Head motion detection method and device

Abstract

The invention relates to the technical field of motion detection, and particularly discloses a head motion detection method and device, and the method comprises the steps: employing a three-axis acceleration sensor to measure a pitch angle, a roll angle and a course angle in a head motion process, calculating a current attitude value, and further calculating a trend value according to the change of the attitude value, therefore, the posture value and the trend value are combined to detect the head movement condition, the head movement times and the movement amplitude of each time can be easily and visually obtained, and the accuracy of the method is verified through a large number of experiments. The device is provided with an outer sponge and an inner sponge which are adaptive to the size of the head, and a circuit module embedded between the outer sponge and the inner sponge, wherein the circuit module is used for realizing the head movement detection method to detect the head movement. The head movement detection device is simple in structure and compact in installation, the outer sponge and the inner sponge are both of flexible design, comfortable wearing experience is brought to a user, and almost no interference is caused to a testee.

Description

Technical Field

[0001] The present invention relates to the technical field of motion detection, and in particular to a head motion detection method and device. Background Art

[0002] Sleep is very important to people. It is not only an important part of human recovery, integration and memory consolidation, but also directly related to human growth and development, energy accumulation, immune function maintenance and other aspects. According to a survey by the World Health Organization (WHO), 27% of people in the world have sleep problems to varying degrees. This data reveals that sleep disorders have seriously threatened the health of people all over the world. It is very important to effectively monitor people's sleep in their daily lives so that they can understand their own sleep problems and improve their sleep conditions in a timely manner.

[0003] Sleep monitoring systems can be roughly divided into two categories, one is non-contact, such as microwave radar-based monitoring systems, and the other is contact, such as polysomnography (PSG). For microwave radar-based systems, non-sensing monitoring is achieved, but the accuracy is not high due to interference from items such as clothing and quilts on the collected signals and the incompleteness of the collected signals. The PSG method is the "gold standard" for diagnosing sleep disorders. It generally requires subjects to go to a hospital with relevant qualifications to monitor their sleep activities for multiple nights. In addition, the numerous electrodes and complex connections may bring psychological burden and physical discomfort to the subjects, making it difficult for them to fall asleep.

[0004] Among the numerous sleep monitoring systems, systems with good user experience and not too low accuracy, such as systems based on heart rate variability, are often preferred by users. Such systems usually use sensors to detect body movements to assist monitoring, making the definition of sleep and wakefulness more accurate. Depending on the different sensors used, the status of related technologies is introduced from three aspects: monitoring based on microwave technology, flexible force-sensitive sensors, and accelerometers.

[0005] Monitoring based on microwave technology uses the microwave Doppler principle and uses radio frequency biological radar to emit a continuous radio frequency signal, which is received by the receiver after being reflected by the target. The signal with a larger amplitude is separated from the echo signal, which is body movement. In specific applications, the separated body movement signal is pre-processed through wavelet transform, and the energy value and extreme value of the body movement signal are calculated to extract the number of body movements. This type of technology realizes the non-sensing monitoring of the subjects and will not affect their normal sleep, but the collected signals are subject to large interference and low accuracy.

[0006] Monitoring based on flexible force-sensitive sensors uses mattresses as the main carrier, which are divided into capacitive and resistive types. The changes in pressure signals are converted into changes in capacitance or resistance, and then converted into electrical quantities (voltage, current, etc.) through circuit output. Before and after each body movement, the pressure dot matrix image will change accordingly. After image denoising, the frame difference method or image grayscale centroid method is used to analyze the amplitude and frequency characteristics of body movement. According to these characteristics, it is divided into large body movement, medium body movement, small body movement and stillness, and then quantified. The advantages of this type of technology are similar to those based on microwave technology, with almost no interference to the subjects. The disadvantage is that when the subjects turn over frequently, they may not be within the monitoring range of the pressure dot matrix.

