CN115530810A - Method for monitoring scoliosis, intelligent backpack and storage medium - Google Patents
Method for monitoring scoliosis, intelligent backpack and storage medium Download PDFInfo
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- CN115530810A CN115530810A CN202211185342.6A CN202211185342A CN115530810A CN 115530810 A CN115530810 A CN 115530810A CN 202211185342 A CN202211185342 A CN 202211185342A CN 115530810 A CN115530810 A CN 115530810A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
- A61B5/1122—Determining geometric values, e.g. centre of rotation or angular range of movement of movement trajectories
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
Abstract
The embodiment of the application relates to the technical field of intelligent wearing, and discloses a method for monitoring scoliosis, an intelligent backpack and a storage medium. And calculating the current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence (the current angle difference value is used for indicating the angle difference of the left inclination angle and the right inclination angle of the user). If the current angle difference value is larger than or equal to the first threshold value, the difference of the left swing amplitude and the right swing amplitude is large in the walking process, high shoulders and low shoulders exist, scoliosis occurs, the risk of scoliosis is prompted, and the posture of a user can be corrected in time.
Description
Technical Field
The embodiment of the application relates to the technical field of intelligent wearing, in particular to a method for monitoring scoliosis, an intelligent backpack and a storage medium.
Background
Scoliosis is the bending of one or more segments of vertebral bodies of the spine in the coronal plane deviating from the midline of the body to the lateral direction, which is accompanied by the rotation of the vertebral bodies, the increase or decrease of the kyphosis or the lordosis in the sagittal plane, the rotation inclination deformity of ribs and pelvis and the paravertebral ligament muscle abnormity, and belongs to the three-dimensional structural deformity of the spine. The scoliosis often occurs in school age teenager groups, on one hand, insufficient physical exercise can cause insufficient muscle strength on two sides of the normal physiological bending of the fixed spine, on the other hand, the groups need to carry a schoolbag with heavy weight, and the abnormal spine bending can be easily caused by unbalanced tensity of muscles on two sides caused by improper carrying method, standing posture and walking posture of the backpack.
Some methods and devices for monitoring scoliosis, which are known by the inventor of the present application, have unstable monitoring due to hardware defects or software defects, and thus the accuracy of the monitoring result is poor.
Disclosure of Invention
In view of this, the embodiment of the present application provides a method for monitoring scoliosis, an intelligent backpack, and a storage medium, which can accurately monitor scoliosis conditions and prompt scoliosis risks, so that scoliosis can be effectively prevented.
In a first aspect, the present application provides a method for monitoring scoliosis, which is applied to an intelligent backpack, and the method includes:
acquiring an angle time sequence generated when the intelligent backpack is used at present, wherein the angle time sequence is time sequence data of angles arranged according to the generated time sequence, and the angles reflect the inclination angle of the intelligent backpack in the use process;
identifying single step periods according to the periodicity of the angle time sequence, obtaining the maximum left inclination angle corresponding to each left single step to form a maximum left inclination angle sequence, and forming a maximum right inclination angle sequence according to the maximum right inclination angle corresponding to each right single step;
calculating a current angle difference value according to the maximum left dip angle sequence and the maximum right dip angle sequence, wherein the current angle difference value is used for indicating the angle difference between the left dip angle and the right dip angle of the user;
and if the current angle difference value is larger than or equal to the first threshold value, prompting the scoliosis risk.
In some embodiments, the calculating the current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence comprises:
deleting invalid data in the maximum left-leaning angle sequence to obtain an effective left-leaning angle sequence, and calculating the mean value of the left-leaning angles in the effective left-leaning angle sequence to obtain a left-leaning average angle;
deleting invalid data in the maximum right dip angle sequence to obtain an effective right dip angle sequence, and calculating the average value of right dip angles in the effective right dip angle sequence to obtain a right dip average angle;
determining the current angle difference as the difference between the left-leaning average angle and the right-leaning average angle.
