Motion effect detection method and device, electronic device, and storage medium

Abstract

This disclosure relates to a method, apparatus, electronic device, and storage medium for detecting motion effects. The method for detecting motion effects in an electronic device includes: detecting physiological data of a target object during movement; determining the motion load and motion capacity information of the target object based on the physiological data; and determining the motion effect of the target object based on the motion load and motion capacity information.

Description

Technical Field

[0001] This disclosure relates to the field of information processing technology, and in particular to a method and apparatus for detecting motion effects, an electronic device, and a storage medium. Background Technology

[0002] With the development of electronic information and smart technology, users often wear electronic devices to monitor their exercise. These wearable devices include wristbands, watches, ankle bracelets, and arm supports. Good exercise can significantly improve a user's physical fitness; however, while excessive exercise can improve fitness, it can also place a greater burden on the body. Therefore, only reasonable exercise can achieve the desired results.

[0003] However, the different parameters used in motion effect detection in related technologies will lead to lower accuracy of motion effect detection, higher computational load, poor intelligence of electronic devices, and negative impact on user experience. Summary of the Invention

[0004] This disclosure provides a method and apparatus for detecting motion effects, an electronic device, and a storage medium.

[0005] The first aspect of this disclosure provides a motion effect detection method applied to an electronic device, the method comprising:

[0006] Detect physiological data of the target object during movement;

[0007] Based on physiological data, determine the exercise load and exercise capacity information of the target object during movement;

[0008] Based on the exercise load and exercise capacity information, the exercise effect of the target object is determined.

[0009] Based on the above scheme, determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes:

[0010] Based on physiological data, determine the current motion intensity of the target object;

[0011] The exercise load is determined based on the current exercise intensity.

[0012] Based on the above scheme, determining the exercise load according to the current exercise intensity includes:

[0013] The exercise load is determined based on the current exercise intensity and the maximum exercise intensity.

[0014] Based on the above scheme, determining the current motion intensity of the target object according to physiological data includes:

[0015] The current exercise intensity of the target object is determined based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

[0016] Based on the above scheme, determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes:

[0017] Based on the physiological data, determine the current movement type of the target object;

[0018] Based on the physiological data associated with the type of exercise, determine the target object's motor ability information.

[0019] Based on the above scheme, determining the target object's motor ability information according to the physiological data associated with the type of movement includes:

[0020] When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise.

[0021] Based on the above scheme, when the exercise type is aerobic exercise, determining the aerobic exercise capacity information of the target object based on the oxygen uptake of the target object during the aerobic exercise includes:

[0022] When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise and the heart rate change information of the target object during the exercise.

[0023] Based on the above scheme, determining the target object's motor ability information according to the physiological data associated with the type of movement includes:

[0024] When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the amount of lactic acid accumulation in the target object.

[0025] Based on the above scheme, when the exercise type is anaerobic exercise, determining the anaerobic exercise capacity information of the target object based on the lactate accumulation of the target object includes:

[0026] When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the amount of lactic acid accumulation in the target object and the number of times the target object enters the anaerobic exercise during exercise.

[0027] Based on the above scheme, the method further includes:

[0028] Detect the motion patterns of the target object;

[0029] The calculation parameters for the oxygen uptake are determined based on the sport.

[0030] The oxygen uptake is determined based on the calculated parameters.

[0031] Based on the above scheme, the method further includes:

[0032] Filter outliers from the physiological data;

[0033] The step of determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes:

[0034] Based on the physiological data from which outliers have been filtered out, the exercise load and exercise capacity information of the target object during movement are determined.

[0035] Based on the above scheme, filtering outliers in the physiological data includes:

[0036] Values ​​outside the first preset range in the physiological data are filtered out to obtain the filtered physiological data.

[0037] And / or,

[0038] The physiological data is filtered out when the rate of change of the physiological data at two adjacent moments during the exercise is outside the preset rate of change, thus obtaining the filtered physiological data.

[0039] Based on the above scheme, the physiological data includes heart rate;

[0040] Determining the current movement type of the target object based on the physiological data includes:

[0041] Based on the range of the heart rate, the exercise type of the target object is determined to be either aerobic or anaerobic exercise.

[0042] Based on the above scheme, the physiological data also includes: lactic acid accumulation;

[0043] Based on whether the amount of lactic acid accumulation reaches the second preset range, the exercise type of the target object is determined to be anaerobic exercise.

[0044] A second aspect of this disclosure provides a motion effect detection device for use in an electronic device, the device comprising:

[0045] The first detection module is used to detect physiological data of the target object during movement.

[0046] The first determining module is used to determine the exercise load and exercise capacity information of the target object during movement based on physiological data;

[0047] The second determining module is used to determine the exercise effect of the target object based on the exercise load and exercise capacity information.

[0048] Based on the above solution, the device further includes:

[0049] The exercise intensity determination module is used to determine the current exercise intensity of the target object based on physiological data;

[0050] The exercise load determination module is used to determine the exercise load based on the current exercise intensity.

[0051] Based on the above scheme, the exercise load determination module is specifically used to determine the exercise load according to the current exercise intensity and the maximum exercise intensity.

[0052] Based on the above scheme, the exercise intensity determination module is specifically used to determine the current exercise intensity of the target object based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

[0053] Based on the above solution, the device further includes:

[0054] The motion type determination module is used to determine the current motion type of the target object based on the physiological data.

[0055] The exercise capacity determination module is used to determine the exercise capacity information of the target object based on the physiological data associated with the exercise type.

