Motion monitoring method and electronic equipment

Abstract

The present application discloses a motion monitoring method and electronic device, belonging to the field of data processing technology. The method comprises: obtaining a motion signal sequence, performing a short-time Fourier transform on the motion signal sequence to obtain a frequency domain signal sequence corresponding to a plurality of time windows, determining a time domain signal sequence with a target frequency based on the frequency domain signal sequence corresponding to the plurality of time windows, determining the start time and end time of each repeated motion of the target user based on the time domain signal sequence, and determining the motion type of each repeated motion based on the motion signal sequence and the start time and end time of the repeated motion. The method provided by the present application does not require the wearing of a wearable device, and does not bring about privacy and security issues. By processing the motion signal sequence, and then determining the motion type of each repeated motion of the target user, the purpose of monitoring the motion of the target user is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a motion monitoring method and electronic equipment. Background Art

[0002] With the progress of society and the development of science and technology, more and more users improve their physical fitness through exercise and use various methods to monitor exercise, such as monitoring the number of exercises, exercise types, etc.

[0003] At present, there are two main methods for motion monitoring. The first method is to use a wearable device. The built-in sensor of the wearable device can monitor the user's heart rate, speed and other data, and determine the user's exercise status based on the data. However, wearing a wearable device during exercise may make the user feel uncomfortable. The second method is to use a camera to shoot the entire exercise process and determine the exercise status based on the captured video. However, the camera has high requirements on the brightness of the shooting environment, and this method requires obtaining a video of the user's exercise process, which may have privacy and security issues. Summary of the invention

[0004] The present application provides a motion monitoring method and electronic device, which can monitor motion. The technical solution is as follows:

[0005] On the one hand, a motion monitoring method is provided, which is applied to an electronic device, the electronic device is located around a target user, and the electronic device has a function of receiving electromagnetic wave signals in a wireless communication system, the method comprising:

[0006] Acquire a motion signal sequence, where the motion signal sequence is obtained by downsampling a time domain motion signal, where the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user;

[0007] Performing short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows;

[0008] Based on the frequency domain signal sequences corresponding to the multiple time windows, determine a time domain signal sequence having a frequency that is a target frequency, where the target frequency is determined based on the number of sampling points included in the time window;

[0009] Determining the start time and end time of each of the multiple repeated movements of the target user based on the time domain signal sequence;

[0010] Based on the motion signal sequence and the start time and the end time of the multiple repeated motions, the motion type of each repeated motion is determined.

[0011] In another aspect, an electronic device is provided, the electronic device is located around a target user, and the electronic device has a function of receiving electromagnetic wave signals in a wireless communication system, and the electronic device includes a processor, and the processor is used to:

[0012] Acquire a motion signal sequence, where the motion signal sequence is obtained by downsampling a time domain motion signal, where the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user;

[0013] Performing short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows;

[0014] Based on the frequency domain signal sequences corresponding to the multiple time windows, determine a time domain signal sequence having a frequency that is a target frequency, where the target frequency is determined based on the number of sampling points included in the time window;

[0015] Determining the start time and end time of each of the multiple repeated movements of the target user based on the time domain signal sequence;

[0016] Based on the motion signal sequence and the start time and the end time of the multiple repeated motions, the motion type of each repeated motion is determined.

[0017] In another aspect, a motion monitoring device is provided, the device comprising:

[0018] An acquisition module, used to acquire a motion signal sequence, wherein the motion signal sequence is obtained by downsampling a time domain motion signal, wherein the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user;

[0019] A transformation module, used for performing short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows;

[0020] A first determination module is used to determine a time domain signal sequence having a target frequency based on the frequency domain signal sequences corresponding to the multiple time windows, where the target frequency is determined based on the number of sampling points included in the time window;

[0021] A second determination module is used to determine the start time and end time of each repeated movement of the target user based on the time domain signal sequence;

[0022] The third determination module is used to determine the motion type of each repeated motion based on the motion signal sequence and the start time and end time of the multiple repeated motions.

[0023] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned motion monitoring method are implemented.

[0024] On the other hand, a computer program product comprising instructions is provided. When the instructions are executed on a computer, the computer executes the steps of the above-mentioned motion monitoring method.

[0025] The technical solution provided by this application can at least bring the following beneficial effects:

[0026] The method provided in the embodiment of the present application does not require wearing wearable devices and does not cause privacy and security issues. The target user only needs to place the electronic device around the target user. Based on the electromagnetic wave signal received by the electronic device, the motion signal sequence of the target user can be determined. By processing the motion signal sequence, the start time and end time of each repeated motion of the target user can be determined, and then the motion type of each repeated motion of the target user can be determined, thereby achieving the purpose of monitoring the target user's motion. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 is a schematic diagram of a freehand exercise provided in an embodiment of the present application;

[0029] Figure 2 It is a schematic diagram of an application scenario of a motion monitoring method provided in an embodiment of the present application;

[0030] Figure 3 is a flow chart of a motion monitoring method provided by an embodiment of the present application;

[0031] Figure 4 is a schematic diagram of a time domain signal sequence provided in an embodiment of the present application;

[0032] Figure 5 is a schematic diagram of a time domain signal sequence provided in an embodiment of the present application;

[0033] Figure 6 is a schematic diagram of a type of motion provided by an embodiment of the present application;

[0034] Figure 7 It is a schematic diagram of the number of exercises provided in an embodiment of the present application;

[0035] Figure 8 is a schematic diagram of a statistical result provided in an embodiment of the present application;

[0036] Fig. 9 is a structural schematic diagram of a motion monitoring device provided in an embodiment of the present application;

[0037] Fig.10 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0039] Before explaining in detail the motion monitoring method provided in the embodiment of the present application, the application scenarios involved in the embodiment of the present application are first introduced.

[0040] Please refer to Figure 1 , Figure 1 Various types of freehand exercises are shown, such as squats, lunges, sit-ups, leg curls, crunches, push-ups, etc. Compared with exercises using equipment, freehand exercises do not have too many restrictions and do not take up too much space. They are also relatively convenient to implement, so they are relatively practical. Moreover, the movements are relatively simple and easier to stick to. Currently, freehand exercises can be monitored through wearable devices, cameras, and other methods. However, the method of monitoring exercise with the aid of wearable devices requires wearing wearable devices, which may make the user feel uncomfortable during exercise. The method of monitoring exercise with the aid of a camera to capture the entire exercise process requires obtaining a video of the user's exercise process, which may pose privacy and security issues. The method provided in the embodiment of the present application does not require wearing a wearable device and does not have privacy and security issues. Please refer to Figure 2 The target user 201 only needs to place the electronic device 202 around the target user 201, such as within a range of 0.3 to 0.8 meters in front of the target user 201. The electronic device 202 processes the received electromagnetic wave signal and determines the number and type of movement of the target user 201, thereby achieving the purpose of monitoring the movement of the target user 201.

