Step counting method, system, equipment and medium

Abstract

The invention belongs to the technical field of intelligent equipment, and particularly relates to a step counting method, a step counting system, a step counting device and a medium applied to a sports equipment use scenario, and the method comprises the following steps: when a user triggers a step counting detection function of an intelligent wearable device, acquiring acceleration waveform data of an arm of the user along a motion main axis direction; the direction of the motion main shaft is at least consistent with the forward-swinging / backward-swinging direction of the arm of the user; identifying a wave crest and a wave trough on the acceleration waveform data by utilizing a wave crest judgment condition and a wave trough judgment condition, and identifying whether the wave crest or the wave trough is effective by utilizing a wave crest effective judgment condition and a wave trough effective judgment condition; and when the effective wave crest or the effective wave trough is identified, adding one to the step number of the user so as to accumulate the total step number of the sports equipment used by the user. According to the method, the characteristic rule between the hand characteristics and the step number change when a user uses similar sports equipment such as an ellipsograph is utilized, so that step counting is carried out by recognizing the wave crest and the wave trough of the acceleration waveform of the arm in the movement main axis direction.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent devices, and in particular relates to a step counting method, system, device, and medium applied to a sports equipment usage scenario. Background Art

[0002] With the rapid development of technology, in order to meet people's growing fitness needs, various fitness equipment have appeared in gyms, among which elliptical machines, treadmills, rowing machines and other sports equipment are widely used. However, when elliptical machines and other sports equipment are used in daily fitness exercises, users often want to pay attention to their health information such as the number of steps and frequency during exercise in real time. Through long-term tracking and recording of data during exercise, they can monitor and optimize their own exercise intensity and methods, evaluate their physical fitness and health status, etc.

[0003] The above problems are currently generally solved by the counting function of elliptical machines, treadmills, rowing machines, etc., which completes the counting function through the movement cycle of the handle or pedal. However, there are still some problems with the counting method of the equipment itself: it cannot continuously monitor for a long time, nor can it intelligently and automatically store health data for users to view later. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to propose a pedometer method that can be applied to smart wearable devices, which can monitor and count the number of steps of users when using sports equipment. It can not only provide more comprehensive health data tracking and feedback, but also formulate more scientific training plans through long-term data accumulation.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a pedometer method applied to sports equipment usage scenarios, including: when a user triggers the pedometer detection function of a smart wearable device, obtaining acceleration waveform data of the user's arm along the main axis of motion; the main axis of motion direction is at least consistent with the direction of the user's arm swinging forward / backward; using peak judgment conditions and trough judgment conditions to identify peaks and troughs on the acceleration waveform data, and using peak validity judgment conditions and trough validity judgment conditions to identify whether the peaks or troughs are valid; when a valid peak or valid trough is identified, the number of steps of the user is added by one to accumulate the total number of steps the user takes when using the sports equipment.

[0006] According to a specific embodiment of the present invention, before using the peak judgment condition and the trough judgment condition to identify the peaks and troughs on the acceleration waveform data, it also includes: performing an anti-shake test to identify whether the user is using sports equipment: if so, the number of steps during the anti-shake test is counted into the total number of steps of the user; if not, the number of steps during the anti-shake test is cleared to zero, and the step counting detection function is terminated.

[0007] According to a specific embodiment of the present invention, the step of performing an anti-shake test to identify whether the user is currently using a sports device includes: when the user triggers the step counting detection function of the smart wearable device, using the peak judgment condition and the trough judgment condition within a preset first time period to identify the peaks and troughs on the acceleration waveform data, and using the peak validity judgment condition and the trough validity judgment condition to identify whether the peak or trough is valid, so as to count the user's steps based on the valid peaks and valid troughs within the preset first time period; if the number of steps is less than a preset threshold, it is identified that the user is not using a sports device; if the number of steps is greater than or equal to the preset threshold, it is identified that the user is using a sports device.

[0008] According to a specific embodiment of the present invention, the steps of using peak determination conditions and trough determination conditions to identify peaks and troughs on the acceleration waveform data, and using peak validity determination conditions and trough validity determination conditions to identify whether the peaks or troughs are valid include: using peak validity determination conditions and trough validity determination conditions to identify whether the peaks or troughs are valid: if the peaks or troughs are invalid, they are ignored.

[0009] According to a specific embodiment of the present invention, the peak determination condition is as follows: when the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is greater than or equal to a preset first value, then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak; or when the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is equal to a preset second value, and the difference between the acceleration value at the current sampling moment and the previous trough is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak.

[0010] According to a specific embodiment of the present invention, the trough determination condition is as follows: when the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a decreasing state, the acceleration value at the next sampling moment is in an increasing state, and the number of decreasing points of the acceleration value at the current sampling moment is greater than or equal to a preset first value, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough; or when the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a decreasing state, the acceleration value at the next sampling moment is in an increasing state, and the number of decreasing points of the acceleration value at the current sampling moment is equal to a preset second value, and the difference between the acceleration value at the current sampling moment and the previous peak is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough.