[0056] Based on the above solution, the device further includes:

[0057] The aerobic exercise capacity determination module is used to determine the aerobic exercise capacity information of the target object based on the oxygen intake of the target object when the exercise type is aerobic exercise.

[0058] Based on the above scheme, the module for determining aerobic exercise capacity is specifically used to determine the aerobic exercise capacity information of the target object based on the oxygen intake of the target object during the aerobic exercise and the heart rate change information of the target object during the exercise when the exercise type is aerobic exercise.

[0059] Based on the above solution, the device further includes:

[0060] The anaerobic exercise capacity determination module is used to determine the anaerobic exercise capacity information of the target object based on the lactate accumulation when the exercise type is anaerobic exercise.

[0061] Based on the above scheme, the anaerobic exercise capacity determination module is specifically used to determine the anaerobic exercise capacity information of the target object based on the lactate accumulation of the target object and the number of times the target object enters the anaerobic exercise when the exercise type is anaerobic exercise.

[0062] Based on the above scheme, the device further includes:

[0063] The second detection module is used to detect the motion patterns of the target object;

[0064] The calculation parameter determination module is used to determine the calculation parameters of the oxygen uptake based on the exercise.

[0065] The oxygen uptake determination module is used to determine the oxygen uptake based on the calculation parameters.

[0066] Based on the above scheme, the device further includes:

[0067] The filtering module is used to filter out abnormal values ​​in the physiological data;

[0068] The first determining module is specifically used to determine the exercise load and exercise capacity information of the target object during movement based on the physiological data from which the outliers have been filtered out.

[0069] Based on the above scheme, the filtering module is specifically used to filter out values ​​in the physiological data that are outside the first preset interval to obtain filtered physiological data, and / or to filter out values ​​in the physiological data whose rate of change is outside the preset rate of change between two adjacent moments during exercise to obtain filtered physiological data.

[0070] Based on the above scheme, the physiological data includes heart rate;

[0071] The exercise type determination module is specifically used to determine whether the exercise type of the target object is aerobic or anaerobic exercise based on the range of the heart rate.

[0072] Based on the above scheme, the physiological data also includes: lactic acid accumulation;

[0073] The exercise type determination module is specifically used to determine the exercise type of the target object as anaerobic exercise based on whether the lactic acid accumulation reaches a second preset range.

[0074] A third aspect of this disclosure provides an electronic device, comprising:

[0075] Memory used to store processor-executable instructions;

[0076] The processor is connected to the memory;

[0077] The processor is configured to execute the motion effect detection method as described in any of the preceding descriptions.

[0078] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium, wherein when instructions in the storage medium are executed by a computer processor, the computer is able to perform the motion effect detection method as described in any of the preceding embodiments.

[0079] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0080] The exercise effect detection method disclosed herein detects physiological data of a target object during exercise, determines the exercise load and exercise capacity information of the target object based on the physiological data, and determines the exercise effect of the target object based on the exercise load and exercise capacity information. On the one hand, the exercise effect is determined by jointly using exercise load and exercise capacity information, taking into account the target object's exercise capacity information, and thus comprehensively determining the exercise effect. This precise determination of exercise effect reduces over-exercise or under-exercise of the target object. On the other hand, by determining exercise load and exercise capacity information based on physiological data and finally combining the exercise load and exercise capacity information to obtain the exercise effect, it has strong versatility and is basically applicable to all users. Attached Figure Description

[0081] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0082] Figure 1 This is a flowchart illustrating a motion effect detection method according to an exemplary embodiment;

[0083] Figure 2 This is a flowchart illustrating a motion effect detection method according to an exemplary embodiment;

[0084] Figure 3 This is a flowchart illustrating a motion effect detection method according to an exemplary embodiment;

[0085] Figure 4 This is a flowchart illustrating a motion effect detection method according to an exemplary embodiment;

[0086] Figure 5 This is a flowchart illustrating a motion effect detection method according to an exemplary embodiment;

[0087] Figure 6 This is a schematic diagram of the structure of a motion effect detection device according to an exemplary embodiment;

[0088] Figure 7This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0089] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0090] like Figure 1 As shown in the embodiments of this disclosure, a motion effect detection method is provided and applied to an electronic device. The method includes:

[0091] S1100: Detects physiological data of the target object during movement;

[0092] S1200: Based on physiological data, determine the exercise load and exercise capacity information of the target object during movement;

[0093] S1300: Determine the exercise effect of the target object based on the exercise load and exercise capacity information.

[0094] The electronic device mentioned here can be a variety of mobile terminals or smart wearable devices. For example, smart wearable devices include, but are not limited to, smartwatches, smart bracelets, smart ankle bracelets, etc.

[0095] The physiological data here may include, but is not limited to, data on physiological characteristics of the target subject that change during exercise, such as oxygen uptake, lactate accumulation, and / or heart rate.

[0096] During exercise, the target subject's oxygen uptake, lactate accumulation, and heart rate change, thus affecting the exercise effect. This physiological data is typically pre-stored in the electronic device.

[0097] It is worth noting that data on physiological parameters of the target subject, such as age, height, weight, and gender, which do not change significantly during the moment of movement, can also be used to determine athletic ability.

[0098] The indicators used to determine exercise load here include, but are not limited to, various indicators such as exercise intensity. For example, these indicators include, but are not limited to, speed, power, heart rate, altitude, and / or exercise distance, which characterize exercise load.