[0041] The method provided in the embodiment of the present application can be executed by any electronic device that has the function of receiving electromagnetic wave signals in a wireless communication system. For example, the electronic device can be a PC (Personal Computer), a mobile phone, a PDA (Personal Digital Assistant), a PPC (Pocket PC), a tablet computer, a USRP (Universal Software Radio Peripheral), etc.

[0042] It should be noted that the application scenarios and execution entities described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the emergence of new application scenarios and electronic devices, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0043] Next, the motion monitoring method provided in the embodiment of the present application is explained in detail. Figure 3 This is a flow chart of a motion monitoring method provided by an embodiment of the present application. The method is applied to an electronic device, which is located around a target user and has a function of receiving electromagnetic wave signals in a wireless communication system. Figure 3 , the method comprises the following steps.

[0044] Step 301: Acquire a motion signal sequence, where the motion signal sequence is obtained by downsampling a time domain motion signal, where the time domain motion signal is obtained by converting an electromagnetic wave signal received by an electronic device during the movement of a target user.

[0045] The electromagnetic wave signals in the wireless communication system will propagate along multiple paths when blocked by objects. Moreover, for static objects in the environment, the electromagnetic wave signals of the static paths caused by the reflection and diffraction of the static objects are basically unchanged. When there are people or objects in the environment, the propagation path will change continuously, and the electromagnetic wave signals will also change. When the target user is exercising, the electromagnetic wave signals received by the electronic device will change periodically due to the blocking effect of the human body on the wireless signal, and different types of movements will cause different blocking effects, which will cause the spectrum sequence of the electromagnetic wave signals received by the electronic device to be different. Therefore, during the movement of the target user, after the electronic device receives the electromagnetic wave signal, the electromagnetic wave signal can be converted into a time domain motion signal, and then the time domain motion signal is downsampled to obtain a motion signal sequence.

[0046] In some embodiments, the time domain motion signal may be downsampled at a certain frequency to obtain a motion signal sequence. As an example, the time domain motion signal may be downsampled at a reference frequency to obtain a motion signal sequence. The motion signal sequence may be expressed by the following formula (1).

[0047] S(t)=(t i ,A i ) 1≤i≤N (1)

[0048] In the above formula (1), S(t) refers to the motion signal sequence, t n It refers to the time corresponding to the nth sampling point in the motion signal sequence, A n It refers to the amplitude corresponding to the nth sampling point in the motion signal sequence. The amplitude can be the signal strength. N refers to the total number of sampling points in the motion signal sequence.

[0049] The reference frequency is set in advance, for example, the reference frequency may be 100 Hz (Hertz), that is, the time domain motion signal is downsampled at 100 Hz. In different situations, it may be adjusted according to different requirements.

[0050] Step 302: Perform short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows.

[0051] For ease of understanding, the process of performing short-time Fourier transform on the motion signal sequence can be represented by the following formula (2) based on the size and step size of the time window.

[0052] F(j)={STFT[S(t)]} j≥1 j∈N + (j-1)×step≤t<(j-1)×step+M (2)

[0053] In the above formula (2), F(j) refers to the frequency domain signal sequence corresponding to the jth time window, S(t) refers to the motion signal sequence, and N + is a positive integer, M is the size of the time window corresponding to the short-time Fourier transform, step is the step size of the time window sliding, and STFT[S(t)] refers to the short-time Fourier transform of the motion signal sequence S(t).

[0054] Since different types of motion correspond to different frequencies of repeated motion, in order to improve the accuracy of motion signal sequence analysis, when performing short-time Fourier transform, it is necessary to ensure that the size of the time window can cover one repeated motion corresponding to different types of motion. Therefore, in some embodiments, a long-time Fourier transform can be performed on the motion signal sequences corresponding to multiple different types of motion, the repeated motion frequency of each type of motion is determined, the average value of the multiple repeated motion frequencies is determined, the inverse of the average value of the multiple repeated motion frequencies is determined as the average repeated motion period, and the average repeated motion period is multiplied by the reference frequency to obtain the size of the time window corresponding to the short-time Fourier transform, that is, to determine the number of sampling points contained in the time window corresponding to the short-time Fourier transform.

[0055] For example, the motion signal sequences corresponding to push-ups and sit-ups can be subjected to long-time Fourier transform to obtain a repetitive motion frequency of 0.170625 Hz for sit-ups and 0.22 Hz for push-ups. Therefore, the average repetitive motion frequency is 0.1953125 Hz, and the average repetitive motion period is 5.12 seconds. Assuming the reference frequency is 100 Hz, the size of the time window corresponding to the short-time Fourier transform is 100×5.12=512, that is, the number of sampling points contained in each time window of the short-time Fourier transform is 512.

[0056] The step length of the time window sliding is pre-set, for example, the sliding step length can be 8 sampling points, that is, 0.08 seconds. In different situations, it can also be adjusted according to different needs.

[0057] It should be noted that repeated exercise refers to multiple exercises of the same type performed by the target user during one exercise, and the multiple exercises of the same type are repeated exercises. For example, the target user can do multiple sit-ups during one exercise, and the multiple sit-ups are repeated exercises. The subsequent "repeated exercise" can refer to this explanation.

[0058] Step 303: Based on the frequency domain signal sequences corresponding to the multiple time windows, determine a time domain signal sequence having a frequency that is a target frequency, where the target frequency is determined based on the number of sampling points included in the time window.

[0059] Although the frequencies of repeated motions corresponding to different types of motions are different, the frequencies of repeated motions corresponding to most motions are within a certain range, that is, only a time domain signal sequence of a certain frequency needs to be analyzed. Since the frequency domain signal sequences corresponding to the multiple time windows include time domain signal sequences of multiple frequencies, in some embodiments, the reference frequency can be divided by the number of sampling points included in the time window to obtain the target frequency, and then based on the frequency domain signal sequences corresponding to the multiple time windows, a time domain signal sequence with a frequency of the target frequency is determined.

[0060] For example, when the number of sampling points contained in the time window is 512 and the reference frequency is 100 Hz, it can be determined that the target frequency is approximately 0.2 Hz, and then based on the frequency domain signal sequences corresponding to the multiple time windows, a time domain signal sequence with a frequency of 0.2 Hz is determined.

