Exercise evaluation method and device, equipment, storage medium and computer program product

Abstract

The invention relates to an exercise evaluation method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring motion sensing data of each motion object in a multi-object cooperative motion process; detecting a motion period included in the motion sensing data of each motion object; for each motion object, determining motion statistical characteristics corresponding to each motion period based on the motion sensing data of each motion period; and based on the motion statistical characteristics of each motion object, determining the multi-object cooperation degree of cooperation. Through the method, the performance of the moving object in the moving process can be evaluated more comprehensively and meticulously, so that the accuracy of matching degree determination can be improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of motion evaluation, and in particular, to a motion evaluation method, device, equipment, storage medium and computer program product. BACKGROUND

[0002] Sports consumption demand, including outdoor sports, is growing rapidly. The public fitness service system is constantly improving, and people's awareness and convenience of participating in fitness are constantly improving. On this basis, multi-object cooperation sports are also booming, and many emerging sports, including kayaking, paddleboarding, and rowing, are deeply loved by people.

[0003] However, for multi-object cooperation sports, the coordination between multiple objects is related to the sports performance, so it is necessary to study the coordination between objects. SUMMARY

[0004] The present disclosure provides a motion evaluation method, device, equipment, storage medium and computer program product.

[0005] According to a first aspect of an embodiment of the present disclosure, a motion evaluation method is provided, the method comprising:

[0006] Obtaining motion sensing data of each motion object in a multi-object cooperation motion process;

[0007] Detecting a motion period included in the motion sensing data of each motion object;

[0008] For each motion object, determining a motion statistical feature corresponding to each motion period based on the motion sensing data of each motion period;

[0009] Based on the motion statistical features of each motion object, determining the coordination of multi-object cooperation. In some embodiments, the method further comprises:

[0010] For each motion object, based on the motion sensing data of each motion period, determining a stability feature corresponding to all motion periods, which represents motion stability;

[0011] The determination of the coordination of multi-object cooperation based on the motion statistical features of each motion object comprises:

[0012] Based on the motion statistical features and the stability features of each motion object, determining the coordination of multi-object cooperation.

[0013] In some embodiments, the determination of the coordination of multi-object cooperation based on the motion statistical features and the stability features of each motion object comprises:

[0014] determine a target moving object, from the plurality of moving objects, based on the stability features of the moving objects, wherein the target moving object is determined to satisfy a preset stability condition based on the stability feature of the target moving object;

[0015] determine, for each moving object other than the target moving object, a first motion feature set composed of the motion statistical feature and the stability feature of the moving object, and a second motion feature set composed of the motion statistical feature and the stability feature of the target moving object, and determine a similarity between the first motion feature set and the second motion feature set;

[0016] determine, based on the similarity between the first motion feature set of each moving object other than the target moving object and the second motion feature set of the target moving object, a cooperation degree of multi-object cooperation.

[0017] In some embodiments, the motion sensing data comprises acceleration data, and the determining, based on the motion sensing data of each motion cycle, of the stability feature representing motion stability corresponding to all motion cycles comprises:

[0018] determining, based on the acceleration data of each motion cycle, a speed value corresponding to each motion cycle;

[0019] determining, based on the speed value corresponding to each motion cycle, a speed average value corresponding to all motion cycles;

[0020] determining, based on the speed value corresponding to each motion cycle and the speed average value corresponding to all motion cycles, the stability feature representing motion stability.

[0021] In some embodiments, the determining, based on the speed value corresponding to each motion cycle and the speed average value corresponding to all motion cycles, of the stability feature representing motion stability comprises:

[0022] determining a speed difference value between the speed value corresponding to each motion cycle and the speed average value;

[0023] squaring the speed difference value corresponding to each motion cycle, summing the squared values, and determining, as the stability feature representing motion stability, a ratio of the summed value to a number of all motion cycles.

[0024] In some embodiments, the motion sensing data comprises acceleration data, and the motion statistical feature corresponding to each motion cycle comprises an acceleration peak value corresponding to each motion cycle;

[0025] The determining, based on the motion sensing data of each motion cycle, of the motion statistical feature corresponding to each motion cycle comprises:

[0026] performing first-order difference on the acceleration value in each motion cycle to obtain a first-order difference result;

[0027] normalizing the first-order difference result to obtain a normalized result;

[0028] performing second-order difference based on the normalized result, and determining an acceleration peak value corresponding to a motion period based on a second-order difference result obtained.

[0029] In some embodiments, the motion sensing data comprises acceleration data, and the motion statistical feature corresponding to each motion period comprises: a time of positive-negative switching of the acceleration data corresponding to each motion period.

[0030] The determining of the motion statistical feature corresponding to each motion period based on the motion sensing data of each motion period comprises:

[0031] Based on the acceleration data in each motion period, a first time of switching of the acceleration data from a negative value to a positive value, and / or a second time of switching of the acceleration data from a positive value to a negative value is determined.

[0032] In some embodiments, the motion statistical feature corresponding to each motion period comprises: a speed value corresponding to each motion period.

[0033] The determining of the motion statistical feature corresponding to each motion period based on the motion sensing data of each motion period further comprises:

[0034] A sum value of the acceleration data between the first time and the second time in each motion period is determined as the speed value corresponding to the motion period.

