Data processing method and device, terminal equipment, storage medium and program product

Abstract

The invention discloses a data processing method, which is applied to a first terminal, and comprises the following steps: acquiring gait data of N1 gait detection of a first gait detection sub-period of a target object according to a first time interval, calculating gait parameters corresponding to the N1 gait detection of the first gait detection sub-period according to the gait data of the first gait detection sub-period, judging whether the change trend of the gait parameter corresponding to the first gait detection sub-cycle accords with a first trend or not; if not, the first time interval is adjusted, and the step of collecting the gait data of N1 gait detection of the second gait detection sub-cycle according to the adjusted time interval is executed, the gait data is collected according to the second time interval obtained through final adjustment, and the gait parameter change trend of the Nth gait detection sub-period obtained through calculation conforms to the first trend; and determining the second time interval as a time interval between two adjacent gait detection of N1 gait detection of each gait detection sub-cycle of the target object.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to a data processing method and apparatus, a terminal device, a storage medium, and a program product. Background Art

[0002] With the development of wearable device technology, it can be used to monitor human body status, such as monitoring gait. To achieve gait monitoring, wearable devices are usually required to collect motion data. At present, when using wearable devices for data collection, the collection mode is usually pre-set. For example, the collection mode of the wearable device can be pre-set according to the usage scenario at the factory, or pre-set by the user according to the usage scenario when using it for the first time. However, due to the different personal situations of each person, the data collection mode, such as the data collection time interval, actually needs to vary from person to person, and is not a unified time value. Therefore, the pre-set fixed collection mode cannot accurately cover the monitoring of each user's human body status. Summary of the invention

[0003] To solve the above technical problems, the embodiments of the present application provide a data processing method and apparatus, a terminal device, a storage medium, and a program product.

[0004] In a first aspect, an embodiment of the present application relates to a data processing method, which is applied to a first terminal and includes:

[0005] Gait data of N1 gait detections of the target object in the first step detection sub-cycle are collected at a first time interval, gait parameters corresponding to the N1 gait detections of the first step detection sub-cycle are calculated according to the gait data of the N1 gait detections of the first step detection sub-cycle, and it is determined whether a change trend of the gait parameters corresponding to the first step detection sub-cycle conforms to a first trend; wherein N1 is an integer greater than or equal to 2; and the first time interval is the time interval between two adjacent gait detections of the N1 gait detections of the first step detection sub-cycle;

[0006] In the case of judging that the variation trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, adjusting the first time interval, and executing the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval, until collecting gait data according to the finally adjusted second time interval and calculating that the variation trend of the gait parameters corresponding to the Nth gait detection sub-cycle conforms to the first trend;

[0007] The second time interval is determined as the time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

[0008] In a second aspect, an embodiment of the present application provides a data processing device, applied to a first terminal, including:

[0009] A first processing unit is used to collect gait data of N1 gait detections of the target object in a first step detection sub-cycle at a first time interval, calculate gait parameters corresponding to the N1 gait detections of the first step detection sub-cycle according to the gait data of the N1 gait detections of the first step detection sub-cycle, and determine whether a change trend of the gait parameters corresponding to the first step detection sub-cycle conforms to a first trend; wherein N1 is an integer greater than or equal to 2; and the first time interval is a time interval between two adjacent gait detections of the N1 gait detections of the first step detection sub-cycle;

[0010] a second processing unit, configured to adjust the first time interval, if it is determined that the variation trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, and execute the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval, until the variation trend of the gait parameters corresponding to the Nth gait detection sub-cycle calculated according to the finally adjusted second time interval conforms to the first trend;

[0011] A determining unit is configured to determine the second time interval as a time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

[0012] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a memory and a processor, wherein the memory stores computer executable instructions, and when the processor runs the computer executable instructions on the memory, the method described in the embodiment of the first aspect above can be implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in the embodiment of the first aspect above is implemented.

[0014] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the method described in the embodiment of the first aspect when executed by a processor.

[0015] The technical solution of the embodiment of the present application utilizes the user's preset gait parameter change trend (i.e., the first trend) to adjust the time interval of the user's gait detection, and when the gait parameters corresponding to the gait data collected according to the adjusted time interval meet the preset gait parameter change trend (i.e., the first trend), the adjusted time interval is determined to be a time interval suitable for the user's gait data detection. In this way, when there are differences among each user, the time interval for gait data detection can be determined by adaptive adjustment to obtain a gait detection solution suitable for each person, thereby avoiding the waste of invalid power, time and data collection resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 1 ;

[0017] Figure 2 is a schematic diagram of a hardware sensor;

[0018] Figure 3 A schematic diagram of gait parameter changes;

[0019] Figure 4 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 2 ;

[0020] Figure 5 A schematic diagram of a motion state detection process provided by an embodiment of the present application;

[0021] Figure 6 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 3 ;

[0022] Figure 7 A flow chart of a data collection and processing method provided in an embodiment of the present application;

[0023] Figure 8 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

[0024] Fig. 9 A schematic diagram of the hardware structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0026] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not necessarily need to be defined or explained in the subsequent drawings.

[0027] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0028] Wearable device technology can be used for health monitoring. For example, health smart watches that use photoelectric sensors can monitor blood pressure, pulse, etc. by calculating the periodic laws of indicators such as hemoglobin transmittance through the "oscillometric method". This can be used for early warning of heart disease risks and to assist in home medical observation.

[0029] However, the data collection modes of current wearable devices are usually fixed paradigms set at the factory, one is uninterrupted monitoring and the other is manual wake-up monitoring. The first uninterrupted monitoring solution mentioned above is difficult to support long-term standby use due to power limitations. The second manual wake-up monitoring is prone to miss some danger warnings and some natural monitoring information that requires the user to be in an unintentional state, such as the white coat effect and the observer effect. It cannot present the disease manifestations in a natural state, and the interval time of each collection varies from person to person, and it is difficult to completely fix it in advance.

[0030] Therefore, post-diagnosis management of mobile medical care at home requires a method that can overcome the shortcomings of the above two paradigms at the same time. It can be tailored to different people and businesses, so as not to miss the key information monitored in the natural state, and it can also avoid the power consumption caused by massive data collection and achieve long-term standby.

[0031] Taking Parkinson's disease as an example, long-term monitoring can be used to adjust the dosage of medicine. The brain disease lesions will show movement characteristics such as tremors in the movement state. Its movement symptoms can be monitored by motion sensors:

[0032] (1) After taking the medicine every day, the movement patterns of key gait indicators of physical signs should be monitored 5 times in a qualified exercise posture, with a certain interval between each time (such as 1 hour interval and 2 minutes each measurement. The interval time varies from person to person. Each person's physical condition and disease course have different effects on different drugs. It is necessary to find the cycle length from the "on period" when the drug takes effect to the "off period" when it loses effect. Accurate monitoring 5 times needs to fully cover this cycle).

