039] The embodiment of the present application determines the motion data of the user during the exercise according to the acceleration data, angular velocity data and six-axis posture fusion algorithm of the user during the exercise, and when the motion data meets the preset conditions, determines the running force value of the user during the exercise according to the heart rate, pace and running force value calculation model, and adjusts the user's exercise plan in a targeted manner through the running force value, thereby improving the user's athletic ability.

[0040] In the first exercise mode, the user's exercise distance may also be obtained in other ways, such as obtaining the user's speed data during exercise in real time through GPS, and determining the user's exercise distance during exercise in combination with the time data during exercise.

[0041] For example, the wearable smart device has built-in GPS, acceleration sensor and gyroscope. When entering the first sports mode, turn on the GPS on the wearable smart device. Determine whether the signal strength of GPS is normal. If the GPS signal strength is normal, obtain the speed data of the user during the exercise through GPS, and determine the movement distance of the user during the exercise based on the speed data and the time data during the exercise. When the exercise distance meets the target distance, the first sports mode stops and substitutes the heart rate, pace, user's age, height and other data into the running force value calculation model to calculate the running force value of the user during the exercise. If the GPS signal strength is abnormal, the acceleration data is obtained through the acceleration sensor, and the angular velocity data is obtained through the gyroscope. Then, the movement distance is determined based on the acceleration data and angular velocity data, and the six-axis posture fusion algorithm is used to determine the movement distance of the user during the exercise, and the subsequent running force value calculation process is executed.

[0042] In the embodiment of the present application, not only can the user speed data be obtained through GPS, and the user's movement distance can be determined based on the speed data and time data; the acceleration data can also be obtained through the acceleration sensor, and the angular velocity data can be obtained through the gyroscope. Then, the movement distance is determined based on the acceleration data and angular velocity data, and the six-axis posture fusion algorithm is used to determine the user's movement distance during the movement, thereby improving the compatibility of the present application and the user's experience.

[0043] In the first motion mode, the user's motion distance is determined by using acceleration data, angular velocity data, and the six-axis attitude fusion algorithm. It is necessary to first determine the user's arm swing amplitude, and then determine the motion distance based on the arm swing amplitude. Figure 2 , which shows a flow chart of determining a movement distance provided by an embodiment of the present application. Figure 2 As shown, including:

[0044] S200, determining the user's arm swing amplitude according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm.

[0045] S210, determining the stride length and number of steps of the user according to the arm swing amplitude and the motion posture model.

[0046] S220, determining the movement distance according to the stride length and the number of steps.

[0047] When the wearable smart device does not have a built-in GPS, or the wearable smart device has a built-in GPS and the GPS signal strength is abnormal, the user's arm swing amplitude is determined based on the acceleration data, angular velocity data, and the six-axis attitude fusion algorithm. Then, the arm swing amplitude is substituted into the motion posture model to calculate the user's stride length and number of steps during the exercise. Then, the stride length and number of steps are multiplied to obtain the user's exercise distance.

[0048] In one embodiment of the present application, when the motion data is the arm swing amplitude, it also includes: when the user's arm swing amplitude is not less than a preset amplitude, repeatedly executing the steps of obtaining acceleration data, angular velocity data, heart rate and pace, and determining the arm swing amplitude.

[0049] In the second exercise mode, when the user's arm swing amplitude is less than the preset amplitude, the user is considered to have stopped exercising. The user's running force value during exercise is determined based on the heart rate, pace and running force value calculation model during exercise. According to the calculated running force value, the predicted performance of the user at different milestones is obtained by looking up the table. The user's running force value is compared with its own historical records (such as the last running force value, the weekly running force value average, the monthly running force value average, etc.) to determine whether the user's athletic ability has improved. If the user's athletic ability has decreased, the user's exercise plan is adjusted according to the optimization rules and the running force value.

[0050] In the second exercise mode, if the user stops exercising for the first time period and then restarts exercising, and the arm swing amplitude is not less than the preset amplitude, the process of obtaining the acceleration data and angular velocity data, heart rate, pace, determining the arm swing amplitude, and determining the running power value during the exercise is repeated. The user is informed of the length of time and heart rate recovery status in the first time period by means of a message reminder.

[0051] In one embodiment of the present application, adjusting the user's exercise plan according to the running effort value specifically includes: comparing the running effort value with the user's historical records, and if the user's exercise ability decreases, adjusting the user's exercise plan according to the running effort value.