[0035] According to a second aspect of the embodiments of the present disclosure, a motion evaluation device is provided, which comprises:

[0036] An acquisition module configured to acquire motion sensing data of each motion object in a multi-object cooperative motion process;

[0037] A detection module configured to detect a motion period included in the motion sensing data of each motion object;

[0038] A first determination module configured to determine, for each motion object, a motion statistical feature corresponding to each motion period based on the motion sensing data of each motion period;

[0039] A second determination module configured to determine a cooperation degree of the multi-object cooperation based on the motion statistical features of the motion objects.

[0040] In some embodiments, the device further comprises:

[0041] A third determination module configured to determine, for each motion object, a stability feature representing motion stability corresponding to all motion periods based on the motion sensing data of each motion period;

[0042] The second determining module is further configured to determine the coordination degree of the multi-object cooperation based on the motion statistical features and the stability features of the motion objects.

[0043] In some embodiments, the second determining module is further configured to determine a target motion object whose motion stability represented by the stability feature satisfies a preset stability condition based on the stability features of the motion objects; determine, for each motion object other than the target motion object, a similarity between a first motion feature set composed of the motion statistical feature and the stability feature of the motion object and a second motion feature set composed of the motion statistical feature and the stability feature of the target motion object; and determine the coordination degree of the multi-object cooperation based on the similarity between the first motion feature set of each motion object other than the target motion object and the second motion feature set of the target motion object.

[0044] In some embodiments, the motion sensing data includes acceleration data, and the third determining module is further configured to determine a speed value corresponding to each motion period based on the acceleration data of the motion period; determine a speed average value corresponding to all motion periods based on the speed value corresponding to each motion period; and determine the stability feature representing the motion stability based on the speed value corresponding to each motion period and the speed average value corresponding to all motion periods.

[0045] In some embodiments, the third determining module is further configured to determine a speed difference value between the speed value corresponding to each motion period and the speed average value; square the speed difference value corresponding to each motion period, sum the squared values, and determine a ratio of the summed value to the number of motion periods as the stability feature representing the motion stability.

[0046] In some embodiments, the motion sensing data includes acceleration data, and the motion statistical feature corresponding to each motion period includes an acceleration peak value corresponding to each motion period.

[0047] The first determining module is further configured to perform first-order differentiation on the acceleration values in each motion period to obtain a first-order differentiation result; perform normalization processing on the first-order differentiation result to obtain a normalized result; perform second-order differentiation based on the normalized result, and determine the acceleration peak value corresponding to the motion period based on the obtained second-order differentiation result.

[0048] In some embodiments, the motion sensing data includes acceleration data, and the motion statistical feature corresponding to each motion period includes a time of positive-negative switching of the acceleration data corresponding to each motion period.

[0049] The first determining module is further configured to determine, based on the acceleration data in each movement cycle, a first time at which the acceleration data switches from a negative value to a positive value, and / or a second time at which the acceleration data switches from a positive value to a negative value.

[0050] In some embodiments, the movement statistical feature corresponding to each movement cycle includes a speed value corresponding to each movement cycle.

[0051] The first determining module is further configured to determine, as the speed value corresponding to each movement cycle, a sum value of the acceleration data between the first time and the second time in each movement cycle.

[0052] According to a third aspect of embodiments of the present disclosure, an electronic device is provided, including:

[0053] a processor;

[0054] a memory for storing computer programs or instructions;

[0055] The processor executes the computer programs or instructions to implement the steps of the method in the first aspect.

[0056] According to a fourth aspect of embodiments of the present disclosure, a non-transitory computer readable storage medium is provided, which stores computer programs or instructions, and when the computer programs or instructions in the storage medium are executed by a processor, the steps of the method in the first aspect are implemented.

[0057] According to a fifth aspect of embodiments of the present disclosure, a computer program product is provided, which includes computer programs or instructions, and when the computer programs or instructions are executed by a processor, the steps of the method in the first aspect are implemented.

[0058] The technical solutions provided by the embodiments of the present disclosure can include the following beneficial effects:

[0059] In the embodiments of the present disclosure, by statistically determining the movement statistical features of each movement object in each movement cycle, the coordination degree of multi-object cooperation is determined, which can more comprehensively and meticulously evaluate the performance of the movement object in the movement process, thereby improving the accuracy of the coordination degree determination.

[0060] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0061] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0062] Figure 1 is a flowchart of a motion evaluation method according to an example embodiment.

[0063] Figure 2 is a schematic diagram of a double-canoe motion according to an example embodiment.

[0064] Figure 3 is a block diagram of a motion evaluation device according to an example embodiment.

[0065] Figure 4 is a structural block diagram of an electronic device according to an example embodiment. DETAILED DESCRIPTION

[0066] The example embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same or similar elements unless otherwise represented. The implementations described in the following example embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0067] The embodiments of the present disclosure provide a motion evaluation method, Figure 1 is a flowchart of a motion evaluation method according to an example embodiment. As Figure 1 shown, the method mainly includes the following steps:

[0068] S11, acquiring motion sensing data of each motion object in a multi-object cooperative motion process;

[0069] S12, detecting a motion period included in the motion sensing data of each motion object;

[0070] S13, for each motion object, determining a motion statistical feature corresponding to each motion period based on the motion sensing data of each motion period;

[0071] S14, determining a coordination degree of multi-object cooperation based on the motion statistical features of each motion object.