[0007] Based on the monitoring of accelerometer, the three-axis acceleration in the MPU6050 sensor is generally used, and the square sum is taken and then squared to form a one-dimensional acceleration value. There are three commonly used methods for measuring body movement. The first is the time threshold method, which records the length of time that the synthetic acceleration is greater than the reference value per unit time; the second is the digital integration method, which records the integral value of the synthetic acceleration over time per unit time, that is, the area under the synthetic acceleration curve; the third is the zero-crossing method, which records the number of times the synthetic acceleration is greater than the reference value (slightly larger than zero) per unit time. The advantage of this type of technology is that it is suitable for wearable applications and has a higher accuracy than the previous two, but the detection results are still not accurate enough if only acceleration is used to directly reflect the number and amplitude of body movement. Summary of the invention

[0008] The present invention provides a head movement detection method and device, which solves the technical problem that the existing body movement detection method based on accelerometer only uses acceleration to directly reflect the number and amplitude of body movement, and the detection result is still not accurate enough.

[0009] In order to solve the above technical problems, the present invention provides a head movement detection method, comprising the steps of:

[0010] S1, obtaining the pitch angle, roll angle and heading angle measured by the three-axis accelerometer fixed on the head at a fixed frequency;

[0011] S2, calculate the current attitude value according to the pitch angle, roll angle and heading angle at the current sampling moment;

[0012] S3, determining the current trend value according to the difference between the current posture value and the previous posture value;

[0013] S4. If the current trend value corresponds to a constant movement trend, the number of times the continuous trend remains unchanged is increased by 1; otherwise, the number of times the continuous trend remains unchanged is reset to zero;

[0014] S5, determine whether the number of times the continuous trend remains unchanged exceeds the continuous trend remains unchanged threshold, if so, go to step S9, otherwise go to the next step;

[0015] S6, calculate the absolute difference between the current trend value and the previous trend value, if the absolute difference is greater than 0, then the absolute difference between the previous posture value and the last posture value of the previous movement is recorded as the amplitude of this movement and go to step S9, otherwise go to the next step;

[0016] S7, calculating the absolute difference between the current posture value and the last posture value of the previous movement, if the absolute difference exceeds the posture absolute difference threshold, proceed to the next step, otherwise go to step S10;

[0017] S8, the number of head movements is accumulated once and the process goes to step S10;

[0018] S9, assign the last posture value to the last posture value of the last movement, assign the number of times the continuous trend remains unchanged to 0, assign the absolute difference between the current posture value and the last posture value of the last movement to 0, and proceed to the next step;

[0019] S10, assigning the current posture value to the previous posture value, assigning the current trend value to the previous trend value, and returning to step S2.

[0020] Furthermore, in step S2, the current attitude value is equal to the square root of the sum of the squares of the pitch angle, the roll angle and the heading angle, and then rounded.

[0021] Further, in step S3, if the difference between the current posture value and the previous posture value is greater than 0, the current trend value trend_cur is the first preset value; if the difference between the current posture value and the previous posture value is less than 0, the current trend value trend_cur is the second preset value; if the difference between the current posture value and the previous posture value is equal to 0, the current trend value trend_cur is the third preset value; the first preset value, the second preset value, and the third preset value are not equal to each other.

[0022] Furthermore, the first preset value is 1, the second preset value is 2, and the third preset value is 0.

[0023] The present invention provides a head movement detection method, which uses a three-axis acceleration sensor to measure the pitch angle, roll angle and heading angle during the head movement process to calculate the current attitude value, and then further calculates the trend value according to the change of the attitude value, so as to combine the attitude value and the trend value to detect the head movement, and it is easy to intuitively obtain the number of head movements and the amplitude of each movement. A large number of experiments have verified the accuracy of the method. In addition, the method does not require high computing power and is easy to be applied in wearable devices.

[0024] The present invention also provides a head movement detection device, the key of which is that it includes an outer sponge and an inner sponge adapted to the size and shape of the head, and a circuit module embedded between the outer sponge and the inner sponge, the circuit module is provided with a main board, and the main board is used to implement the head movement detection method.

[0025] Specifically, the circuit module also includes a battery module, a display and button module, and a magnetic charging port electrically connected to the mainboard, wherein the display and button module and the magnetic charging port are openly embedded in the surface of the outer sponge.