In some embodiments, the foregoing deleting invalid data from the sequence of maximum left-leaning angles to obtain a sequence of valid left-leaning angles includes:
calculating a first mean square error of each maximum left inclination angle in the maximum left inclination angle sequence;
traversing the maximum left inclination angle sequence, if the difference value between a certain maximum left inclination angle and the first mean square error is larger than or equal to a second threshold value, removing the maximum left inclination angle from the maximum left inclination angle sequence, and obtaining an effective left inclination angle sequence after the traversal of the maximum left inclination angle sequence is completed;
deleting invalid data in the maximum right inclination angle sequence to obtain an effective right inclination angle sequence, wherein the effective right inclination angle sequence comprises the following steps:
calculating a second mean square error of each maximum right inclination angle in the maximum right inclination angle sequence;
and traversing the maximum right dip angle sequence, if the difference value between a certain maximum right dip angle and the first mean square error is larger than or equal to a second threshold value, removing the maximum right dip angle from the maximum right dip angle sequence, and obtaining an effective right dip angle sequence after the maximum right dip angle sequence is traversed.
In some embodiments, the method further comprises:
determining a current inclination trend according to the current angle difference and the plurality of historical angle differences;
and prompting the worsening condition of the scoliosis trend according to the trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time.
In some embodiments, the determining the current inclination trend according to the current angle difference value and a plurality of historical angle difference values comprises:
eliminating invalid data in the current angle difference value and the plurality of historical angle difference values to obtain a plurality of valid angle difference values;
and performing linear fitting on the effective angle difference values, acquiring the slope of a linear function obtained by fitting, and taking the slope as the current inclination trend.
In some embodiments, the foregoing removing invalid data from the current angle difference value and the multiple historical angle difference values to obtain multiple valid angle difference values includes:
calculating a third mean square error of the current angle difference value and a plurality of historical angle difference values;
and traversing the current angle difference value and the plurality of historical angle difference values, removing the angle difference value from the current angle difference value and the plurality of historical angle difference values if the difference between a certain angle difference value and the third mean square error is larger than or equal to a third threshold value, and obtaining a plurality of effective angle difference values after traversing.
In some embodiments, the aforementioned prompting of worsening scoliosis trend based on trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time includes:
if the trend difference is within the preset range, indicating that the scoliosis trend is not worsened;
and if the trend difference is not within the preset range, prompting that the scoliosis trend is worsened.
In some embodiments, the method further comprises:
determining the change rate of the inclination trend according to the current inclination trend and a plurality of historical inclination trends;
if the change rate is greater than or equal to a fourth threshold value, prompting the scoliosis risk;
and if the change rate is smaller than a fourth threshold value, prompting that good posture habit is kept.
In a second aspect, an embodiment of the present application provides an intelligent backpack, including:
the backpack body is arranged on the inertia measuring unit and the timing module of the backpack body; the intelligent backpack comprises a backpack body, an inertial measurement unit, a timing module and a control module, wherein the inertial measurement unit is positioned on a central axis of the backpack body and used for acquiring an inclination angle of the intelligent backpack, and the timing module is used for acquiring real-time;
the at least one processor is in communication connection with the inertial measurement unit and the timing module respectively so as to obtain an angle time sequence generated when the intelligent backpack is used currently;
a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
In a third aspect, a computer-readable storage medium is provided in an embodiment of the present application, where the computer-readable storage medium stores computer-executable instructions, and when executed by an intelligent backpack, the computer-executable instructions cause the intelligent backpack to perform the method of the first aspect.