[0061] Step 304: Determine the start time and end time of each repeated motion of the target user in the multiple repeated motions based on the time domain signal sequence having the target frequency.

[0062] In some embodiments, a time domain signal sequence having a target frequency is Gaussian filtered to obtain a time domain signal sequence after Gaussian filtering, a derivative operation is performed on the time domain signal sequence after Gaussian filtering to obtain a time domain signal sequence after derivative operation, and based on the time domain signal sequence after Gaussian filtering and the time domain signal sequence after derivative operation, the start time and end time of each repeated motion of the target user are determined.

[0063] In practical applications, the time domain signal sequence may contain noise due to interference between hardware inside electronic devices, which may affect the accuracy of subsequent determination of the type of movement. Therefore, before determining the start time and end time of each repeated movement of the target user, the time domain signal sequence may be Gaussian filtered, that is, the time domain signal may be denoised.

[0064] At the beginning of a repetitive movement, the human body changes from a static state to a moving state, and the time domain signal sequence will undergo a mutation. Similarly, at the end of a repetitive movement, the time domain signal sequence will also undergo a mutation. Moreover, at the middle moment of a repetitive movement, the time domain signal sequence will reach a peak, that is, each peak in the time domain signal sequence after Gaussian filtering can represent the middle moment of a repetitive movement. However, in order to determine the start time and end time of a repetitive movement, a derivative operation can be performed on the time domain signal sequence after Gaussian filtering, so as to determine the start time and end time of the repetitive movement based on the maximum and minimum values ​​in the time domain signal sequence after the derivative operation.

[0065] As an example, the time corresponding to each peak in the time domain signal sequence after Gaussian filtering can be directly determined. Based on the time corresponding to each peak, the time corresponding to the maximum value adjacent to each peak and the time corresponding to the minimum value are determined from the time domain signal sequence after derivative operation. For any peak, the time corresponding to the maximum value adjacent to the peak is determined as the start time of a repeated movement, and the time corresponding to the minimum value adjacent to the peak is determined as the end time of a repeated movement, so that the start time and end time of each repeated movement of the target user in multiple repeated movements can be determined.

[0066] In practical applications, if there is a device in the environment that interferes with the electromagnetic wave signal or the target user has a long rest time between two repeated movements, it will also cause the time domain signal sequence to generate maximum and minimum values ​​after the derivative operation, but the time corresponding to the maximum and minimum values ​​is not the start time and end time of a repeated movement. Therefore, in some embodiments, the maximum and minimum values ​​in the time domain signal sequence after the derivative operation can be verified to eliminate some erroneous extreme value pairs. That is, if the target maximum value in the time domain signal sequence after the derivative operation meets the maximum value duration condition, the time at which the target maximum value is located is determined as the start time of this repeated movement, and the target maximum value is any maximum value in the time domain signal sequence after the derivative operation. If the target minimum value in the time domain signal sequence after the derivative operation meets the minimum value duration condition and the minimum value amplitude condition, the time at which the target minimum value is located is determined as the end time of this repeated movement, and the target minimum value is the minimum value adjacent to the target maximum value.

[0067] Optionally, if the target maximum value does not satisfy the maximum value duration condition, the time at which the target maximum value is located is not determined as the start time of this repeated exercise.

[0068] It should be noted that the above-mentioned maximum duration condition includes: the duration between the end time of the last repeated movement and the time at which the corresponding maximum value is located is greater than the shortest rest duration. The minimum duration condition includes: the duration between the start time of this repeated movement and the time at which the corresponding minimum value is located is greater than the shortest repeated movement duration. The minimum amplitude condition includes: the average movement amplitude is higher than the average rest amplitude, and the average movement amplitude is the average value of the amplitude corresponding to the start time of this repeated movement and the time at which the corresponding minimum value is located in the time domain signal sequence after Gaussian filtering, and the average rest amplitude is the average value of the amplitude corresponding to the end time of the last repeated movement and the start time of this repeated movement in the time domain signal sequence after Gaussian filtering. Of course, the maximum duration condition, the minimum duration condition and the minimum amplitude condition can also be other conditions, and the embodiments of the present application are not limited to this.

[0069] For a repetitive motion, the most ideal situation is that a maximum value is followed by an adjacent minimum value, and the maximum value satisfies the maximum value duration condition, and the adjacent minimum value after the maximum value satisfies the minimum value duration condition and the minimum value amplitude condition, that is, the maximum value corresponds to the start time of a repetitive motion, and the minimum value corresponds to the end time of a repetitive motion. However, in some cases, there may be multiple maximum values ​​that all meet the maximum value duration condition, or there may be multiple minimum values ​​that all meet the minimum value duration condition and the minimum value amplitude condition, or the adjacent minimum value after the maximum value does not meet the minimum value duration condition or the minimum value amplitude condition. Therefore, the embodiment of the present application can also be processed in the following way.

[0070] When the target maximum value satisfies the maximum value duration condition, that is, the time at which the target maximum value is located is determined as the start time of this repeated movement, if the target minimum value does not satisfy the minimum value duration condition or the minimum value amplitude condition, the next maximum value satisfies the maximum value duration condition, and the next maximum value is greater than the target maximum value, then the time at which the next maximum value is located is updated to the start time of this repeated movement. If the target minimum value does not satisfy the minimum value duration condition or the minimum value amplitude condition, the next maximum value satisfies the maximum value duration condition, and the next maximum value is less than or equal to the target maximum value, then the time at which the next maximum value is located is not updated to the start time of this repeated movement, that is, the time at which the target maximum value is located is still the start time of this repeated movement. If the target minimum value does not meet the minimum value duration condition or the minimum value amplitude condition, and the next maximum value does not meet the maximum value duration condition, it means that the end time of this repeated movement has not been determined, and there is no need to update the start time of this repeated movement. That is, this repeated movement has only one maximum value, so the time of the target maximum value is still the start time of this repeated movement.