[0026] Specifically, the outer sponge and the inner sponge are configured as flexible structures, which can be bent inwardly into an annular structure adapted to the size of the head.

[0027] Specifically, the tail ends of the outer sponge and the inner sponge are provided with Velcro that cooperate with each other.

[0028] Specifically, the battery module includes a battery, a top frame shell and a bottom frame shell, and the battery is fixed in the top frame shell and the bottom frame shell.

[0029] Specifically, the head movement detection device further includes an upper frame shell, a middle frame shell and a lower frame shell, and the main board and the display and button module are fixed in the upper frame shell, the middle frame shell and the lower frame shell.

[0030] The present invention provides a head movement detection device, which is designed with an outer sponge and an inner sponge adapted to the size of the head, and a circuit module embedded between the outer sponge and the inner sponge. The circuit module is used to implement the above-mentioned head movement detection method to detect head movement, and the number of movements detected, the amplitude of each movement, and the remaining power are displayed on the display screen. The outer sponge and the inner sponge are used to adapt to the size and shape of the head, so that it is easy to wear on the user's head to complete the head movement detection. The head movement detection device has a simple structure and compact installation. The outer sponge and the inner sponge are both flexible designs, which not only bring a comfortable wearing experience to the user, but also have a light weight and can be stably worn on the user's head. No matter how the user moves, the user's head movement can be detected, and there is almost no interference to the subject. In addition, the detection method implemented by the circuit module ensures a high detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of a head movement detection method provided by an embodiment of the present invention;

[0032] Figure 2 These are nine trend diagrams of attitude values ​​provided by embodiments of the present invention;

[0033] Figure 3is a schematic diagram of motion missed detection provided by an embodiment of the present invention;

[0034] Figure 4 is an experimental result diagram provided by an embodiment of the present invention;

[0035] Figure 5 It is a structural diagram of a head movement detection device provided by an embodiment of the present invention.

[0036] Figure markings: 1-outer sponge, 2-inner sponge, 3-main board, 4-battery module, 41-battery, 42-top frame shell, 43-bottom frame shell, 5-display and button module, 6-magnetic charging port, 71-upper frame shell, 72-middle frame shell, 73-lower frame shell. DETAILED DESCRIPTION

[0037] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0038] Example 1

[0039] An embodiment of the present invention provides a head movement detection method, such as Figure 1 As shown in the flowchart, it includes the steps of:

[0040] S1. Obtain the pitch angle (Pitch), roll angle (Roll) and heading angle (Yaw) measured by the three-axis accelerometer fixed on the head at a fixed frequency;

[0041] S2. Calculate the current attitude value (pose_cur) according to the pitch angle (Pitch), roll angle (Roll) and heading angle (Yaw) at the current sampling moment;

[0042] S3. Determine the current trend value (trend_cur) according to the difference between the current pose value (pose_cur) and the previous pose value (pose_pre);

[0043] S4. If the current trend value (trend_cur) corresponds to a constant movement trend, the number of times the continuous trend remains unchanged (trend_unchange) is increased by 1; otherwise, the number of times the continuous trend remains unchanged (trend_unchange) is cleared;

[0044] S5, determine whether the number of consecutive trend unchanged times (trend_unchange) exceeds the consecutive trend unchanged threshold (TREND_THRESHOLD), if so, go to step S9, otherwise go to the next step;

[0045] S6, calculate the absolute difference (diff_trend) between the current trend value (trend_cur) and the previous trend value (trend_pre), if the absolute difference (diff_trend) is greater than 0, then the absolute difference (|pose_pre-pose_last|) between the previous posture value (pose_pre) and the last posture value (pose_last) of the previous movement is recorded as the amplitude of this movement and go to step S9, otherwise go to the next step;

[0046] S7, calculate the absolute difference (diff_pose) between the current posture value (pose_cur) and the last posture value (pose_last) of the previous movement, if the absolute difference (diff_pose) exceeds the posture absolute difference threshold (ACTION_THRESHOLD), proceed to the next step, otherwise go to step S10;