[0011] According to a specific embodiment of the present invention, the peak validity judgment conditions include: a first condition, the peak value is at least greater than or equal to an adaptive first threshold; a second condition, the number of climbing points of the peak at least exceeds a preset third value; a third condition, the time difference between the peak moment and the previous trough moment is at least greater than or equal to a preset difference; wherein, the previous trough moment is at least greater than the previous peak moment; a fourth condition, the difference between the peak and the previous trough is at least greater than an adaptive difference threshold; wherein, when the peak meets all conditions, the peak is considered valid.

[0012] According to a specific embodiment of the present invention, it also includes: updating the adaptive first threshold and the adaptive difference threshold; wherein, when a peak is identified, if the time difference between the peak moment and the previous trough moment is greater than or equal to the preset difference, and the difference between the peak and the previous trough is greater than the initial value of the adaptive difference threshold, then the adaptive first threshold and the adaptive difference threshold are updated.

[0013] According to a specific embodiment of the present invention, the conditions for determining whether the trough is valid include: a first condition, the trough value is at least less than or equal to an adaptive second threshold value; a second condition, the number of drop points of the trough at least exceeds a preset third value; a third condition, the time difference between the trough moment and the previous peak moment is at least greater than or equal to a preset difference value; wherein, the previous peak moment is at least greater than the previous trough moment; a fourth condition, the difference between the previous peak and the trough is at least greater than an adaptive difference threshold value; wherein, the trough is considered valid when it meets all conditions.

[0014] According to a specific embodiment of the present invention, it also includes: updating the adaptive second threshold and the adaptive difference threshold; wherein, when a peak is identified, if the time difference between the peak moment and the previous trough moment is greater than or equal to the preset difference, and the difference between the peak and the previous trough is greater than the initial value of the adaptive difference threshold, then the adaptive first threshold and the adaptive difference threshold are updated.

[0015] According to a specific embodiment of the present invention, the adaptive first threshold value / adaptive second threshold value is updated as follows: a new adaptive first threshold value / adaptive second threshold value is calculated based on a preset coefficient and the latest peak and trough; wherein the calculation formula is as follows: , , represents the new adaptive first threshold value / adaptive second threshold value, represents the adaptive first threshold value / adaptive second threshold value, Indicates the latest peak value, Indicates the latest trough value, , represents the preset coefficient, and , The value range is between 0 and 1.

[0016] According to a specific embodiment of the present invention, the adaptive difference threshold is updated as follows: an array is created in advance, and the array stores a preset number of differences between adjacent peaks and troughs; the newly calculated difference between the peak and the previous trough or the difference between the trough and the previous peak is updated to the array, and a difference that is relatively outdated in time is deleted from the array; based on all the differences in the array, a new adaptive difference threshold is obtained by summing and averaging according to preset coefficients.

[0017] According to a specific embodiment of the present invention, it also includes: monitoring the total number of steps of the user in real time, and when it is detected that the total number of steps of the user has not changed within a preset second time period, recognizing that the user has stopped using the sports equipment, and ending the step counting detection function.

[0018] According to a specific embodiment of the present invention, the sports equipment includes an elliptical machine, a treadmill, and a rowing machine, and the direction of the main axis of movement is parallel to the ground and is consistent with the direction of the forward / backward swing of the user's arm.

[0019] According to a specific embodiment of the present invention, the method further includes: performing mean filtering on the acceleration waveform data according to the following formula: , in, Indicates the window length, It represents the data of the main axis data x after mean filtering. Represents the data of i moments before the current moment t.

[0020] A pedometer system applied to a sports equipment usage scenario, comprising: a data acquisition module, for obtaining acceleration waveform data of the user's arm along the main axis of motion when the user triggers the pedometer detection function of an intelligent wearable device; the main axis of motion direction is at least consistent with the direction in which the user's arm swings forward / backward; a data processing module, for identifying peaks and troughs on the acceleration waveform data using peak determination conditions and trough determination conditions, and identifying whether a peak or trough is valid using a peak validity determination condition and a trough validity determination condition; a pedometer display module, for adding one to the number of steps of the user when a valid peak or valid trough is identified, so as to accumulate the total number of steps of the user using the sports equipment.

[0021] A smart wearable device includes a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the above-mentioned method is implemented.

[0022] A readable storage medium includes a program. When the program is run on a smart wearable device, the smart wearable device executes the method described above.