[0071] When the target minimum value satisfies the minimum value duration condition and the minimum value amplitude condition, that is, the time at which the target minimum value is located is determined as the end time of this repeated movement, if the next minimum value satisfies the minimum value duration condition and the minimum value amplitude condition, the duration between the next minimum value and the time at which the target minimum value is located is less than the duration threshold, and the next minimum value is less than the target minimum value, then the time at which the next minimum value is located is updated to the end time of this repeated movement. If the next minimum value satisfies the minimum value duration condition and the minimum value amplitude condition, the duration between the next minimum value and the time at which the target minimum value is located is less than the duration threshold, and the next minimum value is greater than or equal to the target minimum value, then the time at which the next minimum value is located is not updated to the end time of this repeated movement, that is, the time at which the target minimum value is located is still the end time of this repeated movement. If the next minimum value satisfies the minimum duration condition and the minimum amplitude condition, and the duration between the next minimum value and the target minimum value is greater than or equal to the duration threshold, it means that the next minimum value is far away from the end time of this repeated movement, and the next minimum value may correspond to the end time of the next repeated movement. In this case, the time of the target minimum value is still the end time of this repeated movement. If the next minimum value does not meet the minimum duration condition or the minimum amplitude condition, it means that the start time and end time of this repeated movement have been determined. In this case, the time of the target minimum value is the end time of this repeated movement.

[0072] For example, see Figure 4 , Figure 4 The black solid line in the middle is the time domain signal sequence after Gaussian filtering, and the black dotted line is the time domain signal sequence after derivative operation. After the derivative operation, the time domain signal sequence after Gaussian filtering obtains the maximum value at point A, the minimum value at point B, and the maximum value at point C. The maximum value at point A is 1.18 and the time at which the maximum value at point A is 13 seconds, the minimum value at point B is 1.17 and the time at which the minimum value at point B is 13.5 seconds, the maximum value at point C is 1.19 and the time at which the maximum value at point C is 14 seconds, the time at which the minimum value at point D is the end time of the last repeated exercise, the time at which the minimum value at point D is 11 seconds, the shortest rest time is 1.5 seconds, and the shortest repeated exercise time is 2.5 seconds.

[0073] Among them, for the maximum value of point A, the duration between the end time of the last repeated exercise and the time of the maximum value of point A is 2 seconds. Since 2 seconds is greater than 1.5 seconds, the duration between the end time of the last repeated exercise and the time of the maximum value of point A is greater than the shortest rest time, that is, the maximum value of point A meets the maximum value duration condition. Therefore, the time of the maximum value of point A is determined as the start time of this repeated exercise.

[0074] Since the time of the maximum value at point A has been determined as the start time of this repeated movement, for the minimum value at point B, the duration between the start time of this repeated movement and the time of the minimum value at point B is 0.5 seconds. Since 0.5 seconds is less than 2.5 seconds, the duration between the start time of this repeated movement and the time of the minimum value at point B is less than the shortest repeated movement duration, that is, the minimum value at point B does not meet the minimum value duration condition, and the time of the minimum value at point B is not the end time of this repeated movement.

[0075] Since the time of the minimum value at point B is not the end time of this repetitive exercise, for the maximum value at point C, the duration between the end time of the last repetitive exercise and the time of the maximum value at point C is 3 seconds. Since 3 seconds is greater than 1.5 seconds, the duration between the end time of the last repetitive exercise and the time of the maximum value at point C is greater than the shortest rest time, that is, the maximum value at point C meets the maximum value duration condition, and the time of the maximum value at point C can also be determined as the start time of this repetitive exercise. However, since the time of the maximum value at point A and the time of the maximum value at point C can both be determined as the start time of this repetitive exercise, in this case, since 1.19 is greater than 1.18, that is, the maximum value at point C is greater than the maximum value at point A, the time of the maximum value at point C is updated to the start time of this repetitive exercise.

[0076] For another example, please refer to Figure 5 , Figure 5 The black solid line in the middle is the time domain signal sequence after Gaussian filtering, and the black dotted line is the time domain signal sequence after derivative operation. After derivative operation, the time domain signal sequence after Gaussian filtering obtains the maximum value at point E, the minimum value at point F, and the maximum value at point H. The maximum value at point E is 1.07 and the time at which the maximum value at point E is 32 seconds, the minimum value at point F is 0.92 and the time at which the minimum value at point F is 34.7 seconds, the maximum value at point H is 1.22 and the time at which the maximum value at point H is 35.5 seconds, the time at which the minimum value at point G is the end time of the last repeated exercise, the time at which the minimum value at point G is 30.4 seconds, the shortest rest time is 1.5 seconds, and the shortest repeated exercise time is 2.5 seconds.

[0077] Among them, for the maximum value of point E, the duration between the end time of the last repeated exercise and the time of the maximum value of point E is 1.6 seconds. Since 1.6 seconds is greater than 1.5 seconds, the duration between the end time of the last repeated exercise and the time of the maximum value of point E is greater than the shortest rest time, that is, the maximum value of point E meets the maximum value duration condition. Therefore, the time of the maximum value of point E is determined as the start time of this repeated exercise.

[0078] Since the time of the maximum value at point E has been determined as the starting time of this repeated motion, for the minimum value at point F, the duration between the starting time of this repeated motion and the time of the minimum value at point F is 2.7 seconds. Since 2.7 seconds is greater than 2.5 seconds, the duration between the starting time of this repeated motion and the time of the minimum value at point F is greater than the shortest repeated motion duration, that is, the minimum value at point F meets the minimum value duration condition.

[0079] Since the start time of this repeated movement corresponds to point E1 in the time domain signal sequence after Gaussian filtering, the amplitude of point E1 is 0.18, the time of the minimum value of point F corresponds to point F1 in the time domain signal sequence after Gaussian filtering, the amplitude of point F1 is 0.165, and the end time of the last repeated movement corresponds to point G1 in the time domain signal sequence after Gaussian filtering, the amplitude of point G1 is 0.215, therefore, the average movement amplitude is the average value of the amplitudes corresponding to points E1 and F1, that is, the average movement amplitude is (0.18-0.165) ÷ 2 = 0.0075. The average rest amplitude is the average value of the amplitudes corresponding to points G1 and E1, that is, the average rest amplitude is (0.215-0.18) ÷ 2 = 0.0175. Since 0.0075 is less than 0.0175, the average exercise amplitude is less than the average rest amplitude, that is, the minimum value of point F does not meet the minimum amplitude condition, and the time of the minimum value of point F is not the end time of this repeated exercise.

[0080] Since the time of the minimum value of point F is not the end time of this repetitive exercise, for the maximum value of point H, the time between the end time of the last repetitive exercise and the time of the maximum value of point H is 5.1 seconds. Since 5.1 seconds is greater than 1.5 seconds, the time between the end time of the last repetitive exercise and the time of the maximum value of point H is greater than the shortest rest time, that is, the maximum value of point H meets the maximum value duration condition, and the time of the maximum value of point H can also be determined as the start time of this repetitive exercise. However, since the time of the maximum value of E and the time of the maximum value of point H can both be determined as the start time of this repetitive exercise, in this case, since 1.22 is greater than 1.07, that is, the maximum value of point H is greater than the maximum value of point E, the time of the maximum value of point H is updated to the start time of this repetitive exercise.