The beneficial effects of the embodiment of the application are as follows: different from the situation of the prior art, the method for monitoring scoliosis provided by the embodiment of the application is applied to the intelligent backpack, the method acquires the angle time sequence by acquiring the inclination angle of the intelligent backpack in the using process in real time, and the inclination angle of the intelligent backpack can reflect the inclination angle of the human body (spine) based on the intelligent backpack bearing the human body in the using process, so that the angle time sequence can reflect the real-time inclination angle of the human body (spine) in the walking process. The human trunk swings left and right periodically in the walking process, the angle time sequence is also periodic, and therefore, the single step period can be identified according to the periodicity of the angle time sequence, namely, the left step and the right step are identified, the maximum left inclination angle in each left step period is captured to form the maximum left inclination angle sequence, and the maximum right inclination angle in each right step period is captured to form the maximum right inclination angle sequence. And calculating the current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence (the current angle difference value is used for indicating the angle difference of the left inclination angle and the right inclination angle of the user). If the current angle difference value is larger than or equal to the first threshold value, the difference of the left swing amplitude and the right swing amplitude is large in the walking process, high shoulders and low shoulders exist, scoliosis occurs, the risk of scoliosis is prompted, and the posture of a user can be corrected in time. In this embodiment, adopt above-mentioned mode, the difference of amplitude of oscillation about using intelligent knapsack walking period monitoring human trunk to confirm the height shoulder, can accurately monitor the scoliosis condition, suggestion scoliosis risk, thereby, can effectively prevent the scoliosis.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings which correspond to and are not to be construed as limiting the embodiments, in which elements having the same reference numeral designations represent like elements throughout, and in which the drawings are not to be construed as limiting in scale unless otherwise specified.
FIG. 1 is a schematic illustration of a normal spine and scoliosis in some embodiments of the present application;
FIG. 2 is a schematic diagram of the structure of an intelligent backpack in some embodiments of the present application;
FIG. 3 is a schematic view of the backpack body relative to a person in some embodiments of the present application;
FIG. 4 is a schematic illustration of the inclination of the backpack body during walking of a person in some embodiments of the present application;
FIG. 5 is a schematic flow chart of a method of monitoring scoliosis in some embodiments of the present application;
FIG. 6 is a schematic illustration of a time series of angles in some embodiments of the present application;
FIG. 7 is a graph illustrating a linear fit of a plurality of effective angular differences according to some embodiments of the present disclosure.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will aid those skilled in the art in further understanding the present application, but are not intended to limit the present application in any way. It should be noted that numerous variations and modifications could be made by those skilled in the art without departing from the spirit of the application. All falling within the scope of protection of the present application.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that, if not conflicted, the various features of the embodiments of the present application may be combined with each other within the scope of protection of the present application. Additionally, while functional block divisions are performed in device schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in a different order than the block divisions in devices, or in flowcharts. Further, the terms "first," "second," "third," and the like, as used herein do not limit the order of data and execution, but merely distinguish between identical or similar items that have substantially the same function or effect.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In addition, the technical features mentioned in the embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, fig. 1 is a schematic view of a normal spine and scoliosis. Scoliosis is the bending of one or more segments of vertebral bodies of the spine in the coronal plane deviating from the midline of the body to the lateral direction, which is accompanied by the rotation of the vertebral bodies, the increase or decrease of the kyphosis or the lordosis in the sagittal plane, the rotation inclination deformity of ribs and pelvis and the paravertebral ligament muscle abnormity, and belongs to the three-dimensional structural deformity of the spine. Scoliosis includes C-curvature, S-curvature, etc., and FIG. 1 is merely illustrative of a C-curvature and does not limit the type of scoliosis in any way. It is known that scoliosis can cause high and low shoulders and affect the posture.
The scoliosis is mostly generated in the group of teenagers of school age, the group needs to carry a schoolbag with heavy weight, the tension of muscles at two sides is unbalanced due to improper carrying method of the backpack and improper standing and walking postures, and abnormal scoliosis is easily caused. The scoliosis of the teenagers can affect the shape and the function of the spine and also affect the physiological health, and causes symptoms such as low back pain, nerve damage, dyspepsia, abnormal heartbeat, limb somatosensory disorder and the like. Continuously pay attention to the development condition of the vertebral column of the teenager, supervise and urge students to keep good standing and walking postures during weight bearing, is favorable for preventing postural scoliosis (scoliosis), finds abnormal posture and change trend of the trunk in time, and is favorable for intervention and recovery of the scoliosis.
However, the abnormal appearance caused by the early scoliosis is not obvious and is not easy to be found, especially when the clothes are worn, and the scoliosis of most teenagers is found by parents when bathing children or when the clothes are worn less frequently.