[0072] The motion evaluation method in the embodiments of the present disclosure can be applied to electronic devices with built-in motion sensors, such as smart watches, motion bands, and wearable devices such as smart ropes, etc., and can also be applied to other devices connected to the foregoing electronic devices, such as smart phones, notebooks, computing devices, or servers, etc. Among them, the wearable devices can be different according to different motions, such as when the motion is a canoe motion, the wearable device can be a motion band, a smart watch, etc.; when the motion is a multi-person bicycle motion, the wearable device can be a motion ring, etc.

[0073] In step S11, the electronic device acquires motion sensing data of each motion object in a multi-object cooperative motion process, wherein the multi-object cooperative motion represents a motion that requires multiple people to cooperate, such as a double kayak motion, a double bicycle motion, a four-person kayak motion, and the like. Embodiments of the present disclosure do not limit the motion form and the number of objects in the multi-person cooperative motion.

[0074] For example, a kayak is a water sport that can be performed by multiple people. The coordination between different athletes on the boat affects the speed and efficiency of the kayak, especially for a competition. The coordination of different athletes directly affects the final competition result. Figure 2 FIG. 1 is a schematic diagram of a double kayak motion according to an example embodiment, wherein L21 is a motion object in the double kayak motion, L22 is an oar of the motion object L21, L23 is another motion object in the double kayak motion, L24 is an oar of the motion object L23, and L25 is a kayak.

[0075] In embodiments of the present disclosure, the motion sensing data includes acceleration data, angular velocity data, or speed data, and the like, which can be collected by a motion sensor built in the electronic device. The motion sensor can be a single-axis accelerometer, a multi-axis accelerometer, a single-axis gyroscope, a multi-axis gyroscope, or an inertial measurement unit (IMU) integrating an accelerometer and a gyroscope. Embodiments of the present disclosure do not limit the motion sensing data, which can reflect the motion state of the motion object in the multi-object cooperative motion process.

[0076] In step S12, the electronic device detects a motion period included in the motion sensing data of each motion object, wherein the motion period refers to the time required to complete a complete motion action. For example, the motion period can be the time required to complete a complete rowing action in the rowing process or the time required to complete a complete pedaling action in the cycling process.

[0077] It should be noted that the motion state of the motion object can be different in different motion periods. For example, in the rowing motion, the motion object can exhibit different motion performances in each rowing action due to different force distribution, technical adjustment, or physical condition. The difference is not only reflected in the subtle difference in the rowing distance, but also directly reflected in the fluctuation of the rowing speed. Therefore, in order to analyze the motion process of the motion object more carefully, the entire motion process of the motion object can be divided into different motion periods, and the complex motion behavior can be divided into small units that are easier to understand and analyze. By analyzing each motion period, subtle changes in the motion process can be captured more sensitively. For example, in the rowing motion, the rowing distance and the rowing speed corresponding to different motion periods can be different.

[0078] In some embodiments, the motion sensing data of the moving object includes single-axis sensing data, such as single-axis acceleration data. The electronic device can directly determine the motion period based on the single-axis acceleration data, or it can filter the single-axis acceleration data before determining the motion period.

[0079] In other embodiments, the motion sensing data of the moving object includes multi-axis sensing data, such as multi-axis acceleration data. The electronic device can independently analyze the acceleration data on the X, Y, and Z axes in the multi-axis acceleration data, identify the motion cycle corresponding to each axis, and perform fusion processing based on the motion cycles corresponding to each axis to determine the motion cycle during the motion process. For example, repeating motion cycles can be filtered out from the motion cycles included in each axis, and the motion cycles of other time periods can be filled in based on the filtered repeating motion cycles. Alternatively, the target axis can be determined based on the multi-axis acceleration data, and then the motion cycle during the motion process can be determined based on the acceleration data of the target axis.

[0080] In this embodiment of the disclosure, when determining the motion period, for example, it can be based on the motion sensing data during the motion process to perform first-order difference and then second-order difference, and the motion period can be determined based on the second-order difference result.

[0081] In this embodiment of the present disclosure, after the electronic device determines the motion cycle of each moving object based on the motion sensing data of each moving object, in step S13, it further determines the motion statistical characteristics of each moving object based on the motion sensing data within each motion cycle, wherein the motion statistical characteristics represent the characteristics within each motion cycle.

[0082] In this embodiment of the disclosure, motion statistical features may include the peak or mean value of motion sensing data corresponding to each motion cycle, and may also include time information with specific characteristics based on motion sensing data statistics, such as the time corresponding to the peak value, the time when acceleration data switches from a positive value to a negative value, the time when it switches from a negative value to a positive value, etc.

[0083] In step S14, the electronic device determines the coordination degree of the multi-object cooperation based on the motion statistical features of each motion object. In some embodiments, the electronic device can determine an average motion statistical feature of the motion objects based on the motion statistical features of each motion object, compare the motion statistical features of each motion object with the average motion statistical feature in terms of similarity, and determine the coordination degree of the multi-object cooperation based on each similarity value. For example, the similarity is a distance-based measure, and the smaller the average of each similarity value, the higher the coordination degree. In other embodiments, the electronic device can also select a target motion object based on the motion statistical features of each motion object, and compare the motion statistical features of each motion object with the motion statistical features of the target motion object in terms of similarity, thereby determining the coordination degree of the multi-object cooperation.