[0081] Step 305: Determine the motion type of each repeated motion based on the motion signal sequence and the start time and end time of the multiple repeated motions.

[0082] For any repeated motion among multiple repeated motions, a signal sequence located between the start time and the end time of the repeated motion is obtained from the motion signal sequence to obtain a signal subsequence corresponding to the repeated motion, and the features of the signal subsequence are extracted using fast Fourier transform and discrete wavelet transform. The features of the signal subsequence are input into the motion classification model to obtain the motion type of the repeated motion.

[0083] The motion classification model may be a support vector machine model or other motion classification models, which is not limited in the embodiments of the present application. Moreover, before the features of the signal subsequence are input into the motion classification model, the initial classification model needs to be trained to obtain the motion classification model. That is, the features of multiple sample signal sequences and the motion type corresponding to each signal sequence are obtained, the features of the signal sequence are used as the input of the initial classification model, the motion type is used as the output of the initial classification model, and the initial classification model is trained.

[0084] In some embodiments, a fast Fourier transform is performed on a signal subsequence to obtain a frequency domain signal subsequence, a plurality of frequency coefficients are obtained from the frequency domain signal subsequence in order from low to high frequency in the frequency domain signal subsequence, a discrete wavelet transform is performed on the signal subsequence to obtain approximate coefficients and a plurality of detailed coefficients, and the plurality of frequency coefficients, the approximate coefficients and the plurality of detailed coefficients are determined as features of the signal subsequence.

[0085] The frequency domain signal subsequence obtained by performing fast Fourier transform on the signal subsequence includes multiple frequencies, and the frequency corresponding to the repeated motion is often lower. Therefore, the lower frequency part of the multiple frequencies can better reflect the characteristics of the signal subsequence. Therefore, multiple frequency coefficients can be obtained from the frequency domain signal subsequence in the order from low to high frequencies in the frequency domain signal subsequence, that is, multiple lower frequencies are obtained from the frequency domain signal subsequence as frequency coefficients.

[0086] For example, by performing a fast Fourier transform on a signal subsequence, a frequency domain signal subsequence can be obtained. According to the order of frequency in the frequency domain signal subsequence from low to high, 5 frequency coefficients can be obtained from the frequency domain signal subsequence. By performing a discrete wavelet transform on the signal subsequence, the signal subsequence can be decomposed into 5 layers, thereby obtaining approximate coefficients and 5 detailed coefficients, and the 5 frequency coefficients, the approximate coefficients and the 5 detailed coefficients are determined as the features of the signal subsequence. That is, the 5 frequency coefficients, the approximate coefficients and the 5 detailed coefficients are composed of an 11-dimensional vector, and the 11-dimensional vector is input into the motion classification model as the feature of the signal subsequence, thereby obtaining the motion type of the repetitive motion corresponding to the signal subsequence.

[0087] The above process can determine the type of each repetitive exercise. In practical applications, the number of repetitive exercises of the target user can also be determined.

[0088] In some embodiments, the number of repeated movements is determined based on the start and end times of multiple repeated movements of the target user, that is, the start and end time of each repeated movement is recorded as one repeated movement, thereby counting the number of repeated movements.

[0089] In other embodiments, the frequency domain signal sequences corresponding to multiple time windows obtained after the motion signal sequence is subjected to short-time Fourier transform are used to determine the number of movements of repeated motion. That is, for the frequency domain signal sequence corresponding to any time window, if the absolute value of the amplitude of the frequency domain signal sequence corresponding to the time window minus the amplitude corresponding to the target frequency is lower than the amplitude threshold, and the absolute value of the amplitude of the frequency domain signal sequence corresponding to the next time window minus the amplitude corresponding to the target frequency is higher than the amplitude threshold, then the number of movements is increased by one. If the absolute value of the amplitude of the frequency domain signal sequence corresponding to the time window minus the amplitude corresponding to the target frequency is lower than the amplitude threshold, and the absolute value of the frequency amplitude of the frequency domain signal sequence corresponding to the next time window minus the amplitude corresponding to the target frequency is also lower than the amplitude threshold, then the number of movements remains unchanged. If the absolute value of the amplitude of the frequency domain signal sequence corresponding to the time window minus the amplitude corresponding to the target frequency is higher than the amplitude threshold, then the number of movements remains unchanged, and the number of movements of repeated motion can be finally obtained.

[0090] In other words, when the absolute value of the amplitude of the frequency domain signal sequence corresponding to the time window minus the amplitude corresponding to the target frequency is lower than the amplitude threshold for the first time, the number of movements is increased by one. If the absolute value of the amplitude of the frequency domain signal sequence corresponding to the multiple adjacent time windows after the time window minus the amplitude corresponding to the target frequency is also lower than the amplitude threshold, it means that the multiple time windows still correspond to the motion signal sequence of the last repeated motion. In this case, the number of movements remains unchanged. Until the absolute value of the amplitude of the frequency domain signal sequence corresponding to a certain time window minus the amplitude corresponding to the target frequency is higher than the amplitude threshold, it means that the time window no longer corresponds to the motion signal sequence of the last repeated motion. Therefore, if the absolute value of the amplitude of the frequency domain signal sequence corresponding to other time windows minus the amplitude corresponding to the target frequency is lower than the amplitude threshold, it means that the time window has slid to the motion signal sequence of the next repeated motion. In this case, the number of movements is increased by one, and the number of movements of the repeated motion can be finally obtained.

[0091] For example, the result of short-time Fourier transform of the motion signal sequence can be plotted into a time-spectrum diagram, in which the horizontal axis represents the time window and the vertical axis represents the frequency. Different colors in the time-spectrum diagram correspond to different amplitudes. It can be seen from the time-spectrum diagram that at the position where the frequency is the target frequency, 10 points with larger amplitudes appear, and the 10 points with larger amplitudes correspond to 10 repeated movements.

[0092] The amplitude threshold is set in advance, for example, the amplitude threshold may be 0.08, and may be adjusted according to different requirements in different situations.

[0093] In the embodiments of the present application, "higher than" can be understood as greater than, or greater than or equal to. When "higher than" is understood as greater than, "lower than" can be understood as less than or equal to. When "higher than" is understood as greater than or equal to, "lower than" can be understood as less than.