[0052] In the embodiment of the present application, no matter the user is in the first exercise mode or the second exercise mode, after determining the running power value of the user, the running power value of the user can be compared with the user's own historical records (such as the last running power value, the weekly running power value average, the monthly running power value average, etc.) to determine whether the user's athletic ability has improved. If the user's athletic ability has decreased, the user's exercise plan is adjusted according to the optimization rule and the running power value. If the user's athletic ability has not decreased, the user's exercise plan is not adjusted.

[0053] The embodiment of the present application also provides a motion optimization system, including a unit or means for executing any of the steps of the above motion optimization method. The motion optimization system includes: a sensor for obtaining acceleration data, angular velocity data, heart rate and pace of the user during the exercise process; a calculation unit for determining the motion data of the user during the exercise process based on the acceleration data, angular velocity data and the six-axis posture fusion algorithm, wherein the motion data includes the movement distance or the swing arm amplitude; the sensor is also used to stop obtaining the data of the user during the exercise process when the movement distance meets the target distance or the swing arm amplitude is less than the preset amplitude; the calculation unit is also used to determine the running force value of the user during the exercise process based on the heart rate, pace and running force value calculation model; the adjustment unit is used to adjust the user's exercise plan according to the running force value.

[0054] Wearable smart devices have built-in sensors, for example, sensors may include accelerometers and gyroscopes. Accelerometers are used to obtain acceleration data of users during exercise (or running), and gyroscopes are used to obtain angular velocity data of users during exercise. The user's exercise process can be divided into two exercise modes, wherein the first exercise mode is an outdoor running competition mode in which users exercise outdoors, and the second exercise mode is an indoor running competition mode in which users exercise on indoor treadmills. The judgment criteria for recording the user's heart rate and pace in the two exercise modes are different.

[0055] Taking the first exercise mode as an example, the user's heart rate, pace, acceleration data and angular velocity data are obtained in real time. The calculation unit calculates the user's movement distance during exercise based on the acceleration data, angular velocity data and the six-axis posture fusion algorithm. It is determined whether the movement distance meets the target distance, wherein the target distance is given according to the user's exercise plan, and the exercise plan shows the user's exercise frequency and target distance within a week. When the exercise distance does not meet the target distance, the wearable smart device repeatedly obtains the user's heart rate, pace, acceleration data and angular velocity data during exercise; when the exercise distance meets the target distance, the first exercise mode stops and substitutes the heart rate, pace, user's age, height and other data into the running force value calculation model through the calculation unit to calculate the user's running force value during exercise. The adjustment unit adjusts the user's exercise plan according to the calculated running force value, and obtains the user's predicted performance at different milestones by looking up the table.

[0056] Taking the second exercise mode as an example, the user's heart rate, pace, acceleration data and angular velocity data are obtained in real time. The calculation unit calculates the swing amplitude of the user during exercise according to the acceleration data, angular velocity data and the six-axis posture fusion algorithm. It is determined whether the swing amplitude is less than the preset amplitude. When the swing amplitude is not less than the preset amplitude, the wearable smart device repeats the steps of obtaining the user's heart rate, pace, acceleration data, angular velocity data during exercise and determining the swing amplitude; when the swing amplitude gradually decreases and is less than the preset amplitude, the second exercise mode stops, and the user manually enters the treadmill distance in the wearable smart device. At the same time, the sensor stops obtaining the acceleration data, angular velocity data, heart rate and pace of the user during exercise, and substitutes the user's heart rate and pace, the user's age, height and other data obtained before the second exercise mode stops into the running force value calculation model, and the running force value of the user during exercise is calculated by the calculation unit. The adjustment unit adjusts the user's exercise plan according to the calculated running force value, and obtains the user's predicted performance at different milestones by looking up the table.

[0057] In one embodiment of the present application, when the motion data is motion distance, the computing unit is also used to determine the user's arm swing amplitude based on the acceleration data, angular velocity data and six-axis posture fusion algorithm; the computing unit is also used to determine the user's stride length and number of steps based on the arm swing amplitude and the motion posture model; the computing unit is also used to determine the motion distance based on the stride length and number of steps.