Before describing the embodiments of the present application, a brief description of the method for monitoring scoliosis, which is known to the inventors of the present application, is provided to facilitate understanding of the embodiments of the present application.
In some schemes, the method and system for monitoring and preventing human spine curvature deformation based on intelligent backpack, wherein the system comprises the intelligent backpack, the intelligent backpack comprises a backpack body, a first strap and a second strap, a first pressure sensor is arranged on the first strap, a second pressure sensor is arranged on the second strap, the first pressure sensor and the second pressure sensor are electrically connected with a processor, the processor is electrically connected with an alarm module, and the processor further comprises: the operation module is used for subtracting the absolute value of a second pressure value obtained by the second pressure sensor from a first pressure value obtained by the first pressure sensor and dividing the absolute value by the smaller value of the first pressure value and the second pressure value to obtain a percentage number; and the comparison module is used for alarming if the obtained percentage number is larger than a preset percentage threshold value.
According to the scheme, the stress balance of the two shoulders is monitored by the pressure sensors in the two shoulder belts, the pressure sensors are easily damaged due to bending and stress pulling of the shoulder belts, so that the durability is influenced, the mechanical deformation of the shoulder belts also interferes the pressure sensors to influence the accuracy of original data, and therefore the accuracy of a calculation result is influenced. Furthermore, single measurements are sporadic, resulting in lower accuracy.
In some schemes, a monocular camera is arranged on a first face outside the schoolbag (the first face is an opposite face of a face attached to the back), pavement information can be recorded by the monocular camera, a vertical line is marked on the first face outside the schoolbag, and an embedded system is installed inside the schoolbag. The monocular camera shoots road surface information, the embedded system obtains a horizontal line based on the road surface according to a vertical line, obtains a horizontal line included angle according to a vertical line and a horizontal line, sets a preset accumulation duration, for example, 10 minutes, generates a horizontal line included angle accumulated value by the horizontal line included angle obtained through accumulation, one direction is positive, the other direction is negative, and if the horizontal line included angle accumulated value is larger than a first threshold value, an alarm is given.
In the scheme, a monocular camera is adopted, so that the design is complex, the cost is high, and the power consumption is high; and the dependence on the environment is high, and the imaging quality of the monocular camera can be directly influenced in rainy days, haze days and other weather conditions, namely the accuracy of the original data is influenced, so that the accuracy of the calculation result is influenced. Furthermore, single measurements are sporadic, resulting in lower accuracy.
In order to solve the problems, the embodiment of the application provides a method for monitoring scoliosis, which is applied to an intelligent backpack, the method acquires an angle time sequence by acquiring the inclination angle of the intelligent backpack in the using process in real time, and the inclination angle of the intelligent backpack can reflect the inclination angle of the human body (spine) based on the intelligent backpack bearing the human body in the using process, so that the angle time sequence can reflect the real-time inclination angle of the human body (spine) in the walking process. The human trunk swings left and right periodically in the walking process, the angle time sequence is also periodic, and therefore, the single step period can be identified according to the periodicity of the angle time sequence, namely, the left step and the right step are identified, the maximum left inclination angle in each left step period is captured to form the maximum left inclination angle sequence, and the maximum right inclination angle in each right step period is captured to form the maximum right inclination angle sequence. And calculating the current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence (the current angle difference value is used for indicating the angle difference of the left inclination angle and the right inclination angle of the user). If the current angle difference value is larger than or equal to the first threshold value, the difference of the left swing amplitude and the right swing amplitude is large in the walking process, high shoulders and low shoulders exist, scoliosis occurs, the risk of scoliosis is prompted, and the posture of a user can be corrected in time. In this embodiment, adopt above-mentioned mode, the difference of amplitude of oscillation about using intelligent knapsack walking period monitoring human trunk to confirm the height shoulder, can accurately monitor the scoliosis condition, suggestion scoliosis risk, thereby, can effectively prevent the scoliosis.
Referring to fig. 2, the intelligent backpack 200 includes a backpack body 201, an inertia measurement unit 202, a timing module 203, at least one processor 204, and a memory 205.