[0094] In practical applications, after determining the type of exercise of the target user's repeated exercise and the number of times the target user repeats the exercise, the exercise time and rest time of each repeated exercise can be counted based on the start time and end time of the repeated exercises. That is, for any repeated exercise, the duration between the start time and the end time of the repeated exercise is determined to obtain the exercise time of the repeated exercise, and the duration between the end time of the repeated exercise and the start time of the next repeated exercise is determined to obtain the rest time of the repeated exercise, and finally the exercise time and rest time of the target user's repeated exercises can be obtained, and the statistical results of the exercise time and rest time of the target user's repeated exercises can be determined based on the exercise time and rest time of the repeated exercises, so as to display the exercise type, exercise number, and exercise time and rest time of the repeated exercises.

[0095] In some embodiments, the exercise type of the target user's repeated exercise can be displayed in the form of text or in the form of a picture, which is not limited in this embodiment of the present application.

[0096] For example, see Figure 6 , when it is determined that the target user's exercise type is squat, Figure 6 The sport type is displayed in the

[0097] For example, see Figure 7 , the target user's repeated exercise number is determined to be 10 times through the above method. At this time, Figure 7 The target user's repetition count is displayed in .

[0098] For example, see Figure 8 After determining the exercise time and rest time of each repeated exercise of the target user through the above method, the statistical results can be plotted and Figure 8 The statistical results are displayed in .

[0099] In actual applications, during the target user's exercise process, different types of exercise may be performed alternately, such as sit-ups and crunches. In this case, after the above steps 301 to 305, the number of exercises of the same exercise type in the multiple repeated exercises can be determined based on the exercise type of each repeated exercise, and the exercise type of the multiple repeated exercises and the number of exercises of each exercise type can be displayed.

[0100] For example, after the above steps 301 to 305, the determined exercise types are sit-ups and crunches, and the number of sit-ups is 20 times, and the number of crunches is 15 times.

[0101] The method provided by the embodiment of the present application does not require wearing a wearable device, nor does it bring about privacy and security issues. The target user only needs to place the electronic device around the target user. Based on the electromagnetic wave signal received by the electronic device, the target user's motion signal sequence can be determined. By processing the motion signal sequence, the start time and end time of each repeated motion of the target user in multiple repeated motions can be determined, and then the number of motions and the type of motion of the target user can be determined, so as to achieve the purpose of monitoring the target user's motion. Since different types of motion may be performed alternately during the target user's motion process, the method provided by the embodiment of the present application can not only determine a single motion type, but also determine multiple motion types, and determine the number of motions corresponding to each motion type, so as to achieve the purpose of monitoring multiple motion types.

[0102] Fig. 9 This is a schematic diagram of the structure of a motion monitoring device provided in an embodiment of the present application. The motion monitoring device can be implemented as part or all of an electronic device by software, hardware, or a combination of both. Fig. 9 The device includes: an acquisition module 901, a transformation module 902, a first determination module 903, a second determination module 904 and a third determination module 905.

[0103] The acquisition module 901 is used to acquire a motion signal sequence, which is obtained by downsampling a time domain motion signal, and the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user. The detailed implementation process refers to the corresponding content in the above embodiments, which will not be repeated here.

[0104] The transformation module 902 is used to perform short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows. The detailed implementation process refers to the corresponding content in the above embodiments, which will not be repeated here.

[0105] The first determination module 903 is used to determine a time domain signal sequence whose frequency is a target frequency based on the frequency domain signal sequences corresponding to the multiple time windows, and the target frequency is determined based on the number of sampling points included in the time window. The detailed implementation process refers to the corresponding content in the above embodiments, which will not be repeated here.

[0106] The second determination module 904 is used to determine the start time and end time of each repeated movement of the target user based on the time domain signal sequence with the target frequency. The detailed implementation process refers to the corresponding content of the above embodiments, which will not be repeated here.

[0107] The third determination module 905 is used to determine the motion type of each repeated motion based on the motion signal sequence and the start time and end time of the repeated motions. The detailed implementation process refers to the corresponding content of the above embodiments, which will not be repeated here.

[0108] Optionally, the second determining module 904 includes:

[0109] A Gaussian filtering unit, used for performing Gaussian filtering on the time domain signal sequence to obtain a Gaussian filtered time domain signal sequence;

[0110] A derivative operation unit, used for performing a derivative operation on the time domain signal sequence after Gaussian filtering to obtain a time domain signal sequence after the derivative operation;

[0111] The determination unit is used to determine the start time and the end time of each repeated motion in the multiple repeated motions based on the time domain signal sequence after Gaussian filtering and the time domain signal sequence after derivative operation.

[0112] Optionally, the determining unit is specifically configured to:

[0113] If the target maximum value in the time domain signal sequence after the derivative operation satisfies the maximum value duration condition, the time at which the target maximum value is located is determined as the start time of this repeated movement, and the target maximum value is any maximum value in the time domain signal sequence after the derivative operation;

[0114] If the target minimum value in the time domain signal sequence after the derivative operation meets the minimum value duration condition and the minimum value amplitude condition, the time at which the target minimum value is located is determined as the end time of this repeated motion, and the target minimum value is the minimum value adjacent to the target maximum value.

[0115] Optionally, the determining unit is further specifically configured to:

[0116] If the target minimum value does not meet the minimum value duration condition or the minimum value amplitude condition, the next maximum value meets the maximum value duration condition, and the next maximum value is greater than the target maximum value, then the time of the next maximum value is updated to the start time of this repeated movement.

[0117] Optionally, the determining unit is further specifically configured to:

[0118] If the next minimum value satisfies the minimum value duration condition and the minimum value amplitude condition, the duration between the next minimum value and the target minimum value is less than the duration threshold, and the next minimum value is less than the target minimum value, then the time of the next minimum value is updated to the end time of this repeated movement.

[0119] Optionally, the third determining module 905 includes:

[0120] An acquisition unit, configured to acquire, for any one of the multiple repeated movements, a signal sequence between a start time and an end time of the repeated movement from the motion signal sequence, so as to obtain a signal subsequence corresponding to the repeated movement;

[0121] An extraction unit, used for extracting features of a signal subsequence by using fast Fourier transform and discrete wavelet transform;

[0122] The input unit is used to input the features of the signal subsequence into the motion classification model to obtain the motion type of the repeated motion.