[0033] (2) Continue for 7 consecutive days to determine whether the changes in key gait indicators between days and between times of each day are abnormal;

[0034] (3) For abnormal data, doctors need not only the periodicity of key gait indicators but also the original movement details for detailed analysis.

[0035] If the interval time or posture status of a certain monitoring is not up to standard, invalid data will be generated, and all data collection for one day will need to be repeated, wasting the precious standby power of the wearable device.

[0036] The long-range detection needs in natural conditions at home require a standby time of more than three months; current wearable devices such as mobile phones and watches can hardly meet the standby needs.

[0037] Wearable devices based on minimalist micro-inertial measurement unit (IMU) sensors on the soles of the feet are suitable for long-term monitoring needs, but in order to achieve a standby time of more than 3 months and meet strict medical paradigm requirements, they also need to intelligently select the content to be collected, stored, and transmitted. The problem lies in:

[0038] (1) Each person's situation is different, and the interval between each collection is also different. How can the intelligent system automatically provide a solution for selection, experimentation and correction to suit each person and avoid wasting invalid time and energy?

[0039] (2) Whether each collection is in accordance with the collection posture, etc., to avoid unqualified collection behavior and waste of power;

[0040] (3) For abnormal data that does not conform to the periodic law, in addition to the calculated indicator parameters, more detailed original data information is required. The periodic law needs to be collected for a certain number of days to determine how to most effectively save resources and power waste;

[0041] The above demands and challenges determine that it is not suitable for doctors to conduct manual real-time monitoring (needs home observation, natural state, to avoid the white coat effect), is not suitable for patients to monitor themselves (high complexity, reduced abilities of the elderly and patients, and manual monitoring makes it difficult to achieve natural postures without deliberate effort), and is difficult to meet with ordinary wearable devices (uninterrupted monitoring cannot meet long-range low-power requirements, and the fixed paradigm makes it easy to miss key information).

[0042] The technical solution of the present application can save the waste of resources and electricity caused by inappropriate single collection of unqualified data, multiple collections of unqualified time, and periodic collection of unqualified rules, thereby improving the long-term standby capability of minimalist wearable devices at home.

[0043] Next, combine Figures 1 to 7 The data processing methods of the embodiments of the present application are introduced in the following embodiments.

[0044] Figure 1 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 1 , applied to a first terminal, comprising:

[0045] S101: collecting gait data of N1 gait detections of a target object in a first gait detection sub-cycle at a first time interval, calculating gait parameters corresponding to the N1 gait detections of the first gait detection sub-cycle according to the gait data of the N1 gait detections of the first gait detection sub-cycle, and determining whether a change trend of the gait parameters corresponding to the first gait detection sub-cycle conforms to a first trend;

[0046] S102: When it is determined that the variation trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, the first time interval is adjusted, and the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval is performed, until the variation trend of the gait parameters corresponding to the Nth gait detection sub-cycle obtained by collecting gait data according to the finally adjusted second time interval and calculating conforms to the first trend;

[0047] S103: Determine the second time interval as the time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

[0048] In the embodiment of the present application, N1 is an integer greater than or equal to 2; the first time interval is the time interval between two adjacent gait detections of the N1 gait detections in the first gait detection sub-cycle.

[0049] For example, a gait detection sub-cycle may be 1 day, and a gait detection sub-cycle requires 5 gait detections, that is, 5 gait detections are required in one day. There is a time interval between the 5 gait detections, and the time interval may be preset to an initial value, such as a first time interval.

[0050] For example, if the target subject takes the medicine once a day, in a qualified exercise state, from time t0 after taking the medicine to a period of time thereafter, such as 12 hours, the entire time period includes the period after the target subject takes the medicine, the period when the drug effect has not yet taken effect, the period when the drug effect is strongest, the period when the drug effect gradually weakens, and the period when the drug effect basically disappears. In different time periods, the impact on the target subject's gait parameters is different, the target subject's gait parameters are performed differently, and the change trend of the target subject's gait parameters in the entire time period should conform to an expected trend.

[0051] In practical applications, gait data can be collected using Figure 2 The hardware sensor implementation shown, Figure 2 This is a picture of a smart insole with an IMU sensor and an IMU chip. The IMU can be used to collect the gait data of the target object. The gait data collected by the IMU is six-axis sensor data, including raw acceleration data and angular velocity data.

[0052] For each gait detection in the first gait detection sub-cycle, when each gait detection time arrives, the target object is reminded to walk, so as to detect the gait data of the target object in the walking state.

[0053] In the embodiment of the present application, the number of categories of gait parameters corresponding to the N1 gait detections in the first gait detection sub-cycle is N2.

[0054] In the embodiment of the present application, gait parameters, which may also be referred to as gait indices, include parameters such as stride length and gait cycle. The types of gait parameters are listed below:

[0055] 1) Duration of unilateral foot swing phase: Among them, T i O represents the moment when the toe leaves the ground at the i-th step, represents the moment when the heel touches down at the i+1th step;

[0056] 2) Unilateral foot support duration: stD i =T i O -T i HS Among them, T i HS represents the moment when the heel touches down at the i-th step, T iO represents the moment when the toe leaves the ground at the i-th step;

[0057] 3) One-sided high footsteps: in, represents the highest vertical position of the foot in the i-th step, and It means the lowest vertical position of the foot in the i-th step;

[0058] 4) Width of one footstep: in, represents the farthest lateral position of the foot in the i-th step, and It represents the most recent lateral position of the foot in the i-th step;

[0059] 5) Length of one footstep: in, represents the farthest position of the foot in the longitudinal direction (front and back) in the i-th step, and It means the most recent longitudinal position of the foot in the i-th step;

[0060] 6) Coefficient of variation of pitch angle velocity during unilateral foot contact period: Among them, hsPv i is the pitch angular velocity of the unilateral foot during the loading phase of the i-th gait cycle; Count is the total number of gait cycles; μ(hsPv) is the mean pitch angular velocity of the unilateral foot during the loading phase; s(hsPv) is the standard deviation of the pitch angular velocity of the unilateral foot during the loading phase;

[0061] 7) Coefficient of variation of angular velocity of pitch angle during unilateral extension phase: Among them, toPv i represents the unilateral foot pitch angular velocity during the extension phase of the i-th gait cycle, μ(toPv) is the mean unilateral foot pitch angular velocity during the extension phase, s(toPv) is the standard deviation of the unilateral foot pitch angular velocity during the extension phase, and Count is the total number of gait cycles;

[0062] 8) Coefficient of variation of unilateral footstep height: Here s(sH) is the standard deviation of the footstep height, μ(sH) is the mean footstep height, and sH i is the height of the i-th step, Count is the number of steps;

[0063] 9) Coefficient of variation of unilateral foot width: Where s(sW) is the standard deviation of the foot swing width, μ(sW) is the mean of the foot swing width, sW is the foot swing width of the i-th step, and Count represents the total number of steps;

[0064] 10) Coefficient of variation of unilateral step length: Among them, sL i It represents the step length of the i-th step, and Count represents the total number of steps walked.