[0084] It should be noted that the electronic device can form a feature vector set of the motion statistical features of each motion object in each motion period, and determine the coordination degree of the multi-object cooperation based on the feature vector set of each motion object. In related technologies, only single-person motion is generally focused on, and no multi-person cooperation analysis is involved. In addition, in the process of analyzing single-person motion, only general analysis of the entire motion process is involved, and no detailed analysis of each motion period is involved. Moreover, general analysis of the entire motion process cannot be applied to determine the coordination degree of multi-object cooperation.

[0085] To this end, the embodiments of the present disclosure provide a coordination degree analysis method in multi-object cooperation motion, which determines the coordination degree of multi-object cooperation by statistically analyzing the motion statistical features of each motion object in each motion period, can more comprehensively and meticulously evaluate the performance of the motion object in the motion process, and thus can improve the accuracy of coordination degree determination.

[0086] In some embodiments, the method further includes:

[0087] For each motion object, determining, based on the motion sensing data of each motion period, stability features of all motion periods representing motion stability;

[0088] The determining, based on the motion statistical features of each motion object, the coordination degree of the multi-object cooperation includes:

[0089] The determining, based on the motion statistical features of each motion object, the coordination degree of the multi-object cooperation includes:

[0090] In the embodiments of the present disclosure, the stability features represent the statistical features of all motion periods. The stability features can be variance or standard deviation that can reflect the stability of the entire motion process determined based on the mean or peak value of each motion period, or can be the average of the fluctuation values of the motion sensing data in each motion period, and the present disclosure does not limit this.

[0091] In the embodiments of the present disclosure, the electronic device can also select the target motion object based on the stability features of each motion object, or based on the combination of the motion statistical features and the stability features, and compare the motion statistical features and the stability features of each motion object with the motion statistical features and the stability features of the target motion object to determine the coordination degree of the multi-object cooperation.

[0092] In some embodiments, the electronic device can form a feature vector set by using the motion statistical features of each motion object in each motion cycle and the stability features corresponding to all motion cycles, and determine the coordination degree of the multi-object cooperation based on the feature vector set of each motion object.

[0093] In some embodiments, the electronic device can process the motion statistical features and the stability features respectively, and determine the coordination degree of the multi-object cooperation by weighting the final statistical value. For example, the electronic device compares the motion statistical features of each motion object with the motion statistical features of the target motion object to obtain a first similarity value of each motion object, then compares the stability features of each motion object with the stability features of the target object to obtain a second similarity value of each motion object, then fuses the first similarity value and the second similarity value of each motion object by weighting to obtain a similarity fusion value of each motion object, and determines the matching degree based on the similarity fusion value of each motion object. For example, the electronic device determines the coordination degree of the multi-object cooperation by averaging the similarity fusion values of each motion object.

[0094] It can be understood that in the embodiments of the present disclosure, the motion stability features corresponding to all motion cycles are added to the motion statistical features to determine the coordination degree of the multi-object cooperation, which can improve the accuracy of determining the coordination degree of the object cooperation.

[0095] In some embodiments, the determination of the coordination degree of the multi-object cooperation based on the motion statistical features and the stability features of each motion object comprises:

[0096] determining a target motion object whose motion stability represented by the stability features meets a preset stability condition based on the stability features of each motion object;

[0097] determining the similarity between a first motion feature set composed of the motion statistical features and the stability features of each motion object and a second motion feature set composed of the motion statistical features and the stability features of the target motion object for each motion object other than the target motion object;

[0098] determining the coordination degree of the multi-object cooperation based on the similarity between the first motion feature set of each motion object other than the target motion object and the second motion feature set of the target motion object.

[0099] In this embodiment of the disclosure, the electronic device determines the target moving object from multiple moving objects, and compares the features of each moving object other than the target moving object (including the motion statistics features of each motion cycle and the stability features of all motion cycles) with the features of the target moving object to determine the degree of cooperation of the multi-object collaboration.

[0100] In determining the target motion object, considering that the stability characteristics represent the individual athlete's performance, the smaller the stability characteristics represent the person's motion fluctuation, the more professional the person's movements are. Therefore, in this embodiment of the disclosure, the electronic device determines the target motion object whose motion stability represented by the stability characteristics meets the preset stability conditions based on the stability characteristics of each motion object. For example, the motion stability represented by the stability characteristics of the target motion object is the most stable.

[0101] In this embodiment of the disclosure, after determining the target moving object, the electronic device assembles the motion statistical characteristics of each moving object in each motion cycle and the stability characteristics of all motion cycles into a motion feature set. For example, if each moving object includes n F1, F2, F3, and F4 features, and one F5 feature, then the motion feature set of each moving object can be represented as:

[0102]

[0103] in, These represent the statistical characteristics of each type of motion, with F5 representing the stability characteristics. Each includes motion statistical features over multiple periods, as shown below:

[0104]

[0105] In this embodiment of the disclosure, for example, P0 is represented as the second motion feature set of the target moving object, P i Let P be the first set of motion features for all other moving objects. Then the electronic device can compare each P. i The similarity between the moving object and the target moving object is calculated using methods such as Euclidean distance, Manhattan distance, or Dynamic Time Warping (DTW). Then, the overall similarity is determined based on the similarity between each moving object and the target moving object to assess the compatibility. For example, the overall similarity can be calculated using the following method:

[0106]

[0107] Among them, R i0 R represents the similarity between a moving object and a target moving object. R is the mean of the similarity. The smaller the R, the higher the degree of cooperation.