[0099] Understandably, the physiological data and corresponding physiological parameters here can be preset in an electronic device before exercise. The physiological parameters are categorized into variable and invariable data based on whether they change during exercise. Variable data, for example, includes oxygen uptake, lactate accumulation, and heart rate. Invariable data, for example, includes age, height, weight, and gender.

[0100] Electronic devices can record and analyze all heart rate data of a target subject during exercise. When using maximum heart rate to determine exercise load, the maximum heart rate determined from historical records is used. Maximum heart rate can be understood as the heart rate value of the target subject under extreme exercise conditions. Electronic devices can also record lactate accumulation and oxygen uptake during exercise and analyze these data to determine the exercise effect on the target subject. Here, maximum heart rate can be understood as the heart rate value of the target subject under extreme exercise conditions.

[0101] Sensors that detect physiological data or exercise load may include multiple sensors such as heart rate sensors, position sensors (GPS), speed sensors, and / or acceleration sensors.

[0102] In one embodiment, the sensor that detects physiological data or exercise load is a built-in sensor of the electronic device.

[0103] In one embodiment, when the electronic device lacks a corresponding sensor, but other electronic devices with a short-range wireless or wired connection to the electronic device have a corresponding sensor, the physiological data and / or exercise load of the target object can be collected by the sensors of other devices and sent to the electronic device, so as to facilitate the electronic device to obtain the physiological data and / or exercise load of the target object.

[0104] Understandably, this disclosure involves detecting the physiological data of a target object during exercise, and determining the exercise load and exercise capacity information of the target object based on the physiological data. After the exercise ends, the exercise effect of the target object is determined based on the exercise load and exercise capacity information; or, during the exercise of the target object, the exercise effect is given in stages based on the exercise load and exercise capacity information, so that when the actual exercise effect reaches the expected level, a reminder message is output to promptly remind the target object to reduce excessive exercise, etc.

[0105] Taking heart rate as an example, the exercise load is determined to be either aerobic or anaerobic based on the average heart rate. When anaerobic exercise is involved, the anaerobic and aerobic effects on the target individual are determined based on exercise capacity information. When anaerobic exercise is not involved, the aerobic effect on the target individual is determined.

[0106] Thus, this disclosure considers the physiological data of the target subject when determining exercise load and exercise capacity information, making the exercise load and exercise capacity information adaptable to different target subjects and increasing the applicable group for exercise effect detection.

[0107] On the one hand, the exercise effect is determined by combining exercise load and exercise capacity information, taking into account the exercise capacity information of the target individual, and thus comprehensively determining the exercise effect. This precise determination of exercise effect reduces over-exercise or under-exercise by the target individual. On the other hand, exercise load and exercise capacity information are determined based on physiological data, and the exercise effect is ultimately obtained by combining these two information. This method is highly versatile and applicable to virtually all users.

[0108] In some embodiments, such as Figure 2 As shown, S1200 includes:

[0109] S1210: Determine the current motion intensity of the target object based on physiological data;

[0110] S1220: Determine the exercise load based on the current exercise intensity.

[0111] Understandably, the physiological data of the target object during movement is detected, and the current movement intensity of the target object is determined based on the detected physiological data. When the physiological data changes during movement, the current movement intensity of the target object will also change, and the exercise load is determined based on the current movement intensity.

[0112] Taking heart rate as an example of physiological data, the heart rate of a target individual changes during exercise. Heart rate sensors acquire heart rate data, and based on this data, the target individual's average heart rate, maximum instantaneous heart rate, minimum instantaneous heart rate, rate of change of heart rate over a specific period, and heart rate fluctuations can be determined, reflecting the target individual's current exercise intensity. For example, heart rate is positively correlated with exercise intensity; that is, a higher heart rate indicates a greater current exercise intensity. Optionally, the average heart rate during exercise can be obtained from the heart rate sensor data, and this average heart rate can be used as the target individual's current exercise intensity.

[0113] Understandably, exercise load is determined based on the current exercise intensity. Exercise intensity and exercise load are positively correlated; the higher the exercise intensity, the greater the exercise load. Exercise load can be determined based on average heart rate and the gender of the target individual. Exercise load is related to the gender of the target individual; for the same exercise intensity, the exercise load for men is generally lower than that for women.

[0114] In some embodiments, S1220 includes:

[0115] The exercise load is determined based on the current exercise intensity and the maximum exercise intensity.

[0116] Specifically, to ensure a more accurate exercise load, a first exercise load can be determined based on the current exercise intensity, and a second exercise load can be determined based on the maximum exercise intensity. The first exercise load is then normalized using the second exercise load to obtain the final exercise load. Normalization can be achieved by dividing the first exercise load by the second exercise load. This normalization process yields a value between 0 and 1, which indicates whether the target user's current exercise intensity exceeds or is significantly less than their permissible exercise intensity.

[0117] By normalizing the exercise load, the determination of the exercise effect of the target object can be more accurate, reducing the problem of inaccurate determination of the exercise effect when the change in exercise intensity is too large.

[0118] Taking heart rate as an example of physiological data, the current exercise intensity can be quantified as the average heart rate, and the maximum exercise intensity can be quantified as the maximum heart rate. The calculation formula for the first exercise load AOE1 and the second exercise load AOE2 is as follows:

[0119] AOE1 = D × AHR × 0.64e Y (1)

[0120] AOE2 = D × MHR × 0.64e Y (2)

[0121] In formulas (1) and (2), D is the preset duration, AHR is the average heart rate, MHR is the maximum heart rate, e is the natural constant with a value of approximately 2.718281828459045, and Y is a gender-related constant.