It can be understood that the backpack body 201 is a backpack body part of the intelligent backpack, is used for being worn and carried on the back of a human body, and is provided with an object placing cavity which can contain objects. In some embodiments, the backpack body 201 has a body and two shoulder straps, which can be worn on the back of a person. The backpack body with different shapes and styles can be made of materials such as leather, plastics, terylene, canvas, nylon, cotton, hemp and the like. Here, the shape and material of the backpack body are not limited at all.
The inertial measurement unit 202, the timing module 203, the at least one processor 204, and the memory 205 may be integrated together by a bus, disposed in the backpack body, for example, in a sandwich of the backpack body. It is understood that a bus is used to enable connection communication between these components. The bus includes a power bus, a control bus, and a status signal bus in addition to a data bus.
The inertia measurement unit 202 is located on a central axis of the backpack body and is used for acquiring an inclination angle of the intelligent backpack. It is understood that an Inertial Measurement Unit (IMU) is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. The inertial detection unit IMU comprises a triaxial accelerometer and a triaxial gyroscope, the triaxial accelerometer detects acceleration signals of an object in independent three axes of a carrier coordinate system, the triaxial gyroscope detects angular velocity signals of the carrier relative to a navigation coordinate system, angular velocity and acceleration of the object in a three-dimensional space are measured, and the attitude of the object is calculated according to the angular velocity and the acceleration.
Referring to fig. 3, the inertia detecting unit 202 can detect the inclination angle of the backpack body relative to the vertical direction or the horizontal direction in real time. Specifically, the angular velocity (inclination angle signal) of three axle (X axle, Y axle and Z axle) is exported to the three-axis gyroscope to send the microchip of three-axis gyroscope own area, thereby, microchip obtains the deflection of three-axis gyroscope in X, Y and Z three direction according to the angular velocity (angle signal) of three axle, and then obtains the inclination of knapsack body for horizontal direction (X axle) or vertical direction (Y axle).
Referring to fig. 4, based on the fact that the intelligent backpack is carried on the human body in the using process, when the human body inclines to the left, the intelligent backpack also inclines to the left; when the human trunk inclines to the right, the intelligent backpack also inclines to the right. Therefore, the inclination angle of the intelligent backpack can reflect the inclination angle of the human body (spine).
The timing module 203 may be a real-time clock chip, which can provide accurate real-time. In this embodiment, the timing module 203 can provide real-time for the tilt angle collected by the inertial measurement unit. Thereby, the tilt angle in real time is obtained. For example, the inertia detection unit 202 starts to collect data at time T1 and stops collecting data at time T2, so as to obtain a string of tilt angles with real-time attached thereto, i.e., a time-series of angles.
It can be understood that the angle time series is time series data in which angles are arranged according to a generated time sequence, and the angles in the angle time series reflect the inclination angle of the intelligent backpack in the using process. Based on intelligent knapsack bears human trunk in the use, the inclination of intelligent knapsack can reflect the inclination of human trunk (backbone) to, angle time series can reflect the real-time inclination of human trunk (backbone) in the walking in-process. During walking, the human body periodically swings left and right, and the angle time sequence is also periodic.
The Processor 204 may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc., wherein the general purpose Processor may be a microprocessor or any conventional Processor, etc.
Based on the communication connection between the processor 204 and the inertial measurement unit 202 and the timing module 203, the processor 204 can acquire the angle time series collected by the inertial measurement unit 202 and the timing module 203.
The memory 205, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of monitoring scoliosis in the embodiments of the present invention. The processor 204 may implement the method of monitoring scoliosis in any of the method embodiments described below by executing non-transitory software programs, instructions, and modules stored in the memory 205. In particular, the memory 205 includes either volatile memory or nonvolatile memory, and may also include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read Only Memory (ROM), and the volatile Memory may be a Random Access Memory (RAM). The memory 205 described in embodiments herein is intended to comprise any suitable type of memory.