[0108] It can be understood that, in the embodiments of the present disclosure, the cooperation degree of multi-object cooperation is determined based on the similarity between the first motion feature set of each motion object other than the target motion object and the second motion feature set of the target motion object, which is simple and effective, and can improve the efficiency of determining the cooperation degree.

[0109] In some embodiments, the motion sensing data includes acceleration data, and the motion statistical feature corresponding to each motion period includes an acceleration peak value corresponding to each motion period.

[0110] The motion sensing data of each motion period is used to determine the motion statistical feature corresponding to each motion period, including:

[0111] First-order difference is performed on the acceleration value in each motion period to obtain a first-order difference result.

[0112] The first-order difference result is normalized to obtain a normalized result.

[0113] Second-order difference is performed based on the normalized result, and the acceleration peak value corresponding to the motion period is determined based on the obtained second-order difference result.

[0114] In the embodiments of the present disclosure, the motion sensing data includes acceleration data, and the electronic device performs first-order difference processing on the acceleration data in each motion period to obtain a first-order difference result. The first-order difference result refers to the difference between two adjacent data points, which reflects the change rate of the acceleration data. Through first-order difference, the change trend of the acceleration data in the motion process can be preliminarily extracted. In the embodiments of the present disclosure, assuming that the acceleration data list is s_i-1, s_i, s_i+1, the calculated first-order difference result is delta_s_i=(s_i)-(s_i-1), delta_s_i+1=(s_i+1)-(s_i).

[0115] In the embodiments of the present disclosure, the electronic device performs normalization processing on the first-order difference result, that is, all first-order difference results are scaled to the same order of magnitude, for example, in the interval [-1, 1]. Normalization can eliminate the influence of acceleration data differences under different motion intensities, and improve the universality and accuracy of the algorithm. In the embodiments of the present disclosure, the normalized result of delta_s_i is denoted as norm_delta_s_i, and the normalized result of delta_s_i+1 is denoted as norm_delta_s_i+1.

[0116] In the embodiments of the present disclosure, the electronic device further performs second-order difference processing on the normalized result, and determines the peak value of the acceleration data based on the second-order difference result. The second-order difference result refers to the difference between two adjacent first-order difference results, which further reveals the subtle changes in the acceleration data in the movement process. In the embodiments of the present disclosure, the second-order difference result is norm_delta_2_s_i+1=(norm_delta_i+1)-(norm_delta_s_i).

[0117] In the embodiments of the present disclosure, the peak value of the acceleration data in the movement cycle is identified through the second-order difference result, and the second-order difference result is the change rate of the change rate of the acceleration data. The second-order difference result can sensitively capture the bending points of the acceleration curve, which are often closely related to the key nodes (such as the peak value) in the movement process. In order to more accurately identify the peak point, a suitable determination condition can be set to identify the peak point. For example, when the second-order difference result reaches a certain threshold value, such as -2, it indicates that the acceleration data is experiencing a significant process from increasing to decreasing, that is, the acceleration reaches the peak vertex, and then it can be determined that the current acceleration data is at the peak position. In the embodiments of the present disclosure, the acceleration peak value corresponding to the movement cycle can be the aforementioned feature F4.

[0118] In the embodiments of the present disclosure, by performing first-order difference, normalization, second-order difference and other processing on the acceleration data of each movement cycle, the acceleration peak value in the acceleration data can be efficiently extracted, which can effectively reflect the intensity and efficiency of the movement object in the movement cycle.

[0119] It should be noted that, as described above, the electronic device can also determine the movement cycle included in the movement sensing data of the movement object based on the first-order difference and the second-order difference. For example, the electronic device can detect the acceleration peak value in the movement cycle, perform first-order difference, normalization, second-order difference processing on the movement sensing data of the movement object in the movement process to obtain a plurality of peak values, and then determine the time length between adjacent peak values as a movement cycle. Based on this, in the embodiments of the present disclosure, in addition to determining the acceleration peak value based on the acceleration data of the movement cycle as described above, the electronic device can also divide the movement cycle, take the acceleration data at the head or tail of the movement cycle as the acceleration peak value corresponding to the movement cycle, or take the average value of the acceleration data at the head or tail as the acceleration peak value corresponding to the movement cycle. The embodiments of the present disclosure do not limit this.

[0120] In some embodiments, the movement sensing data includes acceleration data, and the movement statistical feature corresponding to each movement cycle includes: the time of positive-negative switching of the acceleration data corresponding to each movement cycle;

[0121] The motion sensing data of each motion cycle is used to determine a motion statistical feature corresponding to each motion cycle, including:

[0122] Based on the acceleration data in each motion cycle, a first time when the acceleration data switches from a negative value to a positive value, and / or a second time when the acceleration data switches from a positive value to a negative value, is determined.

[0123] In the embodiments of the present disclosure, the electronic device can determine, based on the acceleration data in each motion cycle, the first time when the acceleration data switches from a negative value to a positive value, and the second time when the acceleration data switches from a positive value to a negative value. Since acceleration is a physical quantity describing the speed change of an object, the change in direction reflects the change in the force direction of the object, and the change in acceleration is often accompanied by energy conversion. For example, in the process of rowing, the first time when the acceleration data of the moving object switches from a negative value to a positive value marks that the moving object starts to overcome the resistance and exerts a forward propulsion force on the boat, which is a clear sign that the energy is transferred from the athlete's body to the boat body, i.e., the moving object starts to do work effectively; and the second time when the acceleration data of the moving object switches from a positive value to a negative value marks the end of this effective work phase, although the boat may continue to move forward under the action of inertia. In the embodiments of the present disclosure, the first time can be the aforementioned feature F2, and the second time can be the aforementioned feature F3.