[0023] The present invention provides a pedometer method, which theoretically analyzes the characteristic laws between hand movements and step count changes during the use of ellipsometers and similar sports equipment, so that the acceleration data of the user's arm along the main axis of movement can be collected by a smart wearable device to count the steps, effectively avoiding the interference of acceleration data on other axes, improving the accuracy and efficiency of detection, and greatly facilitating users to use smart wearable devices for long-term sports data statistics and health management. In combination with other related parameters such as time and heart rate, it helps users to carry out more professional and detailed sports planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the ellipsometer provided by the present invention; Figure 2 This is a schematic diagram of the three-axis directions of the accelerometer provided by the present invention in a smart wearable device; Figure 3 A schematic diagram of waveforms of acceleration and velocity of the ellipsometer provided by the present invention along the X-axis direction; Figure 4 A schematic diagram of key nodes of the arm during the motion of the elliptical machine provided by the present invention; Figure 5 A flowchart of a specific embodiment of a step counting method applied to a sports equipment usage scenario provided by the present invention; Figure 6 A flowchart of another specific embodiment of a step counting method applied to a sports equipment usage scenario provided by the present invention; Figure 7 A flowchart of another specific embodiment of a step counting method applied to a sports equipment usage scenario provided by the present invention; Figure 8 The actual data waveform diagram in the X-axis direction collected by the accelerometer during the ellipsometer exercise provided by the present invention; Fig. 9 A schematic structural diagram of a specific embodiment of a pedometer system applied to a sports equipment usage scenario provided by the present invention; Fig.10 The present invention provides a structural block diagram of a specific embodiment of an electronic device. DETAILED DESCRIPTION

[0025] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0027] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0028] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0029] First of all, it should be explained that in order to enable people in this technical field to better understand the present application scheme, the technical background of this application is explained accordingly.

[0030] In this embodiment, an ellipsometer is used as an example to analyze the motion characteristics of a user when using the ellipsometer. Specifically, Figure 1 Figure 1 is a schematic diagram of an ellipsometer. When a user uses the exercise equipment, pedal 3 is lifted, armrest 1 is pushed forward, pedal 4 is pressed down, and armrest 2 is pulled back. It can be seen that when the foot on pedal 3 takes a step forward, pedal 3 will be pressed down, and armrest 1 will be pulled back; when pedal 4 is lifted and armrest 2 is pushed forward, it means that the foot on pedal 4 has taken a step forward. In this way, it can be found that the user's foot movement and hand movement have corresponding movement patterns. The pushing and pulling movements of the user's arm holding the handle of the ellipsometer correspond to a step forward taken by the user. In this way, we can reversely infer the number of steps of the user by counting the user's hand movement patterns, and the user's hand movement patterns can be monitored through smart wearable devices, such as smart watches, smart rings, etc.

[0031] Secondly, when the smart wearable device is mounted on the user's hand, the three axes of the built-in accelerometer will have different distribution directions. For details, please refer to Figure 2 As shown. The positive direction of the X axis is parallel to the arm and points to the finger in the horizontal plane, the positive direction of the Y axis is perpendicular to the arm and points to the user's body in the horizontal plane, and the positive direction of the Z axis is perpendicular to the horizontal plane and upward. Figure 1 In the ellipsometer, when the user wearing the smart wearable device holds one of the handles of the ellipsometer and steps, an axis parallel to the arm movement direction is generated, which is called the main movement axis. At this time, the user's arm movement characteristics on the ellipsometer are mainly distributed on this axis. Specifically, in actual applications, the main movement direction of the arm during the ellipsometer movement is parallel to the horizontal direction of the ground, that is, corresponding to Figure 2 The X-axis direction in the figure, therefore, in this application, only the X-axis data of the accelerometer in the smart wearable device is used to count the steps of the user's elliptical machine, rather than using the synthetic data of the three axes, which helps to reduce a lot of interference caused by other axis data. It is understandable that for the motion characteristics of other sports equipment when in use, it is necessary to specifically analyze the corresponding main axis of motion according to the actual situation, which may be the Y-axis or the Z-axis, so as to count the steps with the data on the main axis of motion. Similarly, the direction distribution of the X, Y, and Z axes can also be customized. Without making too many restrictions on this, the modifications and embellishments made to the embodiments of the present invention by those skilled in the art without departing from the spirit of the present invention still fall within the scope of the invention application of the present invention.

[0032] Further, when Figure 1 When the arm holding handle 1 moves along the positive direction of the X-axis, that is, the arm pushes away from the body: when it first approaches the body, the arm is stationary, and then begins to accelerate in the positive direction of the X-axis. At this time, the acceleration value is in the same direction as the positive direction of the X-axis, which is a positive value. When the arm passes a certain position in the complete pushing action, the arm begins to decelerate in the positive direction of the X-axis until it reaches the farthest point of the arm, and the arm reaches a stationary state again. In order to achieve the deceleration effect, the direction of the acceleration is opposite to the positive direction of the X-axis, which is a negative value. It can be seen from this that the acceleration along the X-axis direction during the pushing process of the arm changes from a positive value to a negative value. On the contrary, the acceleration along the X-axis direction during the pulling process of the arm changes from a negative value to a positive value.