In some embodiments, when the user uses the intelligent backpack, the angle time sequence acquired by the inertia measurement unit and the timing module is sent to the processor, after the processor acquires the angle time sequence, the single step period is identified according to the periodicity of the angle time sequence, namely, the left step and the right step are identified, the maximum left inclination angle in each left step period is captured to form the maximum left inclination angle sequence, and the maximum right inclination angle in each right step period is captured to form the maximum right inclination angle sequence. And calculating the current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence (the current angle difference value is used for indicating the angle difference of the left inclination angle and the right inclination angle of the user). If the current angle difference value is larger than or equal to the first threshold value, the difference of the left swing amplitude and the right swing amplitude is large in the walking process, high shoulders and low shoulders exist, scoliosis occurs, the risk of scoliosis is prompted, and the posture of a user can be corrected in time. In this embodiment, adopt above-mentioned mode, the difference of amplitude of oscillation about using intelligent knapsack walking period monitoring human trunk to confirm the height shoulder, can accurately monitor the scoliosis condition, suggestion scoliosis risk, thereby, can effectively prevent the scoliosis.
As can be appreciated from the above, the method for monitoring scoliosis provided by the embodiments of the present application may be performed by an intelligent backpack having computational processing capabilities, for example, by at least one processor in the intelligent backpack.
Referring to fig. 5, the method S100 includes, but is not limited to, the following steps:
s10: and acquiring the angle time sequence generated when the intelligent backpack is used currently.
The angle time sequence is time sequence data of the angles arranged according to the generated time sequence, and the angles reflect the inclination angle of the intelligent backpack in the using process. It will be appreciated that the angular time series may be collected by both the inertial detection unit and the timing module in the above-described intelligent backpack embodiment. Referring to fig. 4 again, it can be seen that, in the process of using the intelligent backpack, the inclination condition of the intelligent backpack is substantially consistent with the inclination condition of the human body, so that the inclination angle of the intelligent backpack can reflect the inclination angle of the human body (spine).
Referring to fig. 6, fig. 6 is a schematic diagram of angle-time relationship, in fig. 6, the horizontal axis is a time axis, the vertical axis is an angle collected by the inertia detecting unit, and the angle and the time are in a sine function relationship. The angle time sequence can reflect the real-time inclination angle of the human body trunk (spine) in the walking process. In the walking process, the human body periodically swings left and right, and the angle time sequence is also periodic.
Specifically, when a person walks, the body can periodically swing left and right to a certain degree around a vertical ground axis, and the inclination angle of the human body trunk is detected in real time by the inertial detection unit IMU, so that the periodicity of the angle time sequence is the same as the periodicity of the left and right swinging of the human body during walking. Since the human body completes the swing cycle every two steps of walking, as shown in fig. 6, each periodic wave of the sine function corresponds to two steps, and one step corresponds to a half periodic wave.
In some embodiments, the angle signal acquired by the inertia detection unit may be subjected to band-pass filtering to filter out part of high-frequency noise and glitches, and then the number M of sine waves is determined by detecting the number of rising edges or falling edges of the waveform, so that the number of motion steps N =2M.
S20: and identifying a single step period according to the periodicity of the angle time sequence, acquiring the maximum left inclination angle corresponding to each left single step to form a maximum left inclination angle sequence, and forming a maximum right inclination angle sequence according to the maximum right inclination angle corresponding to each right single step.
It will be appreciated that here the single step period is a half period of the angular time series. After the single step cycle is identified, whether the single step cycle belongs to the left single step or the right single step can be determined according to the positive and negative conditions of the angle. For example, if the angle is positive, the single step cycle belongs to the right single step, and if the angle is negative, the single step cycle belongs to the right single step. It is understood that the positive and negative conditions of the angle and the corresponding conditions of the left and right single steps can be set in advance according to actual conditions.
It will be appreciated that in a single step cycle, as the steps move, the torso oscillation amplitude increases and then decreases, and thus the angle also increases and then decreases. For each left single step, extract the maximum left-dip angle α, maximum left-dipThe bevel angle α is the maximum angle for a left single step. Thus, each left single step corresponds to a maximum left tilt angle [ α ] 1, α 2 ......α n ]Constituting the sequence of maximum left-tilt angles.