[0065] It can be understood that when judging whether the changing trend of the step parameters corresponding to the first gait detection sub-cycle conforms to the first trend, it can be judged whether the changing trend of some of the gait parameters listed above conforms to the first trend, or it can be judged whether the changing trend of all the gait parameters listed above conforms to the first trend.

[0066] Figure 3 Schematic diagram of the six possible changing trends of gait parameters of the target subject after taking the medicine.

[0067] Figure 3 In the figure, t0~t4 are sampling moments, P0~P4 are gait parameter values ​​at the corresponding sampling moments, and t0 is the data collection moment just after taking the medicine, when the medicine has not yet taken effect. Therefore, P0 can be regarded as the gait index before taking the medicine. Figure 3 The curve of type 1 indicates that the drug inhibits the change of gait parameters, and the curve of type 2 indicates that the drug promotes the change of gait parameters.

[0068] Figure 3 is the change curve of the behavioral standard. The 5 detection times are evenly distributed in the entire drug action cycle, and t2 is the moment when the drug has the strongest effect, and t4 is the moment when the gait parameters recover before taking the medicine. However, in actual measurement, situations 1 and 2 may occur. Situation 1 is because the sampling interval is too short, resulting in the end of the collection before the drug effect disappears. Situation 2 is because the sampling interval is too long, resulting in the basic disappearance of the drug effect at t3. The above two situations will lead to the inability to accurately detect the effect of the drug on gait. Therefore, it is necessary to adjust the detection time interval through the detection time planning algorithm to achieve the effect of the standard detection change curve.

[0069] In some implementations, determining in the above step S101 whether the change trend of the gait parameter corresponding to the first gait detection sub-cycle conforms to the first trend may be achieved by the following steps:

[0070] S1011: calculating the difference ratio and / or change ratio of each type of gait parameter in the N2 type of gait parameters of the first gait detection sub-cycle according to the values ​​of the N2 type of gait parameters corresponding to the N1 gait detections of the first gait detection sub-cycle;

[0071] S1012: Compare the difference ratio of each type of gait parameters in the first step detection sub-cycle with the difference ratio threshold corresponding to each type of gait parameters to obtain a first comparison result; and / or compare the change ratio of each type of gait parameters in the first step detection sub-cycle with the change ratio threshold corresponding to each type of gait parameters to obtain a second comparison result;

[0072] S1013: Determine whether a change trend of each gait parameter corresponding to the first gait detection sub-cycle conforms to a first trend according to the first comparison result and / or the second comparison result.

[0073] Combination Figure 3 In the embodiment of the present application, the difference ratio and change ratio of the gait parameters are introduced to judge whether the change trend of the gait parameters conforms to the first trend. The specific method is as follows:

[0074] σ is the difference ratio, which is used to determine whether the state at time t4 has been restored to the state before taking the medicine, that is, restored to the state at time t0. The difference ratio σ is calculated by the following formula 1:

[0075]

[0076] ∈ is the change ratio, which is used to determine whether the drug effect ends at time t3, that is, the change of gait parameters during t3 to t4 is very small. The change ratio ∈ is calculated by the following formula 2:

[0077]

[0078] Using the difference ratio σ and / or the change ratio ∈ to judge whether the change trend of the gait parameter conforms to the first trend can be achieved using the following conditions ① to ③:

[0079]

[0080] σ threshold is the difference ratio threshold, set to 0.1; ∈ threshold is the change ratio threshold, set to 0.1.

[0081] △T current In the calculation formula, condition ① corresponds to Figure 3 Case 1, that is, the change ratio of the gait parameters is greater than the difference ratio threshold, in this case, the change trend of the gait parameters corresponds to Figure 3 The first case does not conform to the trend of the standard curve; condition ② corresponds to Figure 3 In the second case, the difference ratio of the gait parameters is less than or equal to the difference ratio threshold, and the change ratio of the gait parameters is less than or equal to the change ratio threshold. In this case, it does not conform to the standard curve change trend. The first trend of the embodiment of the present application is Figure 3The standard curve trend in .

[0082] In some implementations, the first time interval may be adjusted in the following manners, including:

[0083] S1021: When the first comparison result is that the difference ratio of each type of gait parameters in the first gait detection sub-period is greater than the difference ratio threshold corresponding to each type of gait parameters, adjusting the first time interval to the first time interval duration plus a set time step;

[0084] S1022: When the first comparison result is that the difference ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the difference ratio threshold corresponding to each type of gait parameters, and the second comparison result is that the change ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the change ratio threshold corresponding to each type of gait parameters, the first time interval is adjusted to the first time interval duration minus the set time step.

[0085] When adjusting the detection time, it can be done according to formula (3), where △T last is the time interval of the last gait detection; △t adapt The time step for adjusting the gait detection time interval each time is set to 0.1h.

[0086] Condition ① corresponds to Figure 3 In case 1, the detection time interval needs to be increased, and condition ② corresponds to Figure 3 In case 2, the detection time interval needs to be reduced. In the above two cases, the difference ratio and change ratio of gait parameters still need to be calculated and judged in the next detection. The last condition ③ corresponds to Figure 3 The standard change curve in the detection time interval does not need to be adjusted, and the calculation and judgment of the difference ratio and change ratio of the gait parameters are no longer performed during subsequent detections.

[0087] The technical solution of the embodiment of the present application utilizes the user's preset gait parameter change trend (i.e., the first trend) to adjust the time interval of the user's gait detection, and when the gait parameters corresponding to the gait data collected according to the adjusted time interval meet the preset gait parameter change trend (i.e., the first trend), the adjusted time interval is determined to be a time interval suitable for the user's gait data detection. In this way, when there are differences among each user, the time interval for gait data detection can be determined by adaptive adjustment to obtain a gait detection solution suitable for each person, thereby avoiding the waste of invalid power, time and data collection resources. Figure 4 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 2 , applied to a first terminal, comprising:

[0088] S401: Adjust the first time interval to obtain a second time interval;

[0089] S402: collecting gait data of a specified period of the target object according to the second time interval;

[0090] S403: Calculating N4 gait parameters corresponding to each gait detection according to the gait data corresponding to each gait detection in the specified period;

[0091] S404: Performing a lateral detection and / or a longitudinal detection on each of the N4 gait parameters corresponding to each gait detection in the specified period, and determining whether there is an abnormality in the gait data corresponding to each gait detection according to the detection result.

[0092] In the embodiment of the present application, the above step S401 is based on the above Figure 1 Steps S101 to S103 in are implemented.

[0093] In the embodiment of the present application, after the second time interval is determined in step S401, gait data of a specified period of the target object is collected according to the second time interval.

[0094] In the embodiment of the present application, the specified period includes N3 gait detection sub-periods, each of the N3 gait detection sub-periods includes N1 gait detections. The second time interval is the time interval between the N1 gait detections included in each gait detection sub-period.