[0047] S8, the number of head movements (total_action) is accumulated by 1 and the process goes to step S10;

[0048] S9, assign the previous posture value (pose_pre) to the last posture value (pose_last) of the last movement, assign the number of times the continuous trend remains unchanged (trend_unchange) to 0, assign the absolute difference (diff_pose) between the current posture value and the last posture value of the last movement to 0, and proceed to the next step;

[0049] S10, assign the current posture value (pose_cur) to the previous posture value (pose_pre), assign the current trend value (trend_cur) to the previous trend value (trend_pre), and return to step S2.

[0050] Each step is described in more detail below.

[0051] Before step S1, the method further includes the following steps:

[0052] S0, initialization: assign the first calculated pose value (pose[0]) to the last pose value (pose_last) and the last pose value (pose_last) of the last movement, assign the last trend value to 0, assign the number of consecutive trend changes (trend_unchange) to 0, assign the number of head movements (total_action) to 0, assign the continuous trend change threshold (TREND_THRESHOLD) to a, and assign the pose absolute difference threshold (ACTION_THRESHOLD) to b. In this example, a = 3, b = 30 sampling cycles are taken, which are determined based on the results of multiple experiments by multiple people to achieve the highest detection accuracy.

[0053] In step S1, the three-axis accelerometer used in this example is the sensor MPU6050. In other implementations, other three-axis accelerometers, such as LIS3DH, may be used.

[0054] In step S2, the current attitude value (pose_cur) is equal to the square root of the sum of the squares of the pitch angle (Pitch), the roll angle (Roll) and the heading angle (Yaw), that is:

[0055]

[0056] Among them, [] indicates taking the integer part. The advantages of rounding are, first, filtering out burrs, making subsequent detection easier, and second, the zero drift problem of the sensor can be ignored. This rounding is reasonable, because head motion detection considers a process, and the decimal part currently ignored will be compensated to a certain extent at the next moment, so it will not affect the detection.

[0057] Each moment can be regarded as the current moment, and the pose value at the corresponding moment is calculated using formula (1). The pose value at the previous moment corresponding to the current pose value (pose_cur) is the previous pose value (pose_pre).

[0058] For the next steps, in order to obtain an effective detection method, it is necessary to analyze the change trend of Pose. Figure 2 , before and after time T, the changes of Pose can be abstracted into 9 situations, divided into 3 categories. The first category corresponds to Figure 2 (a1), (a2) and (a3) ​​in the above table are in an upward trend before time T; the second type corresponds to Figure 2 (b1), (b2) and (b3) in the chart are in a downward trend before time T; the third type corresponds to Figure 2(c1), (c2) and (c3) in the figure are unchanged trends before time T. Through analysis, it is not difficult to draw the following ideas: 1. When the trend does not change before and after time T, no new movement occurs, see (a1), (b1) and (c1); 2. When there is an upward or downward trend after time T, new movement may occur, but the subsequent change range must be further observed, see (a2), (b2), (c2) and (c3); 3. When the trend does not change after time T, the previous possible movement ends, see (a3) ​​and (b3).

[0059] In step S3, through the change trend of the aforementioned Pose, this example obtains the current trend value (trend_cur) according to the difference (diff) between the current pose value (pose_cur) and the previous pose value (pose_pre).

[0060] First, calculate the difference (diff) between the current pose value (pose_cur) and the previous pose value (pose_pre):

[0061] diff = pose_cur - pose_pre (2)

[0062] If diff>0, the current trend value trend_cur=1 (first preset value); if diff<0, the current trend value trend_cur=2 (second preset value); if diff=0, the current trend value trend_cur=0 (third preset value). The values ​​of 1, 2, and 0 of the current trend value trend_cur represent an upward trend, a downward trend, and no change, respectively. In other embodiments, the first preset value, the second preset value, and the third preset value may take other unequal value combinations.