[0033] Therefore, when the user is performing ellipsometer exercise, the acceleration waveform along the X-axis direction can be simplified as follows: Figure 3 As shown in the figure, accelerometer means acceleration and velocity means speed. It can be clearly seen that the acceleration data presents trigonometric function characteristics, with regular alternation of peaks and troughs, which is closely related to the regular push and pull movements of the user's arms when performing elliptical motion.

[0034] In this regard, the movement process of the arm can be simplified as follows Figure 4 Several key motion nodes are shown: when the swing direction of the arm is the same as the positive direction of the X-axis, after one of the user's legs takes a step, when the arm is at point A, the speed is the smallest and the positive acceleration value is the largest. It starts to accelerate from point A to point B, but the acceleration value is decreasing. After reaching point B, the forward speed is the largest and the acceleration value is the smallest. Then it starts to decelerate from point B to point C, and the reverse acceleration value is increasing. After reaching point C, the speed is the smallest and the reverse acceleration is the largest. This shows that the change in the acceleration of the arm from point A to point B and then to point C is from the maximum in the forward direction to 0 and then to the maximum in the reverse direction, which corresponds exactly to Figure 3 The solid line shows the process from the peak A to B and then to the trough C. On the contrary, when the swing direction of the arm is opposite to the positive direction of the X-axis, after the user takes a step with the other leg, the arm is at point C with the minimum speed and the maximum reverse acceleration value. It starts to accelerate in the reverse direction from point C to point B, but the acceleration value is decreasing. After reaching point B, the reverse speed is the maximum and the acceleration value is the minimum. Then it starts to decelerate from point B to point A, and the forward acceleration value increases. After reaching point A, the speed is the minimum and the forward acceleration is the maximum. This shows that the change of the acceleration of the arm from point C to point B and then to point A is the maximum in the reverse direction to 0 and then the maximum in the forward direction, which corresponds exactly to Figure 3 The process from trough C to B and then to peak A. It can be found that the changes from peak to trough and from trough to peak on the acceleration waveform data correspond to a step taken by the user. Then, by using the algorithm to count the total number of peaks and troughs, the total number of steps taken by the user when using the elliptical machine can be obtained, and combined with the exercise time information, other health information of the user's exercise such as step frequency and calories can be obtained.

[0035] Therefore, based on the motion characteristics of the user when using the elliptical machine, this application provides a step counting method applied to the use scenario of sports equipment. Of course, the above description is only explained by taking the elliptical machine as an example but is not limited to the scope of application of the method. For example, when the user uses a treadmill or a rowing machine, the arm characteristics are basically similar to those of the elliptical machine, and the method provided by this application can be directly adopted. In addition, for some other sports equipment, the arm movement and step count correlation law can be specifically analyzed for adjustment, and the acceleration data of the arm corresponding to the main axis of movement can be obtained to count the steps, and there are no excessive restrictions on this.

[0036] Example 1 See also Figure 5 , 6 A step counting method applied to a sports equipment usage scenario is shown, comprising: Step S100, when the user triggers the step counting detection function of the smart wearable device, the acceleration waveform data of the user's arm along the main axis of movement is obtained; the main axis of movement is at least consistent with the direction of the user's arm swinging forward / backward.

[0037] Step S200, using the peak determination condition and the trough determination condition to identify the peaks and troughs on the acceleration waveform data, and using the peak validity determination condition and the trough validity determination condition to identify whether the peak or trough is valid.

[0038] Step S300: When a valid peak or a valid trough is identified, the number of steps of the user is added by one to accumulate the total number of steps of the user using the sports equipment.

[0039] It can be understood here that since the pedometer method provided in this application needs to obtain the acceleration data of the user's hand movement, the corresponding smart wearable device is at least worn on the user's hand, but the type of smart wearable device is not limited. In order to facilitate the explanation of the technical solution, a smart watch is taken as an example.

[0040] Based on the above analysis, when the user triggers the step detection function of the smart watch, the built-in accelerometer of the smart watch will collect acceleration data of the user's arm in different directions. However, in this embodiment, the acceleration data of the arm along the main axis of motion is mainly used, that is, the acceleration data in the X-axis direction, and the data in the Y-axis and Z-axis directions or the synthesized data are not used. Moreover, when the user's arm is not in a static state, a motion trajectory will be formed accordingly, accompanied by continuous speed changes, that is, the acceleration is also a continuous change process. Therefore, the acquired acceleration data is actually a continuous waveform change. In this regard, by identifying such as Figure 3 The peaks and troughs of the acceleration waveform shown in the figure are used to count steps, and then combined with the exercise time information, other health information of the user's exercise such as step frequency and calories can be obtained.

[0041] First, the data needs to be averaged and filtered to reduce the interference caused by the burrs in the waveform changes to a great extent. The specific calculation formula is as follows: , in, Indicates the window length, It represents the data of the main axis data x after mean filtering. Represents the data of i moments before the current moment t, that is, the above formula represents the average of the data of window_len length from the current moment t to the current moment t, as the data after mean filtering at the current moment t .