[0095] For example, the designated period may be 7 days, one gait detection sub-period is 1 day, and one gait detection sub-period requires 5 gait detections, that is, 5 gait detections are required in 1 day, and the time interval between each two adjacent gait detections in the 5 gait detections is the second time interval. The time of the first gait detection in each gait detection sub-period is the time when the target subject just takes the medicine.

[0096] For each gait detection in the first gait detection sub-cycle, when each gait detection time arrives, the target object is reminded to walk, so as to detect the gait data of the target object in the walking state.

[0097] In some embodiments, in step S402, collecting the gait data of the target object for a specified period can be achieved by the following steps:

[0098] S4021: for each gait detection in the specified period, after collecting the gait data of the gait detection, using a motion detection algorithm to determine whether the movement behavior of the target object in the gait detection is walking;

[0099] S4022: When it is determined that the movement behavior of the target object in this gait detection is walking, the gait data collected in this gait detection is saved; otherwise, the target object is reminded to perform gait detection again until it is detected that the movement behavior of the target object in this gait detection is walking.

[0100] In actual applications, each gait detection in the specified period collects N5 groups of gait data, and each group of gait data includes data of multiple dimensions.

[0101] For example, after determining that the target subject has taken the medicine, the gait detection reminder module is used to remind the target subject to start gait detection, and at the same time, the data acquisition module is turned on to collect the three-axis acceleration and three-axis angular velocity data output by the IMU in the insole in real time;

[0102] The motion detection algorithm is used to determine in real time whether the patient's movement behavior is walking, ensuring that the collected data is saved and the number of steps is calculated only when the patient is walking, thereby improving the utilization efficiency of storage resources and computing resources.

[0103] In some implementations, in step S404, whether the target object's running behavior is walking may be determined by the following steps:

[0104] S4041: performing data dimension reduction and feature extraction on the first N6 gait data collected for the gait detection, to obtain differential features in the first N6 gait data;

[0105] S4042: Input the differential feature into a motion behavior classification model to obtain a relative position of the differential feature with respect to the hyperplane; and determine whether the motion behavior of the gait detection is walking according to the relative position.

[0106] In the embodiment of the present application, the hyperplane is a hyperplane obtained when the movement behavior classification model is trained and is used to determine whether the running behavior is walking.

[0107] In practical applications, the motion state of the target object can be detected by Figure 5 The method shown is implemented as follows, Figure 5 A schematic diagram of the motion state detection process provided in an embodiment of the present application.

[0108] The first n six-axis sensor data D collected for each gait detection n , first perform data dimension reduction and feature extraction, Feature n =F(D n ), where F is the dimension reduction calculation function, Feature n To obtain the features, the reduced-dimensional features reduce the six-axis sensor data in the original data to one dimension and extract the differentiated features in the original data.

[0109] (1) There are n IMU six-axis data in the database, forming a matrix where x i is the row vector of the i-th row;

[0110] (2) Perform row zero mean, that is: where x ij is the jth element of the row vector of the i-th row, m is the data dimension 6, is the data matrix after row zero mean;

[0111] (3) Calculate the covariance matrix C, Where C is an m×m matrix;

[0112] (4) Calculate the eigenvalues ​​and eigenvectors of C and obtain: λ1,λ2,…,λ m ,c1,c2,…,c m , where λ k is the kth eigenvalue, c k is the corresponding m×1 eigenvector, and the standardized eigenvector is:

[0113] (5) Arrange the eigenvalues ​​from large to small and select the largest eigenvalue λ max , corresponding to the eigenvector

[0114] (6) Multiplying the data matrix X by P can reduce the data to 1 dimension, that is: Feature n =Y=XP, where Y is the n×1 dimension-reduced data.

[0115] (7) Use the SVM binary classification model to determine whether the current state is walking, that is, state = F SVM (Feature n ), where F SVM represents the SVM model calculation function, state is the final output motion discrimination state, SVM is first pre-trained in the data set, using supervised learning, and further maps the data into a high-dimensional space through the Gaussian kernel function φ(·), that is, Among them l i is a landmark. Here, all data are selected as landmarks, that is, i = 1,…,n, δ is a hyperparameter, and landmark l i Determines the center position mapped to the high-dimensional space, δ determines the degree of discreteness from the center position. In the process of continuous iterative adjustment, the optimal hyperplane for data classification is calculated, and the support vector is found near the hyperplane to maximize the interval of the classification boundary. Finally, the classification hyperplane is obtained, which can be represented by w and b. W is the direction parameter of the classification hyperplane, and b is the position parameter.

[0116] (8) During the test, the reduced-dimensional data is input into the SVM model. The kernel function maps the data into a high-dimensional space, and the relative position of the input data in the hyperplane is determined to determine whether the patient's final movement type is walking or other. The calculation formula is: Where x is the data input to SVM, y is the label of the data position, y=1 means above the hyperplane, which is walking, and y=-1 means below the hyperplane, which is other behavior, not walking.

[0117] Depending on the output of the motion detection algorithm, different operations are selected to run.

[0118] (1) If the patient is not walking, the gait detection reminder module reminds the patient to start gait detection again;

[0119] (2) If the patient is walking, start storing gait data and use a peak detection algorithm to calculate the number of steps.

[0120] In the embodiment of the present application, each time gait data is collected, it is determined in real time whether the target object is walking. If the target object is not walking, the target object is reminded to start walking, thereby ensuring that gait data is collected when the target object is walking, thereby avoiding waste of resources caused by collecting unqualified gait data in a single time.

[0121] In an embodiment of the present application, after completing the gait data collection of all gait detection sub-cycles of the specified period, the first terminal calculates the gait parameters corresponding to each gait detection sub-cycle based on the gait data corresponding to each gait detection sub-cycle of the collected specified period, and the calculation of the types of gait parameters here can be one or more.

[0122] In an embodiment of the present application, for each gait parameter, the lateral detection is the detection of the changing trend of the gait parameter in the corresponding gait detection sub-period; the longitudinal detection is the detection of the deviation of the gait parameter relative to the average value of the gait parameter in the corresponding gait detection sub-period.

[0123] Next, the specific detection methods for horizontal detection and vertical detection are introduced.

[0124] Before performing the horizontal detection and / or the vertical detection, the first terminal will first determine whether all gait detections of the specified period have been completed. If not, the target subject will continue to be reminded to take medicine and perform gait detection every day until all gait detections of the specified period are completed. If completed, the horizontal detection algorithm and the vertical detection algorithm are used to detect the gait parameter change law of each gait detection sub-period and the gait parameter deviation value within a gait detection period (i.e., the specified period).

[0125] In some implementations, the step of performing lateral detection on the gait parameters in S404 in the above step is as follows:

[0126] S4043: for each gait detection sub-cycle in the specified cycle, calculating a value of a second-order forward difference of each gait parameter among N4 gait parameters corresponding to the sub-gait detection sub-cycle in the gait detection sub-cycle;

[0127] S4044: When the values ​​of the second-order forward differences of the gait parameters in the gait detection sub-period meet predetermined conditions, it is determined that the gait parameters corresponding to the gait detection sub-period meet the second trend; otherwise, it is determined that the gait data corresponding to the gait detection sub-period is abnormal.