[0063] In step S4, when the trend corresponding to the current trend value trend_cur is upward or downward (ie, trend_cur is not equal to 0), the number of consecutive trend changes trend_unchange is cleared to zero; and when the trend remains unchanged (trend_cur is equal to 0), the number of consecutive trend changes trend_unchange is incremented by 1.

[0064] In step S5, it is determined whether the number of consecutive trend changes trend_unchange exceeds the threshold TREND_THRESHOLD. If so, it goes to step S9, otherwise it goes to the next step. Figure 3 The motion missed detection problem shown in Figure 1 is as follows. Figure 3In (a), the trend is upward before time T1, remains unchanged between T1 and T2, and continues to rise after T2. If the trend remains unchanged between T1 and T2, that is, it is still considered an upward trend, then a new movement after T2 may be considered a continuation of the movement before T1; when the interval between T1 and T2 is long enough, the movement after T2 can be considered new, not just a continuation of the previous movement, and then missed detection will occur. Figure 3 The problems that may occur in (b) are similar.

[0065] In step S6, the absolute difference (diff_trend) between the current trend value (trend_cur) and the previous trend value (trend_pre) is first calculated:

[0066] diff_trend=|trend_cur-trend_pre| (3)

[0067] If diff_trend>0, it indicates that the trend has changed (if the number of head movements has been counted since the last trend change, |pose_pre-pose_last| is recorded as the amplitude of this movement), and the process goes to step S9, otherwise, to the next step.

[0068] In step S7, the absolute difference (diff_pose) between the current pose value (pose_cur) and the last pose value (pose_last) of the previous movement is first calculated:

[0069] diff_pose=|pose_cur-pose_last| (4)

[0070] If diff_pose exceeds the absolute pose difference threshold ACTION_THRESHOLD, proceed to the next step, otherwise go to step S10.

[0071] In step S9, some data and parameters are reset, the previous posture value (pose_pre) is assigned to the last posture value (pose_last) of the previous movement, the number of times the continuous trend remains unchanged (trend_unchange) is assigned to 0, and the absolute difference (diff_pose) between the current posture value and the last posture value of the previous movement is assigned to 0, that is:

[0072] pose_last=pose_pre, trend_unchange=0,diff_pose=0 (5)

[0073] In step S10, the previous value and the previous trend are updated, the current posture value (pose_cur) is assigned to the previous posture value (pose_pre), and the current trend value (trend_cur) is assigned to the previous trend value (trend_pre), that is:

[0074] pose_pre=pose_cur, trend_pre=trend_cur (6)

[0075] Then go to step S2 to enter the next round of detection.

[0076] A large number of experiments were conducted using the above method with a detection cycle of 30 seconds. Figure 4 Some experimental results are listed, including the test results of 9 test cycles from (a) to (i). Figure 4 It can be seen that according to the Pose value, this method can detect the number of head movements in various head movement situations, which is consistent with actual naked eye observation, and the amplitude recorded for each movement is consistent with the actual situation.

[0077] The embodiment of the present invention provides a head movement detection method, which uses a three-axis acceleration sensor to measure the pitch angle, roll angle and heading angle during the head movement process to calculate the current attitude value, and then further calculates the trend value according to the change of the attitude value, so as to combine the attitude value and the trend value to detect the head movement, and it is easy to intuitively obtain the number of head movements and the amplitude of each movement. A large number of experiments have verified the accuracy of the method. In addition, the method does not require high computing power and is easy to be applied in wearable devices.

[0078] Example 2

[0079] In order to apply the above head movement detection method, the present invention provides a head movement detection device, whose structure is as follows: Figure 5 As shown, it includes an outer sponge 1 adapted to the size and shape of the head, an inner sponge 2 and a circuit module embedded between the outer sponge 1 and the inner sponge 2. The circuit module is provided with a main board 3, and the main board 3 is used to implement a head movement detection method in Example 1.

[0080] Specifically, the outer sponge 1 and the inner sponge 2 are configured as flexible structures, which can be bent inwards into a ring structure adapted to the size of the head, so as to be conveniently worn on the head. The tail ends of the outer sponge 1 and the inner sponge 2 are provided with Velcro that cooperate with each other.