[0042] Correspondingly, the larger the window length is, the better the smoothing effect is, but it is also easy to drown out normal waveform changes, and the smaller the window length is, the less ideal the smoothing effect is. Therefore, in this embodiment, the preferred value of the window length is 8.

[0043] Of course, data preprocessing is not limited to mean filtering, and can be increased / reduced according to actual needs. After preprocessing the acceleration waveform data, the peaks and troughs on the acceleration waveform can be identified to accumulate the total number of steps of the user. Here, in order to avoid similar waveform jitter caused by other user behaviors, which may lead to misidentification of step counting, for example, when the user enters the ellipsometer special sports interface of the smart watch, he does not necessarily start exercising directly (using the ellipsometer for exercise), which may cause similar acceleration waveform changes, but this behavior will not occur continuously. Therefore, an anti-shake test phase is set. Only when the accumulated steps in the preset first time period meet the requirements will the step counting phase be officially entered, that is, the number of steps counted in the anti-shake test phase is greater than or equal to the preset threshold, otherwise the number of steps in the anti-shake time will be cleared.

[0044] For details, see Figure 7 As shown, when the step detection function of the smart watch is not enabled, step_state is set to 0, and when the step detection function is just enabled, the anti-shake test phase is first entered, step_state is set to 1, and the anti-shake test is started. Here, the first time period can be preset as 5s, and the number of steps generated by the user within this 5s is counted, that is, whether the total number of peaks and troughs reaches the preset threshold: if it does not meet the standard, it means that the user has stopped exercising, and the counted steps are directly cleared to zero, not used for display, and the step detection function is exited, that is, step_state is reset to 0; if it meets the standard, it means that the user has started to use the ellipsometer normally, that is, entering the formal step counting phase, step_state needs to be set to 2, and at the same time, the number of steps counted in the anti-shake test phase will be counted into the total number of steps of the user using the ellipsometer. When the step counting phase is officially entered, the acceleration value collected in real time can be identified, or one or more corresponding acceleration values ​​can be identified according to the preset unit time, so as to count the peaks and troughs on the acceleration waveform, and there are no excessive restrictions on this.

[0045] It should also be added here that the accumulated step count changes can also be verified in real time to identify whether the user has stopped using the elliptical trainer. For this, a second time period can be set, for example 10 seconds, and if no new peaks or troughs are identified within 10 seconds, reflecting that the user's step count has not changed, the pedometer detection function is exited, step_state is reset to 0, and the next time the pedometer detection function is turned on.

[0046] The above is the complete process of smart watch step counting when the user uses an elliptical trainer or other sports equipment. In addition, for details on how to identify the peaks and troughs in the acceleration waveform data, please refer to the following.

[0047] For the acceleration value at the current sampling moment, the peak determination condition is first used to determine whether it is a peak. If so, it is necessary to further determine whether it is a valid peak to avoid the influence of other glitch peaks. If not, the trough determination condition is used to determine whether it is a trough. Similarly, when it is determined to be a trough, it is necessary to further determine whether it is a valid trough. If it is neither a peak nor a trough, wait for the acceleration value at the next sampling moment to be identified, and repeat the cycle to count the number of peaks and troughs.

[0048] Among them, the peak determination condition mainly refers to: when the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is greater than or equal to the preset first value (for example, 2), then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak. Alternatively, when the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is equal to the preset second value (for example, 1), and the difference between the acceleration value at the current sampling moment and the previous trough is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak. The trough determination condition mainly refers to: when the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a downward state, the acceleration value at the next sampling moment is in an upward state, and the number of decreasing points of the acceleration value at the current sampling moment is greater than or equal to the preset first value, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough. Alternatively, when the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a downward state, the acceleration value at the next sampling moment is in an upward state, and the number of decreasing points of the acceleration value at the current sampling moment is equal to the preset second value, and at the same time, the difference between the acceleration value at the current sampling moment and the previous peak is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough.

[0049] The peak validity judgment conditions mainly include the following: the first condition, the peak value is at least greater than or equal to the adaptive first threshold; the second condition, the number of climbing points of the peak is at least greater than the preset third value; the third condition, the time difference between the peak moment and the previous trough moment is at least greater than the preset difference; among which, the previous trough moment is at least greater than the previous peak moment; the fourth condition, the difference between the peak and the previous trough is at least greater than the adaptive difference threshold; in order to avoid burr peaks near the peak, or detect two consecutive peaks, and the peak is considered valid when it meets all the above conditions.