[0128] For horizontal detection, take the detection of changes in gait parameters of Parkinson's patients after taking medication once for five times on the same day as an example. According to clinical observations of Parkinson's patients after taking medication, the regulatory effect of the drug on gait gradually increases within 2 hours after taking the medication, reaches the maximum at about 2 hours, and then gradually weakens. Therefore, there are two trends in the change of the corresponding gait parameters, such as Figure 3 As shown in the standard change curve, the horizontal axis is the time after taking the medicine, and the vertical axis is the value of the gait parameter. P0~P4 represent the values ​​at different times t i The gait parameter values ​​below.

[0129] △f(t i )=f(t i+1 )-f(t i )

[0130] △ 2 f(t j )=△f(t j+1 )-△f(t j )

[0131]

[0132] Among them, f(t j ) is the gait parameter value at the i-th moment; △f(t i ) is the first-order forward difference, i takes values ​​of 0, 1, 2, and 3; 2 f(t j ) is the second-order forward difference, j takes values ​​of 0, 1, and 2; Q is the output result of the lateral detection; Condition ① is that the values ​​of the second-order forward difference are all greater than 0, and the corresponding gait parameter curve is a concave function, that is, corresponding to Figure 3 For the curve of type 1, condition ② is that the values ​​of the second-order forward differences are all less than 0, and the corresponding gait parameter curve is a convex function, that is, Figure 3The curve of type 2, both of which conform to the normal change law; condition ③ does not meet any of the above conditions, at this time, it means that the lateral detection is abnormal. For each gait parameter listed in the embodiment of the present application, the corresponding type of discrimination formula is used to determine whether the gait parameter is abnormal;

[0133] In some implementations, the step of performing longitudinal detection of gait parameters in step S404 in the above step is as follows:

[0134] S4045: For each gait parameter of the N4 gait parameters, calculate the average value of the gait parameter in each gait detection sub-cycle in all gait detection sub-cycles included in the specified period, and determine the maximum average value and the minimum average value of the average values ​​of the gait parameter in all gait detection sub-cycles included in the specified period, and calculate the longitudinal detection threshold of the gait parameter according to the maximum average value and the minimum average value;

[0135] S4046: for the i-th gait detection in the N1 gait detections of each gait detection sub-cycle, calculating a deviation value between a gait parameter corresponding to the i-th gait detection and a corresponding average value of the gait parameter; wherein the value of i ranges from 1 to N1;

[0136] S4047: Compare the deviation value with the longitudinal detection threshold. If the deviation value is greater than the longitudinal detection threshold, determine that the original gait data corresponding to the gait parameter is abnormal; otherwise, determine that the original gait data corresponding to the gait parameter is not abnormal.

[0137] For longitudinal detection, taking the longitudinal detection of gait parameters for five times after taking the drug once in a gait detection cycle as an example, the gait parameter calculated by collecting gait data for the jth time on the i-th day is expressed as P i，j , calculate the mean value of the j-th gait parameter in a detection cycle The calculation formula is: N = 7, and then calculate the gait parameters and mean for the jth time each day Deviation If it is greater than the threshold P j,max and express The maximum and minimum values ​​in , that is, i,j >γ, it is judged that there is an abnormality in the vertical direction.

[0138] In some embodiments, after executing the above step S404, for gait data that is determined to have abnormalities, the first terminal sends the abnormal gait data to the second terminal for the second terminal to analyze based on the abnormal gait data; for gait data that is determined not to have abnormalities, the first terminal sends the gait parameters corresponding to the gait data that is not abnormal to the second terminal.

[0139] In practical applications, different operations are selected according to different judgment results of gait abnormality detection.

[0140] If there is no gait abnormality in both the horizontal and vertical directions, only the processed gait parameters are transmitted to the second terminal without transmitting the original data, which can significantly reduce the amount of data transmission and communication power consumption. Here, the second terminal can be a cloud server or a portable terminal such as a mobile phone.

[0141] If gait abnormality occurs in the horizontal or vertical direction, in addition to transmitting the processed gait parameters to the second terminal, a request is also made to the data acquisition module to transmit the cached raw data of the abnormal part.

[0142] In some embodiments, the second terminal may be a mobile phone, and the data acquisition module of the first terminal may also transmit the gait cache data of the abnormal part to the body domain gateway (mobile phone); after the data request and transmission module of the second terminal receives the gait data of the abnormal part, it transmits the data and gait parameters to the server at the same time.

[0143] In actual applications, the abnormal data analysis module in the server receives and stores data uploaded by the body domain gateway (mobile phone). For abnormal data, it is sent to the doctor for calculation and analysis. The doctor adjusts the medication situation of the target object based on the abnormal analysis, such as adjusting the dosage and time of medication.

[0144] The technical solution of the embodiment of the present application introduces a lateral detection and a longitudinal detection method, and detects abnormal data in the gait data collected each time in a specified period by performing lateral detection and / or longitudinal detection on the gait data collected each time in a user-specified period. The data to be transmitted to the second terminal or the cloud server is determined based on the detection results, including that when there is no abnormality in the detection data, only the processed gait parameters are transmitted, which can avoid the waste of redundant data transmission resources. In the case of abnormalities in the detection data, in addition to the processed gait parameters, the original data corresponding to the abnormal gait parameters are also transmitted for analysis by other users.

[0145] Figure 6 Schematic diagram of the data processing method provided in the embodiment of the present application Figure 3 , applied to a first terminal, comprising:

[0146] S601: adjusting a first time interval for collecting gait data of a target object to obtain a second time interval corresponding to the target object;

[0147] S602: Collecting gait data of the target subject during each medication cycle on designated days according to the second time interval.

[0148] S603: Calculating N4 gait parameters corresponding to each gait detection according to the gait data corresponding to each gait detection in the specified days;

[0149] S604: Performing a lateral detection and / or a longitudinal detection on each of the N4 gait parameters corresponding to each gait detection in the specified days, and determining whether there is an abnormality in the gait data corresponding to each gait detection according to the detection results.

[0150] In actual applications, the target object can be a patient with a certain disease, such as Parkinson's disease. The collection of their gait data can be the collection of gait data for a specified period of time when they take medication. By collecting gait data of patients for a specified period after taking medication, doctors can analyze the patient's medication needs and adjust the patient's medication according to relevant data.

[0151] Since different patients have different medication situations, physical conditions, and drug absorption situations, the patterns of drug effects on different patients may be different. Therefore, it is necessary to adapt a gait detection time for each patient. The above step S601 is the step of adapting a suitable gait detection interval for the patient. In this step, the first time interval is an initial time interval, which may be set by the doctor based on most patients or theoretical time patterns of medication, but may not be suitable for individual patients. Therefore, it is necessary to first adjust the initial time interval so that the second time interval obtained after the adjustment conforms to the gait detection pattern of the individual patient's medication process.