[0124] In the embodiments of the present disclosure, the statistics of the first time and / or the second time can effectively reflect the timing of the moving object's effort in each motion cycle during the movement process, and thus the coordination degree in the multi-object cooperative movement can be improved by statistically analyzing the first time and / or the second time of each moving object in each motion cycle.

[0125] In some embodiments, the motion statistical feature corresponding to each motion cycle includes a speed value corresponding to each motion cycle.

[0126] The motion sensing data of each motion cycle is used to determine a motion statistical feature corresponding to each motion cycle, including:

[0127] The sum of the acceleration data between the first time and the second time in each motion cycle is determined as a speed value corresponding to the motion cycle.

[0128] In the embodiments of the present disclosure, after the electronic device determines the first time and the second time in the motion cycle, the sum of the acceleration data between the first time and the second time is determined as a speed value corresponding to the motion cycle. As described above, the first time marks the start of the effective work of the moving object, and the second time marks the end of the effective work of the moving object, and thus the sum of the acceleration data between the first time and the second time also represents the effective work amount of the moving object in the motion cycle.

[0129] In the embodiments of the present disclosure, the manner in which the electronic device determines the speed value corresponding to each movement period can be shown in the following formula (4):

[0130]

[0131] wherein A i is the acceleration data at the i th moment, t1 is the first time, t2 is the second time, and F1 is the speed value corresponding to the movement period.

[0132] In the embodiments of the present disclosure, the sum of the acceleration data between the first time and the second time in each movement period is determined as the speed value, which can accurately reflect the effective work done by the moving object in each movement period, thereby more accurately determining the movement efficiency of the moving object in each movement period.

[0133] In some embodiments, the movement sensing data includes acceleration data, and the determination of the stability feature representing the movement stability corresponding to all movement periods based on the movement sensing data of each movement period includes:

[0134] determining the speed value corresponding to each movement period based on the acceleration data of each movement period;

[0135] determining the average speed value corresponding to all movement periods based on the speed value corresponding to each movement period;

[0136] determining the stability feature representing the movement stability based on the speed value corresponding to each movement period and the average speed value corresponding to all movement periods.

[0137] In the embodiments of the present disclosure, the electronic device can determine the speed value corresponding to each movement period based on the acceleration data of each movement period, which can be the method of the aforementioned formula (4), or can be based on the average acceleration between two adjacent points in the movement period to determine the speed value, which is not limited in the embodiments of the present disclosure.

[0138] In the embodiments of the present disclosure, the electronic device determines the average speed value corresponding to all movement periods based on the speed value corresponding to each movement period, and determines the stability feature based on the speed value corresponding to each movement period and the average speed value.

[0139] In some embodiments, the electronic device can determine the difference between the speed value corresponding to each movement period and the average speed value, and determine the stability feature based on the average value of the difference between the speed value corresponding to each movement period and the average speed value, for example, the greater the average value of the difference, the worse the stability.

[0140] In some embodiments, the stability feature representing the stability of the motion is determined based on the speed value corresponding to each motion cycle and the average speed value corresponding to all motion cycles, including:

[0141] determining a speed difference value between the speed value corresponding to each motion cycle and the average speed value;

[0142] squaring and summing the speed difference value corresponding to each motion cycle, and determining a ratio of the summed value to the number of all motion cycles as the stability feature representing the stability of the motion.

[0143] In the embodiments of the present disclosure, the electronic device determines the speed difference value between the speed value corresponding to each motion cycle and the average speed value, and then squares and sums the speed difference value corresponding to each motion cycle, which aims to quantify the degree of deviation of the acceleration data from the average value, i.e., the fluctuation size.

[0144] In the embodiments of the present disclosure, the electronic device determines the ratio of the summed value to the number of all motion cycles as the stability feature corresponding to all motion cycles. In the embodiments of the present disclosure, the calculation method of the stability feature is shown in the following formula (5):

[0145]

[0146] wherein n is the number of motion cycles, F1 i is the speed value corresponding to the i th motion cycle, is the average speed value, and F5 is the stability feature.

[0147] In the embodiments of the present disclosure, by calculating the difference value between the speed value corresponding to each motion cycle and the average speed value, squaring and summing, and further calculating the stability feature, the fluctuation degree of the speed value in the motion process of the motion object can be accurately quantified, and the accuracy and reliability of the stability feature can be effectively improved.

[0148] Figure 3 is a block diagram of a motion evaluation device according to an exemplary embodiment. As Figure 3 shown, the motion evaluation device mainly includes:

[0149] The acquisition module 301 is configured to acquire motion sensing data of each motion object in a multi-object cooperative motion process.

[0150] The detection module 302 is configured to detect a motion cycle included in the motion sensing data of each motion object.

[0151] The first determination module 303 is configured to determine, for each motion object, a motion statistical feature corresponding to each motion cycle based on the motion sensing data of each motion cycle.

[0152] The second determining module 304 is configured to determine the coordination degree of the multi-object cooperation based on the motion statistical features of the motion objects.

[0153] In some embodiments, the apparatus further comprises:

[0154] The third determining module is configured to determine, for each motion object, a stability feature representing motion stability based on the motion sensing data of each motion period.