[0058] When the wearable smart device does not have a built-in GPS, or the wearable smart device has a built-in GPS and the GPS signal strength is abnormal, the calculation unit determines the user's arm swing amplitude based on the acceleration data, angular velocity data, and the six-axis posture fusion algorithm. Then, the calculation unit substitutes the arm swing amplitude into the motion posture model to calculate the user's stride length and number of steps during the exercise. Then, the calculation unit multiplies the stride length and the number of steps to obtain the user's exercise distance.

[0059] In one embodiment of the present application, when the motion data is the arm swing amplitude, it also includes: an execution unit, which is used to repeat the steps of obtaining acceleration data, angular velocity data, heart rate and pace, and determining the arm swing amplitude when the user's arm swing amplitude is not less than a preset amplitude.

[0060] In one embodiment of the present application, the adjustment unit is further used to compare the running effort value with the user's historical records, and if the user's athletic ability decreases, adjust the user's exercise plan according to the running effort value.

[0061] An embodiment of the present application also provides a wearable smart device, comprising the motion optimization system described in any of the above embodiments.

[0062] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A motion optimization method, characterized in that: include: Obtain the user's acceleration data, angular velocity data, heart rate and pace during exercise; Determine the motion data of the user during the motion process according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm, wherein the motion data includes the motion distance or the arm swing amplitude; When the exercise distance meets the target distance or the arm swing amplitude is less than the preset amplitude, stop acquiring the data of the user during the exercise, determine the running force value of the user during the exercise according to the heart rate, the pace and the running force value calculation model, and adjust the user's exercise plan according to the running force value.

2. The motion optimization method according to claim 1, characterized in that: When the motion data is the motion distance, determining the motion data of the user during the motion process according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm specifically includes: Determining the arm swing amplitude of the user according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm; Determining the stride length and number of steps of the user according to the arm swing amplitude and the motion posture model; The movement distance is determined according to the stride and the number of steps.

3. The motion optimization method according to claim 2, characterized in that: Also includes: Acquiring speed data and time data of the user during exercise; The movement distance of the user during the movement is determined according to the speed data and the time data.

4. The motion optimization method according to claim 1, characterized in that: When the motion data is the arm swing amplitude, it also includes: When the arm swing amplitude of the user is not less than the preset amplitude, the steps of acquiring the acceleration data, the angular velocity data, the heart rate and the pace, and determining the arm swing amplitude are repeated.

5. The motion optimization method according to any one of claims 1 to 4, characterized in that: The step of adjusting the user's exercise plan according to the running effort value specifically includes: The running effort value is compared with the historical record of the user, and if the user's athletic ability decreases, the user's exercise plan is adjusted according to the running effort value.

6. A motion optimization system, characterized in that: include: Sensors are used to obtain acceleration data, angular velocity data, heart rate and pace of the user during exercise; a calculation unit, configured to determine the motion data of the user during the motion process according to the acceleration data, the angular velocity data and a six-axis posture fusion algorithm, wherein the motion data includes a motion distance or an arm swing amplitude; The sensor is further configured to stop acquiring the data of the user during exercise when the exercise distance meets the target distance or the arm swing amplitude is less than a preset amplitude; The calculation unit is further used to determine the running effort value of the user during exercise according to the heart rate, the pace and the running effort value calculation model; An adjustment unit is used to adjust the user's exercise plan according to the running effort value.

7. The motion optimization system according to claim 6, characterized in that: When the motion data is the motion distance, the calculation unit is further used to determine the arm swing amplitude of the user according to the acceleration data, the angular velocity data and the six-axis posture fusion algorithm; The calculation unit is also used to determine the stride and number of steps of the user according to the arm swing amplitude and the motion posture model; The calculation unit is further used to determine the movement distance according to the stride and the number of steps.

8. The motion optimization system according to claim 6, characterized in that: When the motion data is the arm swing amplitude, it also includes: An execution unit is used to repeatedly execute the steps of obtaining the acceleration data, the angular velocity data, the heart rate and the pace, and determining the arm swing amplitude when the arm swing amplitude of the user is not less than the preset amplitude.

9. The motion optimization system according to any one of claims 6 to 8, characterized in that: The adjustment unit is further configured to compare the running effort value with the historical record of the user, and if the user's athletic ability decreases, adjust the user's exercise plan according to the running effort value.

10. A wearable smart device, characterized in that: A motion optimization system comprising the motion optimization system described in any one of claims 6-9.

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