[0152] In the embodiment of the present application, after determining the second time interval corresponding to the individual patient, the gait data of the patient's specified period is collected according to the second time interval. For example, the gait data of the patient's specified period of 7 days is collected in 5 time periods after taking the medicine every day.

[0153] After collecting the gait data of the patient in 5 time periods when taking medicine every day within 7 days, one or more gait parameters of the patient in each detection time period within 7 days are calculated based on the gait data of each time period collected within 7 days.

[0154] Afterwards, the gait parameters calculated in each test period within 7 days are tested horizontally and / or longitudinally to determine whether there are any abnormalities in the data of each gait test of the patient.

[0155] A flowchart of a data collection and processing method provided in an embodiment of the present application. Figure 7 The entire process can achieve the improvement of the adaptive periodic business rules of wearable devices in long-term standby mode at home. Figure 7 The scheme is based on Figure 2 The hardware sensors shown enable the collection of gait data. Figure 7 The entire process relies on data interaction between IMU, smart gateway (usually a mobile phone) and cloud platform.

[0156] The collection and processing of gait data include: the original acceleration and angular velocity data collected by IMU, the calculated gait indicators (stride, gait cycle, etc.), and the set of cyclically changing gait indicators every week, day and time.

[0157] Figure 7 The collection and processing of gait data are carried out through three progressive and regressive cycle steps: single, multiple and periodic, which respectively realize the judgment and collection, storage and transmission of single qualification, the determination and collection, storage and transmission of multiple intervals, and the judgment and collection, storage and transmission of periodic abnormalities.

[0158] first, Figure 7 The first level of discrimination includes the personalized determination of the data collection time interval, and the dynamic adaptive adjustment of the time interval in combination with the user's personalized detection time planning and the change law of gait parameters. The first level mainly includes the following steps 1 to 3.

[0159] Step 1: According to the needs of the patient's condition management, the doctor sets a fixed time period for taking medication during the home rehabilitation period. At the first fixed time period, the mobile phone will start a medication reminder. The patient confirms taking the medication. If the medication has been taken on time, proceed to the next step. If the medication has not been taken on time, repeat this step according to the new reminder time in the reminder rules set by the doctor;

[0160] Step 2: Input the patient's gait parameters from the previous day into the detection time planning algorithm. The changes in gait parameters (e.g., stride length) after taking the medication can be divided into the following categories: Figure 3 Six situations are shown.

[0161] Step 3: When the test time is reached, the medication reminder module reminds the patient to take the medicine in time through a specific sound. After taking the medicine, the patient manually provides feedback on the medication. If there is no feedback within 2 minutes, the medication reminder module will remind the patient to take the medicine again.

[0162] Figure 7 The second level of discrimination includes the real-time status discrimination of each collected gait data, mainly performing real-time discrimination of three conditions, namely, motion status, number of steps and time period, in parallel. If the conditions are met, the third level will be entered.

[0163] Figure 7 The third level of discrimination involves combining periodic rules to realize abnormal data discrimination, mainly through horizontal and vertical detection to determine whether there is an abnormality in the gait data.

[0164] In general, Figure 7 The overall method integrates three-level progressive joint discrimination condition adaptation and three-level data processing. It includes multiple intervals per day to adaptively determine the characteristics of each person and the course of the disease, the joint determination of the movement state time to determine whether the single collection data is qualified, and the judgment of whether the joint law brought by the two laws of convex function and concave function in the 7-day cycle is abnormal. It determines the integration of the three levels of collection, storage, and transmission (raw data, characteristic data, and periodic data), ensuring the use of the most accurate power resources, and improving the long-term standby capability of minimalist wearable devices at home.

[0165] The first judgment level is a method for adaptively determining the interval time multiple times a day based on the characteristics of each person and the course of the disease, and the corresponding method for collecting, storing, transmitting and processing the original data; the second judgment level is a method for determining whether the single collection meets the qualified posture for collection, medication status, etc., and the corresponding method for collecting, storing, transmitting and processing the characteristic data and original data; the third judgment level is a method for determining whether the abnormal data meets the 7-day periodic law, and the corresponding method for collecting, storing, transmitting and processing the characteristic data, original data and periodic data.

[0166] Figure 8 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application, applied to a first terminal, includes:

[0167] The first processing unit 801 is used to collect gait data of N1 gait detections of the target object in the first step detection sub-cycle at a first time interval, calculate the gait parameters corresponding to the N1 gait detections of the first step detection sub-cycle according to the gait data of the N1 gait detections of the first step detection sub-cycle, and determine whether the change trend of the gait parameters corresponding to the first step detection sub-cycle conforms to the first trend; wherein N1 is an integer greater than or equal to 2; the first time interval is the time interval between each adjacent two gait detections of the N1 gait detections of the first step detection sub-cycle;

[0168] The second processing unit 802 is used to adjust the first time interval when it is determined that the change trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, and execute the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval, until the change trend of the gait parameters corresponding to the Nth gait detection sub-cycle calculated according to the finally adjusted second time interval conforms to the first trend;

[0169] The determining unit 803 is configured to determine the second time interval as a time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

[0170] In some implementations, the number of categories of gait parameters corresponding to the N1 gait detections in the first gait detection sub-cycle is N2, and the first processing unit is configured to:

[0171] Calculating the difference ratio and / or change ratio of each type of gait parameter in the N2 type of gait parameters of the first gait detection sub-cycle according to the values ​​of the N2 type of gait parameters corresponding to the N1 gait detections of the first gait detection sub-cycle;

[0172] Compare the difference ratio of each type of gait parameters in the first step detection sub-cycle with the difference ratio threshold corresponding to each type of gait parameters to obtain a first comparison result; and / or compare the change ratio of each type of gait parameters in the first step detection sub-cycle with the change ratio threshold corresponding to each type of gait parameters to obtain a second comparison result;

[0173] It is determined whether the variation trend of each gait parameter corresponding to the first gait detection sub-cycle conforms to the first trend according to the first comparison result and / or the second comparison result.

[0174] In some embodiments, the second processing unit is configured to:

[0175] When the first comparison result is that the difference ratio of each type of gait parameters in the first gait detection sub-period is greater than the difference ratio threshold corresponding to each type of gait parameters, adjusting the first time interval to the first time interval duration plus a set time step;

[0176] When the first comparison result is that the difference ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the difference ratio threshold corresponding to each type of gait parameters, and the second comparison result is that the change ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the change ratio threshold corresponding to each type of gait parameters, the first time interval is adjusted to the first time interval duration minus the set time step.