[0081] Specifically, Figure 5 As shown, the circuit module also includes a battery module electrically connected to the mainboard 3, a display and button module 5 and a magnetic charging port 6, wherein the display and button module 5 and the magnetic charging port 6 are openly embedded in the surface of the outer sponge 1, so that users can watch the display screen, operate buttons, and charge.

[0082] Specifically, the battery module includes a battery 41 and a top frame shell 42 and a bottom frame shell 43 for fixing and protecting the battery 41 .

[0083] Specifically, in order to fix and protect the mainboard 3 and the display and button module 5 , the head movement detection device further includes an upper frame shell 71 , a middle frame shell 72 and a lower frame shell 73 .

[0084] Specifically, the motherboard 3 is located on the right side of the inner sponge 2. The display and button module 5 is electrically connected to the motherboard 3 and is located on the outside of the outer sponge 1. The battery 41 is located on the left side of the inner sponge 2. The magnetic charging port 6 is located on the right side of the outer sponge 1. The inner sponge 2 and the outer sponge 1 are sealed by wrapping cloth.

[0085] The specific installation steps of a head movement detection device provided in this embodiment are:

[0086] 1) Use screws to fix the mainboard 3 in the lower frame shell 73, and add a little environmentally friendly glue to fix it with the inner sponge 2;

[0087] 2) The display and key module 5 is clamped in the middle frame shell 72 and reinforced on the lower frame shell 73 through positioning columns;

[0088] 3) Install the upper frame shell 71 on the middle frame shell 72;

[0089] 4) Fix the battery 41 in the top frame shell 42 and the bottom frame shell 43, and fix them with a little environmentally friendly glue and sponge;

[0090] 5) Use a little environmentally friendly glue to fix the magnetic charging port 6 on the outer sponge 1;

[0091] 6) Sew the inner sponge 2 and the outer sponge 1 together and seal the edges with a wrapping strip.

[0092] The operation process of the device is as follows:

[0093] 1) Align the concave area of ​​the device with the bridge of the nose and the eyes, and fix it to the user's head with the Velcro on both ends.

[0094] 2) After long pressing the button for 2 seconds to turn on the device, the mainboard 3 obtains the detection signal and obtains the pitch angle, roll angle and heading angle data of the sensor in the most recent recording cycle (30 seconds) at a certain frequency, detects the head movement by the method of Example 1, and displays the number of movements detected and the amplitude of each movement on the OLED screen. At the same time, the mainboard 3 also displays the power data of the coulomb meter on the OLED screen.

[0095] 3) After the test, press and hold the button for 2 seconds to turn off the device.

[0096] 4) During the test, if the power display area is flashing, it is necessary to charge in time. When charging, it is necessary to connect it with the attached magnetic charger, and the charging progress will be displayed on the screen. When charging, the magnetic charger charges the battery 41 and sends a charging signal to the mainboard 3 at the same time. The mainboard 3 obtains the power data of the coulomb meter and displays it on the OLED screen.

[0097] It should also be noted that the present device does not limit the installation method and installation position of the circuit module on the inner sponge 2 and the outer sponge 1, does not limit the structure of the circuit module, and does not limit whether the inner and outer sponges are fixed with Velcro. The head movement detection device suitable for being worn on the head can be designed based on actual needs and limitations.

[0098] The present embodiment provides a head movement detection device, which is designed with an outer sponge 1 adapted to the size of the head, an inner sponge 2, and a circuit module embedded between the outer sponge 1 and the inner sponge 2. The circuit module is used to implement the head movement detection method shown in Example 1 to detect head movement, and the number of movements detected, the amplitude of each movement, and the remaining power are displayed on the display screen. The outer sponge 1 and the inner sponge 2 are used to adapt to the size and shape of the head, so that they are easy to wear on the user's head to complete the head movement detection. The head movement detection device has a simple structure and compact installation. The outer sponge 1 and the inner sponge 2 are both flexible designs, which not only bring a comfortable wearing experience to the user, but also have a light weight and can be stably worn on the user's head. No matter how the user moves, the user's head movement can be detected, and there is almost no interference to the subject. In addition, the detection method implemented by the circuit module ensures a high detection accuracy.