[0122] There are several ways to determine the maximum heart rate. For example, it can be determined based on the target's historical heart rate data, or based on monitoring by sensors or portable detection devices, or based on the target's age.

[0123] In some embodiments, S1210 includes:

[0124] The current exercise intensity of the target object is determined based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

[0125] For example, the preset duration can be 5s, 6s, 7s, or 8s, etc.

[0126] Understandably, during movement, the current movement intensity of a target object is determined every time a preset time period is reached.

[0127] In one embodiment, heart rate values ​​collected by sensors at various times within a preset time period are used. The average heart rate within that preset time period is determined based on these heart rate values, and this average heart rate is used as the target object's current exercise intensity. The target object's maximum heart rate is used as the target object's maximum exercise intensity. Therefore, the exercise load is determined based on the current exercise intensity and the maximum exercise intensity. The maximum heart rate can be determined in various ways, such as based on the target object's historical heart rate data, based on monitoring by sensors or portable detection devices, or based on the target object's age, etc.

[0128] In one embodiment, oxygen uptake is determined based on motion data from sensors at various times within a preset time period. The average oxygen uptake over the preset time period is then determined based on the oxygen uptake at each time point, and this average oxygen uptake is used as the current exercise intensity of the target object. The target object's maximum oxygen uptake is used as its maximum exercise intensity. Therefore, the exercise load is determined based on the current exercise intensity and the maximum exercise intensity.

[0129] There are several ways to determine maximum oxygen uptake. For example, maximum oxygen uptake can be determined by the target subject's age, gender, weight, resting heart rate, and maximum heart rate. It can also be determined by the target subject's exercise activity and historical exercise data.

[0130] In one embodiment, lactate accumulation is acquired from sensors at various times within a preset time period. An average lactate accumulation over the preset time period is determined based on the lactate accumulation at each time point, and this average lactate accumulation is used as the target's current exercise intensity. When the target is engaged in anaerobic exercise, the average lactate accumulation can be used as the target's current exercise intensity. The target's maximum lactate accumulation value is used as the target's maximum exercise intensity. Thus, the exercise load is determined based on the current exercise intensity and the maximum exercise intensity. For example, the maximum lactate accumulation value is determined using the target's historical exercise data.

[0131] In one embodiment, average power is calculated based on information such as average speed, friction, and gravity acquired by sensors at various times within a preset time period. This average power is then used as the current motion intensity of the target object. For example, when a user is cycling or exercising on an elliptical machine, the current motion intensity of the target object can be determined based on the average power. The maximum power of the target object is then used as its maximum motion intensity. The exercise load is then determined based on both the current and maximum motion intensities. For example, the maximum power can be determined using historical motion data of the target object.

[0132] In one embodiment, the average step frequency is calculated based on the step frequency of the motion acquired by sensors at various times within a preset time period. The average step frequency is used as the current motion intensity of the target object. The maximum step frequency of the target object is used as the maximum motion intensity of the target object. Thus, the motion load is determined based on the current motion intensity and the maximum motion intensity. For example, the maximum power is determined using the historical motion data of the target object.

[0133] In some embodiments, such as Figure 3 As shown, S1200 includes:

[0134] S1230: Based on the physiological data, determine the current movement type of the target object;

[0135] S1240: Determine the target object's motor ability information based on the physiological data associated with the type of movement.

[0136] Understandably, when physiological data of a target object during movement is detected, the current type of movement can be determined based on this data. For example, taking heart rate as an example, the current type of movement can be determined based on the average heart rate during exercise. The target type includes aerobic exercise and anaerobic exercise.

[0137] Aerobic and anaerobic exercise consume different physiological data from the target individual; therefore, the type of exercise needs to be correlated with physiological data. Based on the physiological data associated with the type of exercise, the target individual's exercise capacity information can be determined.

[0138] In some embodiments, S1240 includes:

[0139] S1241: When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise.

[0140] The type of exercise here is aerobic exercise, and the physiological data associated with aerobic exercise is oxygen uptake.

[0141] Understandably, when the physiological data of the target object's movement indicates that the type of exercise within the current preset duration is aerobic exercise, the oxygen uptake of the target object during exercise is determined, and the aerobic exercise capacity information of the target object is determined based on the oxygen uptake.

[0142] In one embodiment, oxygen uptake is the maximum oxygen uptake. Maximum oxygen uptake can be determined using the target subject's age, sex, weight, resting heart rate, and maximum heart rate. It can also be determined using the target subject's exercise type and historical exercise data.

[0143] In some embodiments, S1241 includes:

[0144] When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise and the heart rate change information of the target object during the exercise.

[0145] The type of exercise here is aerobic exercise, and the physiological data associated with aerobic exercise are oxygen uptake and heart rate changes.

[0146] The heart rate variability information here is the difference in heart rate variability between the start and end of exercise.

[0147] The formula for calculating heart rate variability is:

[0148]

[0149] In formula (3), RMSSD represents heart rate variability, NN interval is the time interval between two heart rate collections, n is the number of NN intervals, NNI is the difference in heart rate between two adjacent NN intervals, and j is a positive integer less than or equal to n.

[0150] The NN interval here is obtained by preprocessing the RR interval to remove outliers. The RR interval is the time interval between two heart rate acquisitions. The RR interval is used to detect human heart rate during exercise using photoplethysmography (PPG) technology. It uses photoelectric sensors to detect the difference in the intensity of reflected light after absorption by human blood and tissues, recording the changes in blood vessel volume during the cardiac cycle, and calculating the heart rate from the obtained pulse waveform.