[0050] Similarly, the conditions for determining whether a trough is valid also include the following: first, the trough value is at least less than or equal to the adaptive second threshold; second, the number of drop points in the trough is at least greater than a preset third value; third, the time difference between the trough moment and the previous peak moment is at least greater than or equal to a preset difference; wherein the previous peak moment is at least greater than the previous trough moment; fourth, the difference between the trough and the previous peak moment is at least greater than the adaptive difference threshold; and when the trough meets all conditions, the trough is considered valid.

[0051] Based on the above, the user's cumulative steps are counted only when the peak is a valid peak or the trough is a valid trough, that is, when a valid peak or a valid trough is identified, the user's steps are counted plus one. Invalid peaks and troughs are ignored and considered as glitches.

[0052] In addition, as time goes by when the user uses the sports equipment, the movements may become more and more irregular, and the corresponding peak and trough values ​​will be adjusted. In this regard, the above-mentioned adaptive first threshold, adaptive second threshold, and adaptive difference threshold will also be updated synchronously to adapt to the user's arm movements. Specifically, when a peak is identified, the time difference between the corresponding peak moment and the previous trough moment is greater than or equal to the preset difference, and the difference between the peak and the previous trough is greater than the initial value of the adaptive difference threshold, then the adaptive first threshold, adaptive second threshold, and adaptive difference threshold are updated accordingly. Regardless of whether the peak is a valid peak, the above thresholds will be updated to avoid the situation where the arm movement amplitude becomes smaller, resulting in a smaller change in the waveform amplitude, and the step counting cannot continue to follow.

[0053] Therefore, the adaptive first threshold and the adaptive second threshold can be updated according to the following formula: , , in, represents the new adaptive first threshold value / adaptive second threshold value, represents the adaptive first threshold value / adaptive second threshold value, Indicates the latest peak value, Indicates the latest trough value, , represents the preset coefficient, and , The value range is between 0 and 1. If the value of is too large, the new adaptive first threshold / adaptive second threshold will be closer to the peak, otherwise it will be closer to the trough. Similarly, The value of represents the update speed of the adaptive first threshold / adaptive second threshold. The larger the value, the more relevant the final updated adaptive first threshold / adaptive second threshold is to the historical value, and the slower the update speed. The smaller the value, the more relevant it is to the new value, and the faster the update speed.

[0054] The adaptive difference threshold can be updated according to the following formula: , in, represents the new adaptive difference threshold, represents the number of differences in the array, It means the sum of all the differences in the array. Represents a preset coefficient to proportionally determine the difference between the peak and the trough. Generally, this value is greater than 1, indicating that the fluctuation between the peak and the trough is at least greater than the adaptive difference threshold updated using this coefficient.

[0055] It should be noted that before updating the adaptive difference threshold, an array needs to be created in advance, and the array stores the differences between a preset number of adjacent peaks and troughs. In this embodiment, the specific number is set to 3. When a peak or trough is identified, the difference between the peak and the previous trough or the difference between the trough and the previous peak will be calculated accordingly, so that it can be updated to the array. At the same time, the differences in the array that are out of date relative to the time are removed, that is, storing a difference requires deleting an initial difference. Finally, when the adaptive difference threshold needs to be updated, the sum can be calculated and calculated according to the preset coefficient. Take the average value to get the new adaptive difference threshold.

[0056] Based on the above content, the accuracy of the user's cumulative step count when using the elliptical machine can be greatly improved. In a specific implementation, see Figure 8 The data shown shows that a user wearing a smart watch takes 100 steps on an ellipsometer, where filter_x represents the x-axis data after mean filtering, peak represents the peak, valley represents the trough, and threadvalue represents the adaptive updated threshold, i.e., the adaptive first threshold and the adaptive second threshold. It can be found that the data characteristics are consistent with the waveform characteristics of the theoretical analysis, and the pedometer method provided in this application can find 99 peaks and troughs, i.e., the user's cumulative number of steps is 99, which is basically consistent with the theoretical data, fully demonstrating the effectiveness of the pedometer method provided in this application.

[0057] It should be noted that the step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0058] Example 2 See also Fig. 9 As shown, this embodiment also provides a pedometer system applied to a sports equipment usage scenario, including: The data acquisition module 10 is used to obtain the acceleration waveform data of the user's arm along the main axis of movement when the user triggers the step counting detection function of the smart wearable device; the main axis of movement is at least consistent with the direction of the user's arm swinging forward / backward.

[0059] The data processing module 20 is used to identify the peaks and troughs on the acceleration waveform data using the peak determination condition and the trough determination condition, and to identify whether the peak or trough is valid using the peak validity determination condition and the trough validity determination condition.

[0060] The step counting and display module 30 is used to count the number of steps of the user plus one when a valid peak or a valid trough is identified, so as to accumulate the total number of steps taken by the user using the sports equipment.

[0061] It should be noted that the pedometer system for sports equipment use scenarios provided in the above embodiments and the pedometer method for sports equipment use scenarios provided in the above embodiment 1 belong to the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiments and will not be repeated here. In actual applications, the pedometer method for sports equipment use scenarios provided in the above embodiment 1 can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0062] Example 3 See also Fig.10 As shown, an embodiment of the present application further provides a smart wearable device, comprising a memory 2, a processor 1, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the above methods are implemented.