[0099] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A head movement detection method, characterized in that: Includes steps: S1, obtaining the pitch angle, roll angle and heading angle measured by the three-axis accelerometer fixed on the head at a fixed frequency; S2, calculate the current attitude value according to the pitch angle, roll angle and heading angle at the current sampling moment; S3, determining the current trend value according to the difference between the current posture value and the previous posture value; S4. If the current trend value corresponds to a constant movement trend, the number of times the continuous trend remains unchanged is increased by 1; otherwise, the number of times the continuous trend remains unchanged is reset to zero; S5, determine whether the number of times the continuous trend remains unchanged exceeds the continuous trend remains unchanged threshold, if so, go to step S9, otherwise go to the next step; S6, calculate the absolute difference between the current trend value and the previous trend value, if the absolute difference is greater than 0, then the absolute difference between the previous posture value and the last posture value of the previous movement is recorded as the amplitude of this movement and go to step S9, otherwise go to the next step; S7, calculating the absolute difference between the current posture value and the last posture value of the previous movement, if the absolute difference exceeds the posture absolute difference threshold, proceed to the next step, otherwise go to step S10; S8, the number of head movements is accumulated once and the process goes to step S10; S9, assign the last posture value to the last posture value of the last movement, assign the number of times the continuous trend remains unchanged to 0, assign the absolute difference between the current posture value and the last posture value of the last movement to 0, and proceed to the next step; S10, assigning the current posture value to the previous posture value, assigning the current trend value to the previous trend value, and returning to step S2.

2. A head movement detection method according to claim 1, characterized in that: In step S2, the current attitude value is equal to the square root of the sum of the squares of the pitch angle, the roll angle and the heading angle, and then rounded.

3. A head movement detection method according to claim 2, characterized in that: In step S3, if the difference between the current posture value and the previous posture value is greater than 0, the current trend value trend_cur is the first preset value; if the difference between the current posture value and the previous posture value is less than 0, the current trend value trend_cur is the second preset value; if the difference between the current posture value and the previous posture value is equal to 0, the current trend value trend_cur is the third preset value; the first preset value, the second preset value, and the third preset value are not equal to each other.

4. A head movement detection method according to claim 3, characterized in that: The first preset value is 1, the second preset value is 2, and the third preset value is 0.

5. A head movement detection device, characterized in that: The invention comprises an outer sponge (1) adapted to the size and shape of the head, an inner sponge (2), and a circuit module embedded between the outer sponge (1) and the inner sponge (2), wherein the circuit module is provided with a main board (3), and the main board (3) is used to implement a head movement detection method as claimed in any one of claims 1 to 4.

6. A head movement detection device according to claim 5, characterized in that: The circuit module also includes a battery module (4) electrically connected to the main board (3), a display and button module (5) and a magnetic charging port (6), wherein the display and button module (5) and the magnetic charging port (6) are open-embedded on the surface of the outer sponge (1).

7. The head movement detection device according to claim 5, characterized in that: The outer sponge (1) and the inner sponge (2) are configured as flexible structures, and can be bent inwards into an annular structure that fits the size of the head.

8. A head movement detection device according to claim 7, characterized in that: The tail ends of the outer sponge (1) and the inner sponge (2) are provided with Velcro strips that fit in with each other.

9. The head movement detection device according to claim 5, characterized in that: The battery module (4) comprises a battery (41), a top frame shell (42) and a bottom frame shell (43), and the battery (41) is fixed in the top frame shell (42) and the bottom frame shell (43).

10. The head movement detection device according to claim 5, characterized in that: The head movement detection device further comprises an upper frame shell (71), a middle frame shell (72) and a lower frame shell (73), and the main board (3) and the display and key module (5) are fixed in the upper frame shell (71), the middle frame shell (72) and the lower frame shell (73).

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