[0151] Based on the above heart rate variability, the heart rate variability during the initial phase within the preset start time and the heart rate variability during the final phase within the preset end time can be calculated.

[0152] For example, the preset start duration can be 20 seconds, 30 seconds, or 60 seconds, etc.

[0153] For example, the preset end time can be 20 seconds, 30 seconds, or 60 seconds, etc.

[0154] Understandably, motion data of the moving object is acquired by sensors, and oxygen uptake is determined based on this data. The aerobic exercise capacity information of the target object is determined based on its oxygen uptake during aerobic exercise, as well as the difference between its heart rate variability at the beginning and end of the exercise.

[0155] In some embodiments, S1240 includes:

[0156] S1242: When the exercise type is anaerobic exercise, determine the anaerobic exercise capacity information of the target object based on the lactic acid accumulation of the target object.

[0157] The type of exercise here is anaerobic exercise, and the physiological data associated with anaerobic exercise is the amount of lactic acid accumulation.

[0158] Understandably, when physiological data from the detected target's movement indicates that the exercise type within the current preset duration is anaerobic, the lactate accumulation value of the target during exercise is determined for each preset duration. After the exercise ends, the lactate accumulation values ​​obtained from each preset duration are added together to obtain the lactate accumulation amount, and the target's aerobic exercise capacity information is determined based on the lactate accumulation amount.

[0159] In some embodiments, S1242 includes:

[0160] When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the amount of lactic acid accumulation in the target object and the number of times the target object enters the anaerobic exercise during exercise.

[0161] The type of exercise here is anaerobic exercise, and the physiological data associated with anaerobic exercise are the amount of lactic acid accumulation and the number of times anaerobic exercise is performed.

[0162] Understandably, during exercise, a preset duration determines one type of exercise, and exercise may include multiple preset durations. Therefore, each time a preset duration is reached, and the determined exercise type is anaerobic exercise, the number of anaerobic exercises is accumulated. After the exercise, the target individual's anaerobic exercise capacity is determined based on the amount of lactic acid buildup and the number of anaerobic exercises.

[0163] For example, the preset duration can be 5s, 6s, 7s, or 8s, etc.

[0164] In some embodiments, such as Figure 4 As shown, the method further includes:

[0165] S1400: Detect the motion of the target object;

[0166] S1500: Determine the calculation parameters for the oxygen uptake based on the exercise;

[0167] S1600: Determine the oxygen uptake based on the calculated parameters.

[0168] For example, sports activities may include running, walking, cycling, or using an elliptical machine.

[0169] Understandably, electronic devices detect the exercise selected by the target before the exercise begins. The type of exercise affects the calculation of maximum oxygen uptake (VO2 max), and different exercises require different calculation parameters. For example, the calculation parameters for running and walking may be speed, acceleration, or cadence, while the calculation parameters for cycling and elliptical training are power. After determining the calculation parameters, VO2 max can be determined based on them.

[0170] In one embodiment, the calculation parameter for maximum oxygen uptake also includes slope. During exercise, the electronic device records the target's altitude and distance traveled. Altitude and distance can be obtained from sensors built into the wearable device or from external devices connected via wired or wireless means. These parameters are used to calculate the user's real-time slope.

[0171] In some embodiments, such as Figure 5 As shown, the method further includes:

[0172] S1700: Filter outliers in the physiological data;

[0173] The S1200 includes:

[0174] S1250: Based on the physiological data from which the outliers have been filtered out, determine the exercise load and exercise capacity information of the target object during movement.

[0175] Understandably, when detecting physiological data during the movement of a target object, outliers may exist in the physiological data. For example, the physiological data is [10,12,11,25,9,10,9,45,13,1,12,10,11,78,12,12,13,10,9]. In this sequence, it is clear that 25, 45, and 78 are much larger than the other data, and 1 is much smaller than the other data. Therefore, these four data are considered outliers.

[0176] After detecting physiological data of the target object during movement, outliers are filtered out. Using the filtered outlier-free physiological data, the exercise load and ability information of the target object during movement are determined. This improves the accuracy of exercise load and ability information.

[0177] The detected heart rate and exercise load signals in this disclosure are low-pass filtered to filter out noise signals, making the final exercise effect information more accurate.

[0178] In some embodiments, S1700 includes:

[0179] Values ​​outside the first preset range in the physiological data are filtered out to obtain the filtered physiological data.

[0180] And / or,

[0181] The physiological data is filtered out when the rate of change of the physiological data at two adjacent moments during the exercise is outside the preset rate of change, thus obtaining the filtered physiological data.

[0182] In one embodiment, a minimum and a maximum value of the physiological data are determined, and the minimum and maximum values ​​form a first preset interval. Physiological data that are less than the minimum value or greater than the maximum value are filtered out to obtain filtered physiological data.

[0183] In one embodiment, physiological data from two adjacent time points are acquired from all physiological data, and the rate of change between the two physiological data points is calculated. This rate of change is compared with a preset rate of change, and physiological data with a rate of change greater than the preset rate of change are filtered out to obtain filtered physiological data.

[0184] In one embodiment, after filtering out values ​​outside the first preset interval, values ​​whose rate of change of physiological data at two adjacent moments during the exercise is outside the preset rate of change can be filtered out.

[0185] In some embodiments, the physiological data includes heart rate;

[0186] S1230 includes:

[0187] Based on the range of the heart rate, the exercise type of the target object is determined to be either aerobic or anaerobic exercise.