[0155] The second determining module 304 is further configured to determine the coordination degree of the multi-object cooperation based on the motion statistical features and the stability features of the motion objects.

[0156] In some embodiments, the second determining module 304 is further configured to determine, based on the stability features of the motion objects, a target motion object whose motion stability represented by the stability feature meets a preset stability condition; determine, for each motion object other than the target motion object, a similarity between a first motion feature set composed of the motion statistical feature and the stability feature of the motion object and a second motion feature set composed of the motion statistical feature and the stability feature of the target motion object; and determine the coordination degree of the multi-object cooperation based on the similarity between the first motion feature set of each motion object other than the target motion object and the second motion feature set of the target motion object.

[0157] In some embodiments, the motion sensing data comprises acceleration data, and the third determining module is further configured to determine a speed value corresponding to each motion period based on the acceleration data of each motion period; determine a speed average value corresponding to all motion periods based on the speed value corresponding to each motion period; and determine the stability feature representing motion stability based on the speed value corresponding to each motion period and the speed average value corresponding to all motion periods.

[0158] In some embodiments, the third determining module is further configured to determine a speed difference value between the speed value corresponding to each motion period and the speed average value; sum the squares of the speed difference values corresponding to each motion period, and determine, as the stability feature representing motion stability, a ratio of the summed value to the number of all motion periods.

[0159] In some embodiments, the motion sensing data comprises acceleration data, and the motion statistical feature corresponding to each motion period comprises an acceleration peak value corresponding to each motion period.

[0160] The first determination module 303 is further configured to perform first-order difference on the acceleration values in each motion period to obtain a first-order difference result, perform normalization processing on the first-order difference result to obtain a normalized result, perform second-order difference based on the normalized result, and determine the acceleration peak value corresponding to the motion period based on the obtained second-order difference result.

[0161] In some embodiments, the motion sensing data comprises acceleration data, and the motion statistical feature corresponding to each motion period comprises: a time of positive-negative switching of the acceleration data corresponding to each motion period.

[0162] The first determination module 303 is further configured to determine, based on the acceleration data in each motion period, a first time at which the acceleration data switches from a negative value to a positive value, and / or a second time at which the acceleration data switches from a positive value to a negative value.

[0163] In some embodiments, the motion statistical feature corresponding to each motion period comprises: a speed value corresponding to each motion period.

[0164] The first determination module 303 is further configured to determine, as the speed value corresponding to the motion period, a sum value of the acceleration data between the first time and the second time in each motion period.

[0165] As to the apparatus in the above embodiments, specific manners in which various modules perform operations have been described in details in the embodiments of the method, and thus will not be described in details here.

[0166] Figure 4 is a structural block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device can be a wearable device, a computer device, etc.

[0167] Referring to Figure 4 , the electronic device 400 can include one or more of the following components: a processing component 402, a memory 404, a power supply component 406, a multimedia component 408, an audio component 410, an input / output (I / O) interface 412, a sensor component 414, and a communication component 416.

[0168] The processing component 402 usually controls overall operations of the electronic device 400, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 402 can include one or more processors 420 to execute instructions to complete all or part of steps of the methods described above. In addition, the processing component 402 can include one or more modules to facilitate the interaction between the processing component 402 and other components. For example, the processing component 402 can include a multimedia module to facilitate the interaction between the multimedia component 408 and the processing component 402.

[0169] The memory 404 is configured to store various types of data to support operations on the electronic device 400. Examples of such data include at least one of instructions, contact data, phonebook data, messages, pictures, and videos for any application or method operating on the electronic device 400. The memory 404 can be implemented by any type of volatile or nonvolatile memory, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disc, or an optical disc.

[0170] The power component 406 supplies power to various components of the electronic device 400. The power component 406 can include at least one of a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 400.

[0171] The multimedia component 408 includes a screen providing an output interface between the electronic device 400 and a user. In some embodiments, the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes the touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect duration and pressure related to the touching or sliding action. In some embodiments, the multimedia component 408 includes a front camera and / or a back camera. When the electronic device 400 is in an operating mode, such as a photographing mode or a video mode, the front camera and / or the back camera can receive external multimedia data. Each of the front camera and the back camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0172] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC) that is configured to receive an external audio signal when the electronic device 400 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

[0173] The input / output (I / O) interface 412 provides an interface between the processing component 402 and peripheral interface modules, which can include a keypad, click wheel, button, and so on. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0174] The sensor component 414 includes one or more sensors for providing status assessments of various aspects of the electronic device 400. For example, the sensor component 414 can detect an open / closed position of the electronic device 400, relative positioning of components, such as a display and a keypad of the electronic device 400, a change in position of the electronic device 400 or a component of the electronic device 400, the presence or absence of user contact with the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and a temperature change of the electronic device 400. The sensor component 414 can include a proximity sensor configured to detect the presence of a nearby object without any physical touch. The sensor component 414 can also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, utilized in an imaging application. In some embodiments, the sensor component 414 can further include at least one of an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, and a temperature sensor, among others.

[0175] The communication component 416 is configured to facilitate wired or wireless communication between the electronic device 400 and other devices. The electronic device 400 can access a wireless network based on a communication standard, such as Wi-Fi, 4G, 5G, or a combination thereof. In an example embodiment, the communication component 416 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an example embodiment, the communication component 416 also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0176] In an example embodiment, the electronic device 400 can be implemented with one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements.