[0063] Among them, the memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, card-type memory (for example: SD or DX memory, etc.), magnetic memory, etc. The memory may be an internal storage unit of the smart wearable device in some embodiments. Furthermore, the memory may also include both an internal storage unit of the smart wearable device and an external storage device. The memory can be used not only to store application software and various types of data installed in the smart wearable device, but also to temporarily store data that has been output or is to be output.

[0064] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor is the control core (Control Unit) of the smart wearable device, and uses various interfaces and lines to connect the various components of the entire smart wearable device, and executes various functions of the smart wearable device and processes data by running or executing programs or modules stored in the memory, and calling data stored in the memory.

[0065] The processor executes the operating system of the smart wearable device and various installed application programs. The processor executes the application programs to implement the steps in the above method embodiment.

[0066] Exemplarily, the program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of program instruction segments capable of completing specific functions, which are used to describe the execution process of the program in the electronic device.

[0067] The above integrated unit implemented in the form of software function modules can be stored in a readable storage medium. The above software function modules are stored in a storage medium and include several instructions for enabling the smart wearable device to perform some functions of various embodiments of the present invention.

[0068] In summary, the present invention provides a pedometer method, which theoretically analyzes the characteristic laws between hand movement and step number changes during the use of elliptical machines and similar sports equipment, so that the acceleration data of the user's arm along the main axis of motion can be collected by a smart wearable device to count the steps, effectively avoiding the interference of acceleration data on other axes, improving the accuracy and efficiency of detection, and greatly facilitating users to use smart wearable devices for long-term sports data statistics and health management. Combined with other related parameters such as time and heart rate, it helps users to make more professional and detailed exercise plans.

[0069] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A step counting method applied to a sports equipment usage scenario, characterized in that: include: When the user triggers the step counting detection function of the smart wearable device, the acceleration waveform data of the user's arm along the main axis of motion is obtained; The direction of the main axis of movement is at least consistent with the direction in which the user's arm swings forward / backward; Using the peak determination condition and the trough determination condition to identify the peaks and troughs on the acceleration waveform data, and using the peak validity determination condition and the trough validity determination condition to identify whether the peak or trough is valid; When a valid peak or a valid trough is identified, the number of steps of the user is added by one to accumulate the total number of steps the user has taken using the sports equipment.

2. The step counting method for sports equipment use scenario according to claim 1, characterized in that: Before using the peak determination condition and the trough determination condition to identify the peaks and troughs on the acceleration waveform data, the method further includes: Perform anti-shake testing to identify if the user is using a sports device: If yes, the number of steps taken during the anti-shake test is counted into the user's total number of steps; If not, the number of steps during the anti-shake test is reset to zero, and the step counting detection function is terminated.

3. The step counting method for sports equipment use scenario according to claim 2, characterized in that: The steps for conducting a shake test to identify whether a user is using a sports device include: When the user triggers the step counting detection function of the smart wearable device, the peaks and troughs on the acceleration waveform data are identified within a preset first time period using the peak determination condition and the trough determination condition, and whether the peaks or troughs are valid is identified using the peak validity determination condition and the trough validity determination condition, so as to count the number of steps of the user based on the valid peaks and valid troughs within the preset first time period; If the number of steps is less than a preset threshold, it is recognized that the user is not using the exercise equipment; If the number of steps is greater than or equal to a preset threshold, it is recognized that the user is using a sports device.

4. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The steps of identifying the peaks and troughs on the acceleration waveform data by using the peak determination condition and the trough determination condition, and identifying whether the peaks or troughs are valid by using the peak validity determination condition and the trough validity determination condition include: Use the peak validity judgment condition and the trough validity judgment condition to identify whether the peak or trough is valid: If a peak or trough is invalid, it is ignored.

5. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The peak determination conditions are as follows: When the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is greater than or equal to a preset first value, then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak; or When the acceleration waveform data is in an upward trend, if the acceleration value at the current sampling moment is in an upward state, the acceleration value at the next sampling moment is in a downward state, and the number of climbing points of the acceleration value at the current sampling moment is equal to the preset second value, and the difference between the acceleration value at the current sampling moment and the previous trough is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a peak moment, and the corresponding acceleration value is the peak.

6. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The trough determination conditions are as follows: When the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a decreasing state, the acceleration value at the next sampling moment is in an increasing state, and the number of decreasing points of the acceleration value at the current sampling moment is greater than or equal to a preset first value, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough; or When the acceleration waveform data is in a downward trend, if the acceleration value at the current sampling moment is in a decreasing state, the acceleration value at the next sampling moment is in an increasing state, and the number of decreasing points of the acceleration value at the current sampling moment is equal to the preset second value, and the difference between the acceleration value at the current sampling moment and the previous peak is greater than the initial value of the adaptive difference threshold, then the current sampling moment is considered to be a trough moment, and the corresponding acceleration value is the trough.

7. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The peak validity determination conditions include: The first condition is that the peak value is at least greater than or equal to the adaptive first threshold; The second condition is that the number of climbing points of the wave crest exceeds at least a preset third value; The third condition is that the time difference between the peak moment and the previous trough moment is at least greater than or equal to a preset difference; wherein the previous trough moment is at least greater than the previous peak moment; The fourth condition is that the difference between the peak and the previous trough is at least greater than the adaptive difference threshold; When a peak satisfies all conditions, the peak is considered valid.

8. The step counting method for sports equipment use scenario according to claim 7, characterized in that: Also includes: updating the adaptive first threshold and the adaptive difference threshold; Among them, when a peak is identified, if the time difference between the peak moment and the previous trough moment is greater than or equal to the preset difference, and the difference between the peak and the previous trough is greater than the initial value of the adaptive difference threshold, the adaptive first threshold and the adaptive difference threshold are updated.

9. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The trough validity determination conditions include: The first condition is that the trough value is at least less than or equal to the adaptive second threshold; The second condition is that the number of falling points of the trough at least exceeds a preset third value; The third condition is that the time difference between the trough moment and the previous peak moment is at least greater than or equal to a preset difference; wherein the previous peak moment is at least greater than the previous trough moment; The fourth condition is that the difference between the previous peak and the trough is at least greater than the adaptive difference threshold; When the trough satisfies all conditions, the trough is considered valid.

10. The step counting method for sports equipment use scenario according to claim 9, characterized in that: Also includes: updating the adaptive second threshold and the adaptive difference threshold; Among them, when a peak is identified, if the time difference between the peak moment and the previous trough moment is greater than or equal to the preset difference, and the difference between the peak and the previous trough is greater than the initial value of the adaptive difference threshold, the adaptive first threshold and the adaptive difference threshold are updated.

11. The step counting method for sports equipment use scenario according to claim 8 or 10, characterized in that: The adaptive first threshold value / adaptive second threshold value is updated as follows: A new adaptive first threshold value / adaptive second threshold value is calculated based on the preset coefficient and the latest peak and trough; The calculation formula is as follows: ， ， represents the new adaptive first threshold value / adaptive second threshold value, represents the adaptive first threshold value / adaptive second threshold value, Indicates the latest peak value, Indicates the latest trough value, , represents the preset coefficient, and , The value range is between 0 and 1.

12. The step counting method for sports equipment use scenario according to claim 8 or 10, characterized in that: The adaptive difference threshold is updated as follows: Pre-create an array, and the array stores the difference between a preset number of adjacent peaks and troughs; The newly calculated difference between the peak and the last trough or the difference between the trough and the last peak is updated into the array, and a relative time-expired difference is deleted from the array; Based on all the differences in the array, a new adaptive difference threshold is obtained by summing and averaging the values ​​according to preset coefficients.

13. The step counting method for sports equipment use scenario according to claim 1, characterized in that: Also includes: The total number of steps of the user is monitored in real time, and when it is detected that the total number of steps of the user has not changed within a preset second time period, it is recognized that the user has stopped using the sports equipment and the step counting detection function is terminated.

14. The step counting method for sports equipment use scenario according to claim 1, characterized in that: The sports equipment includes an elliptical machine, a treadmill, and a rowing machine, and the direction of the main axis of movement is parallel to the ground and is consistent with the direction of the forward / backward swing of the user's arm.

15. The step counting method for sports equipment use scenario according to claim 1, characterized in that: Also includes: The acceleration waveform data is mean filtered according to the following formula: ， in, Indicates the window length, It represents the data of the main axis data x after mean filtering. Represents the data of i moments before the current moment t.

16. A pedometer system applied to sports equipment usage scenarios, characterized in that: include: The data acquisition module is used to obtain the acceleration waveform data of the user's arm along the main axis of motion when the user triggers the step counting detection function of the smart wearable device; The direction of the main axis of movement is at least consistent with the direction in which the user's arm swings forward / backward; A data processing module, used to identify the peaks and troughs on the acceleration waveform data using the peak determination condition and the trough determination condition, and to identify whether the peaks or troughs are valid using the peak validity determination condition and the trough validity determination condition; The step counting and display module is used to count the number of steps of the user plus one when a valid peak or a valid trough is identified, so as to accumulate the total number of steps taken by the user using the sports equipment.

17. A smart wearable device, characterized in that: The method comprises a processor, wherein the processor is coupled to a memory, wherein the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the method according to any one of claims 1 to 15 is implemented.

18. A readable storage medium, characterized in that: The invention comprises a program, which, when being executed on a computer, causes the computer to execute the method according to any one of claims 1 to 15.

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