[0188] Understandably, physiological data includes heart rate, specifically the heart rate detected during the target subject's exercise. Based on the heart rate, the target subject's current exercise type is determined, which may be aerobic or anaerobic exercise. Specifically, a heart rate range for anaerobic exercise is defined. When the average heart rate within a preset duration of exercise falls within this range, the exercise type for that preset duration is determined to be anaerobic exercise.

[0189] During exercise, at each preset duration, and when the exercise type within that preset duration is determined to be anaerobic, the anaerobic exercise load for that preset duration is determined based on physiological data. After the exercise, the anaerobic exercise loads for each preset duration are summed to obtain the sum of the anaerobic exercise loads for the target individual. Based on this sum of anaerobic exercise loads and exercise capacity information, the anaerobic exercise effect on the target individual is determined.

[0190] Understandably, during exercise, physiological data within each preset duration participates in aerobic exercise. Upon reaching each preset duration, the exercise load for that duration is determined based on the physiological data within that duration. After exercise, the exercise loads for each preset duration are summed to obtain the total exercise load. Based on this total exercise load and exercise capacity information, the aerobic exercise effect on the target individual is determined.

[0191] In some embodiments, the physiological data further includes: lactic acid accumulation;

[0192] Based on whether the amount of lactic acid accumulation reaches the second preset range, the exercise type of the target object is determined to be anaerobic exercise.

[0193] Understandably, the physiological data includes: lactate accumulation, and the amount of lactate accumulation detected during exercise. When the lactate accumulation reaches a second preset range, the exercise type of the target individual is determined to be anaerobic exercise.

[0194] Understandably, during exercise, a preset duration is used to measure lactic acid buildup, and the exercise type is determined based on this amount. Exercise may include multiple preset durations. Therefore, after each preset duration is reached, if the lactic acid buildup reaches a second preset range, and the exercise type is determined to be anaerobic exercise, the number of anaerobic exercise sessions is accumulated.

[0195] After exercise, the anaerobic exercise capacity of the target individual is determined based on the amount of lactic acid buildup and the number of anaerobic exercises performed.

[0196] Understandably, the exercise effects on the aforementioned target group include at least aerobic exercise effects and anaerobic exercise effects.

[0197] The formula for calculating the effect of aerobic exercise is as follows:

[0198] The effect of aerobic exercise = A1 × exercise load + B1 × maximum oxygen uptake + C1 × difference in heart rate variability at the start and end of exercise + W1;

[0199] Where A1 is the weight of exercise load, B1 is the weight of VO2 max, C1 is the weight of the difference in heart rate variability at the start and end of exercise, and W1 is the bias value. The exercise load here is the sum of the exercise loads over the total exercise duration.

[0200] The formula for calculating the effects of anaerobic exercise is:

[0201] Anaerobic exercise effect = (A2 × anaerobic exercise load + B2 × lactic acid accumulation) × number of times anaerobic exercise was achieved + W2;

[0202] Where A2 represents the weight of the anaerobic exercise load, B2 represents the weight of lactate accumulation, and W2 is the bias value. The anaerobic exercise load here refers to the sum of the anaerobic exercise loads during the exercise process.

[0203] like Figure 6 As shown, this disclosure provides a motion effect detection device for use in electronic devices, the device comprising:

[0204] The first detection module 210 is used to detect physiological data of the target object during movement;

[0205] The first determining module 220 is used to determine the exercise load and exercise capacity information of the target object during exercise based on physiological data;

[0206] The second determining module 230 is used to determine the motion effect of the target object based on the motion load and motion ability information.

[0207] In some embodiments, the apparatus further includes:

[0208] The exercise intensity determination module is used to determine the current exercise intensity of the target object based on physiological data;

[0209] The exercise load determination module is used to determine the exercise load based on the current exercise intensity.

[0210] In some embodiments, the exercise load determination module is specifically used to determine the exercise load based on the current exercise intensity and the maximum exercise intensity.

[0211] In some embodiments, the exercise intensity determination module is specifically used to determine the current exercise intensity of the target object based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset duration at the current time.

[0212] In some embodiments, the apparatus further includes:

[0213] The motion type determination module is used to determine the current motion type of the target object based on the physiological data.

[0214] The exercise capacity determination module is used to determine the exercise capacity information of the target object based on the physiological data associated with the exercise type.

[0215] In some embodiments, the apparatus further includes:

[0216] The aerobic exercise capacity determination module is used to determine the aerobic exercise capacity information of the target object based on the oxygen intake of the target object when the exercise type is aerobic exercise.

[0217] In some embodiments, the module for determining aerobic exercise capacity is specifically used to determine the aerobic exercise capacity information of the target object based on the oxygen uptake of the target object during the aerobic exercise and the heart rate change information of the target object during the exercise when the exercise type is aerobic exercise.

[0218] In some embodiments, the apparatus further includes:

[0219] The anaerobic exercise capacity determination module is used to determine the anaerobic exercise capacity information of the target object based on the lactate accumulation when the exercise type is anaerobic exercise.

[0220] In some embodiments, the anaerobic exercise capacity determination module is specifically used to determine the anaerobic exercise capacity information of the target object based on the amount of lactate accumulation in the target object and the number of times the target object enters the anaerobic exercise when the exercise type is anaerobic exercise.

[0221] In some embodiments, the apparatus further includes:

[0222] The second detection module is used to detect the motion patterns of the target object;

[0223] The calculation parameter determination module is used to determine the calculation parameters of the oxygen uptake based on the exercise.