[0123] Optionally, the extraction unit is specifically used for:

[0124] Performing fast Fourier transform on the signal subsequence to obtain a frequency domain signal subsequence;

[0125] Acquire multiple frequency coefficients from the frequency domain signal subsequence in order from low to high frequency in the frequency domain signal subsequence;

[0126] Performing discrete wavelet transform on the signal subsequence to obtain approximate coefficients and multiple detailed coefficients;

[0127] The plurality of frequency coefficients, the approximation coefficients and the plurality of detailed coefficients are determined as characteristics of the signal subsequence.

[0128] Optionally, the device further comprises:

[0129] A fourth determination module is used to determine the number of exercises of the same exercise type in multiple repeated exercises;

[0130] The display module is used for displaying the types of exercise of multiple repeated exercises and the number of exercises of each exercise type.

[0131] The method provided by the embodiment of the present application does not require wearing a wearable device, nor does it bring about privacy and security issues. The target user only needs to place the electronic device around the target user. Based on the electromagnetic wave signal received by the electronic device, the target user's motion signal sequence can be determined. By processing the motion signal sequence, the start time and end time of each repeated motion of the target user in multiple repeated motions can be determined, and then the number of motions and the type of motion of the target user can be determined, so as to achieve the purpose of monitoring the target user's motion. Since different types of motion may be performed alternately during the target user's motion process, the method provided by the embodiment of the present application can not only determine a single motion type, but also determine multiple motion types, and determine the number of motions corresponding to each motion type, so as to achieve the purpose of monitoring multiple motion types.

[0132] It should be noted that: the motion monitoring device provided in the above embodiment only uses the division of the above functional modules as an example when performing motion monitoring. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the motion monitoring device provided in the above embodiment and the motion monitoring method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0133] Fig.10 1 is a block diagram of an electronic device 1000 provided in an embodiment of the present application. The electronic device 1000 may be a portable mobile electronic device, such as a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device 1000 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal, or other names.

[0134] Typically, the electronic device 1000 includes a processor 1001 and a memory 1002 .

[0135] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1001 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0136] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction, which is used to be executed by the processor 1001 to implement the motion monitoring method provided in the method embodiment of the present application.

[0137] In some embodiments, the electronic device 1000 may further optionally include: a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002 and the peripheral device interface 1003 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 1003 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1004, a touch display screen 1005, a camera 1006, an audio circuit 1007, a positioning component 1008 and a power supply 1009.

[0138] The peripheral device interface 1003 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0139] The radio frequency circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 1004 can communicate with other electronic devices through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 1004 may also include circuits related to NFC (Near Field Communication), which is not limited in the embodiments of the present application.

[0140] The display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the display screen 1005. The touch signal can be input to the processor 1001 as a control signal for processing. At this time, the display screen 1005 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 1005 can be one, and the front panel of the electronic device 1000 is set; in other embodiments, the display screen 1005 can be at least two, which are respectively set on different surfaces of the electronic device 1000 or are folded; in some other embodiments, the display screen 1005 can be a flexible display screen, which is set on the curved surface or folded surface of the electronic device 1000. Even, the display screen 1005 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0141] The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the electronic device, and the rear camera is arranged on the back of the electronic device. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize the panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0142] The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 1001 for processing, or input them into the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the electronic device 1000. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0143] The positioning component 1008 is used to locate the current geographic location of the electronic device 1000 to implement navigation or LBS (Location Based Service). The positioning component 1008 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system or Russia's Galileo system.

[0144] The power supply 1009 is used to power various components in the electronic device 1000. The power supply 1009 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1009 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0145] In some embodiments, the electronic device 1000 further includes one or more sensors 1010 , including but not limited to: an acceleration sensor 1011 , a gyroscope sensor 1012 , a pressure sensor 1013 , a fingerprint sensor 1014 , an optical sensor 1015 , and a proximity sensor 1016 .

[0146] The acceleration sensor 1011 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the electronic device 1000. For example, the acceleration sensor 1011 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 1001 can control the touch display screen 1005 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 1011. The acceleration sensor 1011 can also be used for collecting game or user motion data.

[0147] The gyro sensor 1012 can detect the body direction and rotation angle of the electronic device 1000, and the gyro sensor 1012 can cooperate with the acceleration sensor 1011 to collect the user's 3D actions on the electronic device 1000. The processor 1001 can implement the following functions based on the data collected by the gyro sensor 1012: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0148] The pressure sensor 1013 can be set on the side frame of the electronic device 1000 and / or the lower layer of the touch display screen 1005. When the pressure sensor 1013 is set on the side frame of the electronic device 1000, it can detect the user's holding signal of the electronic device 1000, and the processor 1001 performs left and right hand recognition or shortcut operations according to the holding signal collected by the pressure sensor 1013. When the pressure sensor 1013 is set on the lower layer of the touch display screen 1005, the processor 1001 controls the operability controls on the UI interface according to the user's pressure operation on the touch display screen 1005. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0149] The fingerprint sensor 1014 is used to collect the user's fingerprint, and the processor 1001 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 1014, or the fingerprint sensor 1014 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 1001 authorizes the user to perform relevant sensitive operations, which include unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings. The fingerprint sensor 1014 can be set on the front, back, or side of the electronic device 1000. When a physical button or a manufacturer logo is set on the electronic device 1000, the fingerprint sensor 1014 can be integrated with the physical button or the manufacturer logo.

[0150] The optical sensor 1015 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the touch display screen 1005 according to the ambient light intensity collected by the optical sensor 1015. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 1005 is reduced. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 according to the ambient light intensity collected by the optical sensor 1015.

[0151] The proximity sensor 1016, also called a distance sensor, is usually arranged on the front panel of the electronic device 1000. The proximity sensor 1016 is used to collect the distance between the user and the front of the electronic device 1000. In one embodiment, when the proximity sensor 1016 detects that the distance between the user and the front of the electronic device 1000 is gradually decreasing, the processor 1001 controls the touch display screen 1005 to switch from the screen-on state to the screen-off state; when the proximity sensor 1016 detects that the distance between the user and the front of the electronic device 1000 is gradually increasing, the processor 1001 controls the touch display screen 1005 to switch from the screen-off state to the screen-on state.

[0152] Those skilled in the art will understand that Fig.10 The structure shown in the figure does not constitute a limitation on the electronic device 1000, and the electronic device 1000 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0153] In some embodiments, a computer-readable storage medium is also provided, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the motion monitoring method in the above embodiment are implemented. For example, the computer-readable storage medium can be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0154] It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0155] It should be understood that all or part of the steps to implement the above embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-mentioned computer readable storage medium.

[0156] That is, in some embodiments, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the steps of the motion monitoring method described above.