[0177] In some embodiments, the device further comprises:

[0178] A collection unit, configured to collect gait data of a specified period of the target object at the second time interval; wherein the specified period includes N3 gait detection sub-periods, and each gait detection sub-period of the N3 gait detection sub-periods includes N1 gait detections;

[0179] a calculation unit, configured to calculate N4 gait parameters corresponding to each gait detection according to the gait data corresponding to each gait detection in the specified period;

[0180] a detection unit, configured to perform a lateral detection and / or a longitudinal detection on each of the N4 gait parameters corresponding to each gait detection in the specified period, and determine whether the gait data corresponding to each gait detection is abnormal according to the detection result;

[0181] Among them, for each gait parameter, the lateral detection is the detection of the changing trend of the gait parameter in the corresponding gait detection sub-cycle; the longitudinal detection is the detection of the deviation of the gait parameter relative to the average value of the gait parameter in the corresponding gait detection sub-cycle.

[0182] In some embodiments, the acquisition unit is used to: for each gait detection in the specified period, after collecting the gait data of the gait detection, use a motion detection algorithm to determine whether the movement behavior of the target object in the gait detection is walking; if it is determined that the movement behavior of the target object in the gait detection is walking, save the collected gait data of the gait detection; otherwise, remind the target object to perform gait detection again until it is detected that the movement behavior of the target object in the gait detection is walking.

[0183] In some embodiments, each gait detection in the specified period collects N5 groups of gait data, each group of gait data includes data of multiple dimensions, and the collection unit is used to perform data dimension reduction and feature extraction on the first N6 gait data collected for this gait detection to obtain differentiated features in the first N6 gait data; input the differentiated features into the motion behavior classification model to obtain the relative position of the differentiated features relative to the hyperplane; judge whether the motion behavior of this gait detection is walking according to the relative position; wherein the hyperplane is a hyperplane obtained when training the motion behavior classification model and is used to judge whether the running behavior is walking.

[0184] In some embodiments, the detection unit is used to calculate, for each gait detection sub-cycle in the specified cycle, the value of the second-order forward difference of each gait parameter among the N4 gait parameters corresponding to the sub-gait detection cycle in the gait detection sub-cycle; when the value of the second-order forward difference of each gait parameter in the gait detection sub-cycle meets a predetermined condition, it is judged that the gait parameter corresponding to the gait detection sub-cycle conforms to the second trend; otherwise, it is judged that the gait data corresponding to the gait detection sub-cycle is abnormal.

[0185] In some embodiments, the detection unit is used to:

[0186] For each of the N4 gait parameters, calculate the average value of the gait parameter in each gait detection sub-cycle in all gait detection sub-cycles included in the specified period, and determine the maximum average value and the minimum average value of the average values ​​of the gait parameter in all gait detection sub-cycles included in the specified period, and calculate the longitudinal detection threshold of the gait parameter according to the maximum average value and the minimum average value;

[0187] For the i-th gait detection in the N1 gait detections of each gait detection sub-cycle, calculate the deviation value between the gait parameter corresponding to the i-th gait detection and the corresponding average value of the gait parameter; wherein the value of i ranges from 1 to N1;

[0188] The deviation value is compared with the longitudinal detection threshold. If the deviation value is greater than the longitudinal detection threshold, it is determined that the original gait data corresponding to the gait parameter is abnormal; otherwise, it is determined that the original gait data corresponding to the gait parameter is not abnormal.

[0189] In some embodiments, the device further comprises:

[0190] The sending unit is used to send the abnormal gait data to the second terminal for the second terminal to analyze according to the abnormal gait data; for the gait data that is judged not to be abnormal, the gait parameters corresponding to the gait data without abnormality are sent to the second terminal.

[0191] In some embodiments, each gait detection sub-cycle in the specified cycle corresponds to a gait detection cycle of the target subject after taking medication, and the collection unit is used to collect gait data of the target subject in each medication cycle in the specified days.

[0192] Those skilled in the art should understand that Figure 8 The implementation functions of each unit in the data processing device shown can be understood by referring to the relevant description of the aforementioned data processing method. Figure 8 The functions of each unit in the data processing device shown may be implemented by a program running on a processor, or may be implemented by a specific logic circuit.

[0193] The embodiment of the present application also provides a terminal device. Fig. 9 Schematic diagram of the hardware structure of the terminal device of the embodiment of the present application, such as Fig. 9As shown, the terminal device includes: a communication component 903 for data transmission, at least one processor 901, and a memory 902 for storing a computer program that can be run on the processor 901. The various components in the terminal are coupled together through a bus system 904. It can be understood that the bus system 904 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 904 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig. 9 Various buses are labeled as bus system 904.

[0194] When the processor 901 executes the computer program, at least Figure 1 , Figure 4 or Figure 6 The steps of the method are shown.

[0195] It can be understood that the memory 902 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 902 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0196] The method disclosed in the above embodiment of the present application can be applied to the processor 901, or implemented by the processor 901. The processor 901 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 901 or an instruction in the form of software. The above processor 901 may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 901 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the memory 902, and the processor 901 reads the information in the memory 902 and completes the steps of the above method in combination with its hardware.

[0197] In an exemplary embodiment, the terminal device may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), FPGA, general-purpose processor, controller, MCU, microprocessor, or other electronic components to execute the aforementioned data processing method.

[0198] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the program is at least used to execute when executed by a processor Figure 1 , Figure 4 or Figure 6 The computer readable storage medium may be a memory. The memory may be Fig. 9 Memory 902 is shown.

[0199] The present application also provides a computer program product, including a computer program, which can be Fig. 9 The processor 901 in the terminal device executes to complete the steps of the data processing method described in the first terminal side embodiment.

[0200] The technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed methods and intelligent devices can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0202] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0203] In addition, all functional units in the embodiments of the present application may be integrated into a second processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0204] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that: Applied to a first terminal, the method includes: Gait data of N1 gait detections of the target object in the first step detection sub-cycle are collected at a first time interval, gait parameters corresponding to the N1 gait detections of the first step detection sub-cycle are calculated according to the gait data of the N1 gait detections of the first step detection sub-cycle, and it is determined whether a change trend of the gait parameters corresponding to the first step detection sub-cycle conforms to a first trend; wherein N1 is an integer greater than or equal to 2; and the first time interval is the time interval between two adjacent gait detections of the N1 gait detections of the first step detection sub-cycle; In the case of judging that the variation trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, adjusting the first time interval, and executing the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval, until collecting gait data according to the finally adjusted second time interval and calculating that the variation trend of the gait parameters corresponding to the Nth gait detection sub-cycle conforms to the first trend; The second time interval is determined as the time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

2. The method according to claim 1, characterized in that The number of categories of gait parameters corresponding to the N1 gait detections in the first gait detection sub-cycle is N2; and judging whether the change trend of the gait parameters corresponding to the first gait detection sub-cycle conforms to the first trend includes: Calculating the difference ratio and / or change ratio of each type of gait parameter in the N2 type of gait parameters of the first gait detection sub-cycle according to the values ​​of the N2 type of gait parameters corresponding to the N1 gait detections of the first gait detection sub-cycle; Compare the difference ratio of each type of gait parameters in the first step detection sub-cycle with the difference ratio threshold corresponding to each type of gait parameters to obtain a first comparison result; and / or compare the change ratio of each type of gait parameters in the first step detection sub-cycle with the change ratio threshold corresponding to each type of gait parameters to obtain a second comparison result; It is determined whether the variation trend of each gait parameter corresponding to the first gait detection sub-cycle conforms to the first trend according to the first comparison result and / or the second comparison result.