[0224] The oxygen uptake determination module is used to determine the oxygen uptake based on the calculation parameters.

[0225] In some embodiments, the apparatus further includes:

[0226] The filtering module is used to filter out abnormal values ​​in the physiological data;

[0227] The first determining module is specifically used to determine the exercise load and exercise capacity information of the target object during movement based on the physiological data from which the outliers have been filtered out.

[0228] In some embodiments, the filtering module is specifically used to filter out values ​​in the physiological data that are outside a first preset interval to obtain filtered physiological data, and / or to filter out values ​​in the physiological data whose rate of change is outside a preset rate of change at two adjacent moments during exercise to obtain filtered physiological data.

[0229] In some embodiments, the physiological data includes heart rate;

[0230] The exercise type determination module is specifically used to determine whether the exercise type of the target object is aerobic or anaerobic exercise based on the range of the heart rate.

[0231] In some embodiments, the physiological data further includes: lactic acid accumulation;

[0232] The exercise type determination module is specifically used to determine the exercise type of the target object as anaerobic exercise based on whether the lactic acid accumulation reaches a second preset range.

[0233] This disclosure provides an electronic device, including:

[0234] Memory used to store processor-executable instructions;

[0235] The processor is connected to the memory;

[0236] The processor is configured to execute the motion effect detection method provided by any of the aforementioned technical solutions.

[0237] Processors may include various types of storage media that are non-transitory computer storage media, capable of continuing to store information after the electronic device loses power.

[0238] The processor can connect to memory via a bus or similar means to read executable programs stored in memory; for example, it can execute programs such as... Figures 1 to 5 At least one of the methods shown.

[0239] Figure 7 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be included in an electronic device such as a mobile phone or mobile computer, or a device such as a server. In short, the data processing electronic device 800 may be included in any type of electronic device.

[0240] Reference Figure 7 Electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0241] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0242] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device 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 storage, flash memory, magnetic disk, or optical disk.

[0243] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0244] Multimedia component 808 includes a screen that provides an output interface between electronic device 800 and user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When device 800 is in an operational state, such as a shooting state or a video state, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0245] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operational state, such as a call state, recording state, or voice recognition state. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0246] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0247] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0248] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as Wi-Fi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0249] In an exemplary embodiment, the apparatus 800 may be implemented by 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, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0250] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0251] This disclosure provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of a UE, the UE or a base station can execute the motion effect detection method provided in any of the foregoing embodiments, enabling it to perform... Figures 1 to 5 At least one of the methods shown.

[0252] A motion effect detection method applied to electronic devices may include: detecting physiological data of a target object during motion; determining the motion load and motion capacity information of the target object during motion based on the physiological data; and determining the motion effect of the target object based on the motion load and motion capacity information.

[0253] Understandably, determining the exercise load and exercise capacity information of the target object during movement based on physiological data includes:

[0254] Based on physiological data, determine the current motion intensity of the target object;

[0255] The exercise load is determined based on the current exercise intensity.

[0256] Understandably, determining the exercise load based on the current exercise intensity includes:

[0257] The exercise load is determined based on the current exercise intensity and the maximum exercise intensity.

[0258] Understandably, determining the current motion intensity of the target object based on physiological data includes:

[0259] The current exercise intensity of the target object is determined based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

[0260] Understandably, determining the exercise load and exercise capacity information of the target object during movement based on physiological data includes:

[0261] Based on the physiological data, determine the current movement type of the target object;

[0262] Based on the physiological data associated with the type of exercise, determine the target object's motor ability information.

[0263] Understandably, determining the target object's motor ability information based on physiological data associated with the type of movement includes:

[0264] When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise.

[0265] Understandably, when the type of exercise is aerobic exercise, determining the aerobic exercise capacity information of the target object based on the oxygen uptake of the target object during the aerobic exercise includes:

[0266] When the type of exercise is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen intake of the target object during the aerobic exercise and the heart rate change information of the target object during the exercise.

[0267] Understandably, determining the target object's motor ability information based on physiological data associated with the type of movement includes:

[0268] When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the amount of lactic acid accumulation in the target object.

[0269] Understandably, when the type of exercise is anaerobic exercise, determining the anaerobic exercise capacity information of the target object based on the amount of lactate accumulation includes:

[0270] When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the amount of lactic acid accumulation in the target object and the number of times the target object enters the anaerobic exercise during exercise.

[0271] Understandably, the method also includes:

[0272] Detect the motion patterns of the target object;

[0273] The calculation parameters for the oxygen uptake are determined based on the sport.

[0274] The oxygen uptake is determined based on the calculated parameters.

[0275] Understandably, the method also includes:

[0276] Filter outliers from the physiological data;

[0277] The step of determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes:

[0278] Based on the physiological data from which outliers have been filtered out, the exercise load and exercise capacity information of the target object during movement are determined.

[0279] Understandably, filtering outliers in the physiological data includes:

[0280] Values ​​outside the first preset range in the physiological data are filtered out to obtain the filtered physiological data.

[0281] And / or,

[0282] The physiological data is filtered out when the rate of change of the physiological data at two adjacent moments during the exercise is outside the preset rate of change, thus obtaining the filtered physiological data.

[0283] Understandably, the physiological data includes heart rate;

[0284] Determining the current movement type of the target object based on the physiological data includes:

[0285] Based on the range of the heart rate, the exercise type of the target object is determined to be either aerobic or anaerobic exercise.