[0157] It should be understood that the "at least one" mentioned herein refers to one or more, and "multiple" refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and the like are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not limit them to be necessarily different.

[0158] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of the present application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the motion signal sequence involved in the embodiments of the present application is obtained with full authorization.

[0159] The above-mentioned embodiments are provided for the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A motion monitoring method, characterized in that: Applied to an electronic device, the electronic device is located around a target user, and the electronic device has a function of receiving electromagnetic wave signals in a wireless communication system, the method comprising: Acquire a motion signal sequence, where the motion signal sequence is obtained by downsampling a time domain motion signal, where the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user; Performing short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows; Based on the frequency domain signal sequences corresponding to the multiple time windows, determine a time domain signal sequence whose frequency is a target frequency, the time domain signal sequence indicates the relationship between time and signal strength, the target frequency is a value obtained by dividing a reference frequency by the number of sampling points included in the time window, the number of sampling points included in the time window is the product of an average repetitive motion period and the reference frequency, and the average repetitive motion period is an average of repetitive motion periods of multiple different types of motions; Performing Gaussian filtering on the time domain signal sequence to obtain a Gaussian filtered time domain signal sequence; Performing a derivative operation on the Gaussian filtered time domain signal sequence to obtain a derivative-operated time domain signal sequence; Determine the start time and end time of each of the multiple repeated movements of the target user based on the time domain signal sequence after the Gaussian filtering and the time domain signal sequence after the derivative operation; For any repeated motion among the multiple repeated motions, a signal sequence located between the start time and the end time of the any repeated motion is acquired from the motion signal sequence to obtain a signal subsequence corresponding to the any repeated motion; Extracting the features of the signal subsequence using fast Fourier transform and discrete wavelet transform; The features of the signal subsequence are input into a motion classification model to obtain the motion type of any repeated motion.

2. The method according to claim 1, characterized in that The step of determining the start time and the end time of each of the multiple repeated movements based on the time domain signal sequence after the Gaussian filtering and the time domain signal sequence after the derivative operation comprises: If the target maximum value in the time domain signal sequence after the derivative operation satisfies the maximum value duration condition, the time at which the target maximum value is located is determined as the start time of this repeated motion, and the target maximum value is any maximum value in the time domain signal sequence after the derivative operation; If the target minimum value in the time domain signal sequence after the derivative operation meets the minimum value duration condition and the minimum value amplitude condition, the time at which the target minimum value is located is determined as the end time of this repeated motion, and the target minimum value is the minimum value adjacent to the target maximum value.

3. The method according to claim 2, characterized in that After determining the time at which the target maximum value is located as the start time of this repeated exercise, the method further includes: If the target minimum value does not satisfy the minimum value duration condition or the minimum value amplitude condition, the next maximum value satisfies the maximum value duration condition, and the next maximum value is greater than the target maximum value, then the time of the next maximum value is updated to the start time of this repeated movement.

4. The method according to claim 2, characterized in that After determining the time at which the target minimum value is located as the end time of this repeated exercise, the method further includes: If the next minimum value satisfies the minimum value duration condition and the minimum value amplitude condition, the duration between the next minimum value and the target minimum value is less than the duration threshold, and the next minimum value is less than the target minimum value, then the time of the next minimum value is updated to the end time of this repeated movement.

5. The method according to any one of claims 2 to 4, characterized in that: The maximum value duration condition includes: the duration between the end time of the last repeated exercise and the time of the corresponding maximum value is greater than the shortest rest time; The minimum duration condition includes: the duration between the start time of the current repeated motion and the time of the corresponding minimum value is greater than the shortest repeated motion duration; The minimum amplitude condition includes: the average movement amplitude is higher than the average rest amplitude, the average movement amplitude is the average value of the amplitudes corresponding to the start time of the current repeated movement and the time of the corresponding minimum value in the time domain signal sequence after Gaussian filtering, and the average rest amplitude is the average value of the amplitudes corresponding to the end time of the previous repeated movement and the start time of the current repeated movement in the time domain signal sequence after Gaussian filtering.

6. The method according to claim 1, characterized in that The method of extracting the features of the signal subsequence by using fast Fourier transform and discrete wavelet transform includes: Performing a fast Fourier transform on the signal subsequence to obtain a frequency domain signal subsequence; Acquire a plurality of frequency coefficients from the frequency domain signal subsequence in order from low to high frequency in the frequency domain signal subsequence; Performing discrete wavelet transform on the signal subsequence to obtain approximate coefficients and multiple detailed coefficients; The plurality of frequency coefficients, the approximation coefficients and the plurality of detailed coefficients are determined as characteristics of the signal subsequence.

7. The method according to claim 1, characterized in that The method further comprises: Determining the number of movements of the same movement type in the plurality of repeated movements; The exercise types of the multiple repetition exercise and the number of exercises of each exercise type are displayed.

8. An electronic device, characterized in that: The electronic device is located around a target user, and the electronic device has a function of receiving electromagnetic wave signals in a wireless communication system, and the electronic device includes a processor, and the processor is used to: Acquire a motion signal sequence, where the motion signal sequence is obtained by downsampling a time domain motion signal, where the time domain motion signal is obtained by converting an electromagnetic wave signal received by the electronic device during the movement of the target user; Performing short-time Fourier transform on the motion signal sequence to obtain frequency domain signal sequences corresponding to multiple time windows; Based on the frequency domain signal sequences corresponding to the multiple time windows, determine a time domain signal sequence whose frequency is a target frequency, the time domain signal sequence indicates the relationship between time and signal strength, the target frequency is a value obtained by dividing a reference frequency by the number of sampling points included in the time window, the number of sampling points included in the time window is the product of an average repetitive motion period and the reference frequency, and the average repetitive motion period is an average of repetitive motion periods of multiple different types of motions; Performing Gaussian filtering on the time domain signal sequence to obtain a Gaussian filtered time domain signal sequence; Performing a derivative operation on the Gaussian filtered time domain signal sequence to obtain a derivative-operated time domain signal sequence; Determine the start time and end time of each of the multiple repeated movements of the target user based on the time domain signal sequence after the Gaussian filtering and the time domain signal sequence after the derivative operation; For any repeated motion among the multiple repeated motions, a signal sequence located between the start time and the end time of the any repeated motion is acquired from the motion signal sequence to obtain a signal subsequence corresponding to the any repeated motion; Extracting the features of the signal subsequence using fast Fourier transform and discrete wavelet transform; The features of the signal subsequence are input into a motion classification model to obtain the motion type of any repeated motion.

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