3. The method according to claim 2, characterized in that When it is determined that the change trend of the gait parameter of the first gait detection sub-cycle does not conform to the first trend, adjusting the first time interval includes: When the first comparison result is that the difference ratio of each type of gait parameters in the first gait detection sub-period is greater than the difference ratio threshold corresponding to each type of gait parameters, adjusting the first time interval to the first time interval duration plus a set time step; When the first comparison result is that the difference ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the difference ratio threshold corresponding to each type of gait parameters, and the second comparison result is that the change ratio of each type of gait parameters of the first gait detection sub-cycle is less than or equal to the change ratio threshold corresponding to each type of gait parameters, the first time interval is adjusted to the first time interval duration minus the set time step.

4. The method according to claim 1, characterized in that: The method further comprises: Collecting gait data of a specified period of the target object according to the second time interval; wherein the specified period includes N3 gait detection sub-periods, and each gait detection sub-period of the N3 gait detection sub-periods includes N1 gait detections; Calculate N4 gait parameters corresponding to each gait detection according to the gait data corresponding to each gait detection in the specified period; Performing a horizontal detection and / or a vertical detection on each of the N4 gait parameters corresponding to each gait detection in the specified period, and judging whether there is an abnormality in the gait data corresponding to each gait detection according to the detection result; Among them, for each gait parameter, the lateral detection is the detection of the changing trend of the gait parameter in the corresponding gait detection sub-cycle; the longitudinal detection is the detection of the deviation of the gait parameter relative to the average value of the gait parameter in the corresponding gait detection sub-cycle.

5. The method according to claim 4, characterized in that The step of collecting gait data of a target object for a specified period includes: For each gait detection in the specified period, after collecting the gait data of the gait detection, using a motion detection algorithm to determine whether the movement behavior of the target object in the gait detection is walking; If it is determined that the movement behavior of the target object in this gait detection is walking, the gait data collected for this gait detection is saved; otherwise, the target object is reminded to perform gait detection again until it is detected that the movement behavior of the target object in this gait detection is walking.

6. The method according to claim 5, characterized in that Each gait detection in the specified period collects N5 groups of gait data, each group of gait data includes data of multiple dimensions; and using the motion detection algorithm to determine whether the motion behavior of the target object in the gait detection is walking includes: Performing data dimension reduction and feature extraction on the first N6 gait data collected for this gait detection to obtain differentiated features in the first N6 gait data; The differential feature is input into the motion behavior classification model to obtain the relative position of the differential feature with respect to the hyperplane; and whether the motion behavior of the gait detection is walking is judged according to the relative position; wherein the hyperplane is a hyperplane obtained when the motion behavior classification model is trained and used to judge whether the running behavior is walking.

7. The method according to claim 4, characterized in that The step of performing a lateral detection on each of the N4 gait parameters corresponding to each gait detection in the specified period, and judging whether the gait data corresponding to each gait detection is abnormal according to the detection result, includes: For each gait detection sub-cycle in the specified cycle, calculate the value of the second-order forward difference of each gait parameter among the N4 gait parameters corresponding to the sub-gait detection cycle in the gait detection sub-cycle; when the value of the second-order forward difference of each gait parameter in the gait detection sub-cycle meets a predetermined condition, judge that the gait parameter corresponding to the gait detection sub-cycle conforms to the second trend; otherwise, judge that the gait data corresponding to the gait detection sub-cycle is abnormal.

8. The method according to claim 4, characterized in that The longitudinal detection of each of the N4 gait parameters corresponding to each gait detection in the specified period, and judging whether the gait data corresponding to each gait detection is abnormal according to the detection result, includes: For each of the N4 gait parameters, calculate the average value of the gait parameter in each gait detection sub-cycle in all gait detection sub-cycles included in the specified period, and determine the maximum average value and the minimum average value of the average values ​​of the gait parameter in all gait detection sub-cycles included in the specified period, and calculate the longitudinal detection threshold of the gait parameter according to the maximum average value and the minimum average value; For the i-th gait detection in the N1 gait detections of each gait detection sub-cycle, calculate the deviation value between the gait parameter corresponding to the i-th gait detection and the corresponding average value of the gait parameter; wherein the value of i ranges from 1 to N1; The deviation value is compared with the longitudinal detection threshold. If the deviation value is greater than the longitudinal detection threshold, it is determined that the original gait data corresponding to the gait parameter is abnormal; otherwise, it is determined that the original gait data corresponding to the gait parameter is not abnormal.

9. The method according to any one of claims 4 to 8, characterized in that The method further comprises: For gait data that is determined to be abnormal, the abnormal gait data is sent to the second terminal, so that the second terminal performs analysis based on the abnormal gait data; For the gait data that is determined not to have abnormality, the gait parameters corresponding to the gait data that is not abnormal are sent to the second terminal.

10. The method according to any one of claims 4 to 8, characterized in that Each gait detection sub-cycle in the specified cycle corresponds to a gait detection cycle of the target subject after taking the medicine; The step of collecting gait data of a target object for a specified period includes: Gait data of the target subject is collected during each medication cycle on the specified days.

11. A data processing device, characterized in that: Applied to a first terminal, the method includes: A first processing unit is used to collect gait data of N1 gait detections of the target object in a first step detection sub-cycle at a first time interval, calculate gait parameters corresponding to the N1 gait detections of the first step detection sub-cycle according to the gait data of the N1 gait detections of the first step detection sub-cycle, and determine whether a change trend of the gait parameters corresponding to the first step detection sub-cycle conforms to a first trend; wherein N1 is an integer greater than or equal to 2; and the first time interval is a time interval between two adjacent gait detections of the N1 gait detections of the first step detection sub-cycle; a second processing unit, configured to adjust the first time interval, if it is determined that the variation trend of the gait parameters of the first gait detection sub-cycle does not conform to the first trend, and execute the step of collecting gait data of N1 gait detections of the next second gait detection sub-cycle after the first gait detection sub-cycle according to the adjusted time interval, until the variation trend of the gait parameters corresponding to the Nth gait detection sub-cycle calculated according to the finally adjusted second time interval conforms to the first trend; A determining unit is configured to determine the second time interval as a time interval between two adjacent gait detections of N1 gait detections in each gait detection sub-cycle of the target object.

12. A terminal device, characterized in that: The terminal device comprises: a memory and a processor, the memory stores computer executable instructions, and the processor can implement any one of claims 1 to 10 when running the computer executable instructions on the memory.

13. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

14. A computer program product comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 10 when executed by a processor.

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