[0286] Understandably, the physiological data also include: lactic acid accumulation;

[0287] Based on whether the amount of lactic acid accumulation reaches the second preset range, the exercise type of the target object is determined to be anaerobic exercise.

[0288] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0289] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for detecting motion effects, applied to electronic devices, characterized in that, The method includes: Detect physiological data of the target object during movement; Based on physiological data, determine the exercise load and exercise capacity information of the target object during movement; Based on the exercise load and exercise capacity information, determine the exercise effect on the target object; The step of determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes: Based on the physiological data, the current exercise type of the target object is determined. When the exercise type is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen uptake of the target object during the aerobic exercise and the heart rate change information of the target object during exercise. The oxygen uptake is determined based on calculation parameters, which are determined based on the exercise selected by the target object, and different exercise items have different calculation parameters for oxygen uptake. The heart rate change information is the difference in heart rate variability between the start and end of exercise. When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the lactate accumulation of the target object and the number of times the target object enters the anaerobic exercise.

2. The method according to claim 1, characterized in that, The step of determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes: Based on physiological data, determine the current motion intensity of the target object; The exercise load is determined based on the current exercise intensity.

3. The method according to claim 2, characterized in that, Determining the exercise load based on the current exercise intensity includes: The exercise load is determined based on the current exercise intensity and the maximum exercise intensity.

4. The method according to claim 2, characterized in that, Determining the current movement intensity of the target object based on physiological data includes: The current exercise intensity of the target object is determined based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

5. The method according to claim 1, characterized in that, The method further includes: Filter outliers from the physiological data; The step of determining the exercise load and exercise capacity information of the target object during exercise based on physiological data includes: Based on the physiological data from which outliers have been filtered out, the exercise load and exercise capacity information of the target object during movement are determined.

6. The method according to claim 5, characterized in that, The filtering of outliers in the physiological data includes: Values ​​outside the first preset range in the physiological data are filtered out to obtain the filtered physiological data. And / or, The physiological data is filtered out when the rate of change of the physiological data at two adjacent moments during the exercise is outside the preset rate of change, thus obtaining the filtered physiological data.

7. The method according to claim 1 or 2, characterized in that, The physiological data includes heart rate; Determining the current movement type of the target object based on the physiological data includes: Based on the range of the heart rate, the exercise type of the target object is determined to be either aerobic or anaerobic exercise.

8. The method according to claim 1 or 2, characterized in that, The physiological data also include: lactic acid accumulation; Based on whether the amount of lactic acid accumulation reaches the second preset range, the exercise type of the target object is determined to be anaerobic exercise.

9. A motion effect detection device, applied to electronic equipment, characterized in that, The device includes: The first detection module is used to detect physiological data of the target object during movement. The first determining module is used to determine the exercise load and exercise capacity information of the target object during movement based on physiological data; The second determining module is used to determine the exercise effect of the target object based on the exercise load and exercise capacity information; The first determining module is specifically used for: Based on the physiological data, the current exercise type of the target object is determined. When the exercise type is aerobic exercise, the aerobic exercise capacity information of the target object is determined based on the oxygen uptake of the target object during the aerobic exercise and the heart rate change information of the target object during exercise. The oxygen uptake is determined based on calculation parameters, which are determined based on the exercise selected by the target object, and different exercise items have different calculation parameters for oxygen uptake. The heart rate change information is the difference in heart rate variability between the start and end of exercise. When the exercise type is anaerobic exercise, the anaerobic exercise capacity information of the target object is determined based on the lactate accumulation of the target object and the number of times the target object enters the anaerobic exercise.

10. The apparatus according to claim 9, characterized in that, The device further includes: The exercise intensity determination module is used to determine the current exercise intensity of the target object based on physiological data; The exercise load determination module is used to determine the exercise load based on the current exercise intensity.

11. The apparatus according to claim 10, characterized in that, The exercise load determination module is specifically used to determine the exercise load based on the current exercise intensity and the maximum exercise intensity.

12. The apparatus according to claim 10, characterized in that, The exercise intensity determination module is specifically used to determine the current exercise intensity of the target object based on at least one of the average heart rate, average oxygen uptake, average lactate accumulation, and average power within a preset time period at the current time.

13. The apparatus according to claim 9, characterized in that, The device further includes: The filtering module is used to filter out abnormal values ​​in the physiological data; The first determining module is specifically used to determine the exercise load and exercise capacity information of the target object during movement based on the physiological data from which the outliers have been filtered out.

14. The apparatus according to claim 13, characterized in that, The filtering module is specifically used to filter out values ​​in the physiological data that are outside the first preset interval, to obtain filtered physiological data, and / or to filter out values ​​in the physiological data whose rate of change is outside the preset rate of change at two adjacent moments during exercise, to obtain filtered physiological data.

15. The apparatus according to claim 9 or 10, characterized in that, The physiological data includes heart rate; The exercise type determination module is specifically used to determine whether the exercise type of the target object is aerobic or anaerobic exercise based on the range of the heart rate.

16. The apparatus according to claim 9 or 10, characterized in that, The physiological data also include: lactic acid accumulation; The exercise type determination module is specifically used to determine whether the exercise type of the target object is anaerobic exercise based on whether the lactic acid accumulation reaches a second preset range.

17. An electronic device, characterized in that, include: Memory used to store processor-executable instructions; The processor is connected to the memory; The processor is configured to perform the motion effect detection method provided in any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of a computer, enable the computer to perform the motion effect detection method provided in any one of claims 1 to 8.

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