[0177] In an example embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 404 including executable instructions or a computer program, is also provided, which can be executed by the processor 420 of the electronic device 400 to complete the above-described method. For example, the non-transitory computer-readable storage medium can be a ROM, a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0178] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform any one of the motion evaluation methods according to the embodiments of the present disclosure.

[0179] The embodiment of the disclosure provides a computer program product, which comprises a computer program or executable instruction stored in a computer readable storage medium. The processor of the computer device reads the computer program or executable instruction from the computer readable storage medium, and the processor executes the computer program or executable instruction, so that the computer device executes any one of the motion evaluation methods provided in the embodiment of the disclosure.

[0180] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the concepts disclosed herein. The disclosure is intended to cover any variations, uses or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. The specification and examples are to be regarded as illustrative only, and the true scope and spirit of the disclosure are indicated by the claims.

[0181] It should be understood that the present disclosure is not limited to the precise structures as herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from its scope. The scope of the present disclosure is limited only by the claims that follow.

Claims

1. A method for assessing motion, characterized in that, The method includes: Acquire motion sensing data of each moving object during multi-object collaborative motion; The motion cycle included in the motion sensing data of each moving object is detected; For each moving object, the motion statistical characteristics corresponding to each motion cycle are determined based on the motion sensing data of each motion cycle. Based on the motion statistics of each moving object, the degree of cooperation of multi-object collaboration is determined.

2. The method according to claim 1, characterized in that, The method further includes: For each moving object, based on the motion sensing data of each motion cycle, determine the stability characteristics that characterize motion stability for all motion cycles. The determination of the degree of cooperation among multiple objects based on the motion statistical characteristics of each moving object includes: Based on the motion statistics and stability characteristics of each moving object, the degree of cooperation of multi-object collaboration is determined.

3. The method according to claim 2, characterized in that, The above is based on the motion statistical characteristics and stability characteristics of each moving object. Determine the degree of cooperation in multi-object collaboration, including: Based on the stability characteristics of each moving object, the target moving object whose motion stability, as represented by the stability characteristics, meets the preset stability conditions is determined. For each moving object other than the target moving object, determine the similarity between a first set of motion features composed of the motion statistical features and stability features of the moving object and a second set of motion features composed of the motion statistical features and stability features of the target moving object; The degree of cooperation among multiple objects is determined based on the similarity between the first motion feature set of each moving object other than the target moving object and the second motion feature set of the target moving object.

4. The method according to claim 2, characterized in that, The motion sensing data includes acceleration data, and the stability features characterizing motion stability corresponding to all motion cycles are determined based on the motion sensing data for each motion cycle, including: Based on the acceleration data of each motion cycle, determine the velocity value corresponding to each motion cycle; Based on the speed value corresponding to each motion cycle, determine the average speed value corresponding to all motion cycles; Based on the velocity value corresponding to each motion cycle and the average velocity value corresponding to all motion cycles, the stability feature characterizing motion stability is determined.

5. The method according to claim 4, characterized in that, The determination of the stability characteristics characterizing motion stability based on the velocity value corresponding to each motion cycle and the average velocity value corresponding to all motion cycles includes: Determine the speed difference between the speed value corresponding to each motion cycle and the average speed; The summation of the squared velocity differences corresponding to each motion cycle, and the ratio of the summation to the number of all motion cycles, is determined as the stability characteristic characterizing motion stability.

6. The method according to claim 1, characterized in that, The motion sensing data includes acceleration data, and the motion statistical features corresponding to each motion cycle include: the peak acceleration corresponding to each motion cycle; The determination of motion statistical features corresponding to each motion cycle based on motion sensing data of each motion cycle includes: The acceleration values ​​within each motion cycle are subjected to first-order difference to obtain the first-order difference result; The first-order difference result is normalized to obtain the normalized result; Based on the normalization result, a second-order difference is performed, and the peak acceleration corresponding to the motion cycle is determined based on the obtained second-order difference result.

7. The method according to claim 1 or 4, characterized in that, The motion sensing data includes acceleration data, and the motion statistical features corresponding to each motion cycle include: the time of positive and negative switching of acceleration data corresponding to each motion cycle; The determination of motion statistical features corresponding to each motion cycle based on motion sensing data of each motion cycle includes: Based on the acceleration data within each motion cycle, determine the first time when the acceleration data switches from negative to positive, and / or the second time when the acceleration data switches from positive to negative.

8. The method according to claim 7, characterized in that, The motion statistics characteristics corresponding to each motion cycle include: the velocity value corresponding to each motion cycle; The method of determining the motion statistical features corresponding to each motion cycle based on motion sensing data of each motion cycle also includes: The sum of the acceleration data between the first time and the second time within each motion cycle is determined as the velocity value corresponding to the motion cycle.

9. A motion assessment device, characterized in that, The device includes: The acquisition module is configured to acquire motion sensing data of each moving object during multi-object collaborative motion. The detection module is configured to detect the motion cycle included in the motion sensing data of each moving object; The first determining module is configured to determine the motion statistical features corresponding to each motion cycle based on the motion sensing data of each motion cycle for each moving object. The second determination module is configured to determine the degree of cooperation among multiple objects based on the motion statistical characteristics of each moving object.

10. An electronic device, characterized in that, include: processor; Memory used to store computer programs or instructions; The processor executes the computer program or instructions to implement the steps of the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing a computer program or instructions, characterized in that, When the computer program or instructions in the storage medium are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

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