Similarly, for each right single step, the maximum right inclination angle β is extracted, and the maximum right inclination angle β is the maximum angle corresponding to one right single step. Thus, the maximum right tilt angle [ β ] for each right single step 1, β 2 ......β n ]The maximum right dip angle sequence is formed.
It will be appreciated that the maximum left tilt angle sequence is the maximum angle sequence of left tilting that is monitored over a period of time when the intelligent backpack is used next time. The sequence of maximum right dip angles is the sequence of maximum angles of tilt to the right that are monitored over a period of time when the intelligent backpack is used next time.
S30: and calculating a current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence, wherein the current angle difference value is used for indicating the angle difference between the left inclination angle and the right inclination angle of the user.
It will be appreciated that the sequence of maximum left-leaning angles reflects a user's left-leaning over a period of time, and the sequence of maximum right-leaning angles reflects a user's right-leaning over a period of time. Thus, the current angle difference may be calculated from the maximum left and right tilt sequences. For example, the mode in the maximum left-leaning angle sequence is compared with the mode in the maximum right-leaning angle sequence, and the difference value is used as the current angle difference value.
In order to reduce the sporadic nature of single measurement data, the left-leaning average angle is used for representing the degree of leaning of the human body (spine) to the left in the current monitoring period, and the right-leaning average angle is used for representing the degree of leaning of the human body (spine) to the right in the current monitoring period. It can be understood that the left-leaning average angle is an average value of a plurality of maximum left-leaning angles in the current monitoring period, and can reduce errors caused by abnormal accidental data, so that the left-leaning average angle can more accurately represent the left-leaning degree of the human trunk (spine) in the current monitoring period. The right-leaning average angle is the average value of a plurality of maximum right-leaning angles in the current monitoring period, and can reduce errors caused by abnormal accidental data, so that the right-leaning average angle can more accurately represent the degree of the right-leaning of the human trunk (spine) in the current monitoring period. Here, the current monitoring period is a period of time during which the smart backpack is currently used.
In order to further increase the reliability of the data, in some embodiments, the step S30 specifically includes: and deleting invalid data in the maximum left-leaning angle sequence to obtain an effective left-leaning angle sequence, and calculating the mean value of the left-leaning angles in the effective left-leaning angle sequence to obtain a left-leaning average angle. And deleting invalid data in the maximum right-leaning angle sequence to obtain an effective right-leaning angle sequence, and calculating the average value of the right-leaning angles in the effective right-leaning angle sequence to obtain the average right-leaning angle. Determining the current angle difference as the difference between the left-leaning average angle and the right-leaning average angle.
It can be understood that the measured data has sporadic nature, and in order to further increase the reliability of the data, here, invalid data (for example, a bigger maximum left-leaning angle or a smaller maximum left-leaning angle) is deleted from the maximum left-leaning angle sequence, the remaining maximum left-leaning angles with little difference are reserved, the effective left-leaning angle sequence is formed, then the average value of the left-leaning angles in the effective left-leaning angle sequence is calculated, and a left-leaning average angle is obtained, so that the left-leaning average angle is more accurate, and misjudgments caused by sporadic limb actions are reduced.
Similarly, invalid data (such as a larger maximum right inclination angle or a smaller maximum right inclination angle) is deleted from the maximum right inclination angle sequence, the remaining maximum right inclination angles with small difference are reserved to form the effective right inclination angle sequence, then the average value of the right inclination angles in the effective right inclination angle sequence is calculated to obtain a right inclination average angle, so that the right inclination average angle is more accurate, and misjudgment caused by accidental limb actions is reduced. And finally, taking the difference value between the left-inclined average angle and the right-inclined average angle as the current angle difference value.
In some embodiments, the foregoing "deleting invalid data in the maximum left-leaning angle sequence to obtain a valid left-leaning angle sequence" specifically includes: calculating a first mean square error of each maximum left inclination angle in the maximum left inclination angle sequence; traversing the maximum left inclination angle sequence, if the difference value between a certain maximum left inclination angle and the first mean square error is larger than or equal to a second threshold value, removing the maximum left inclination angle from the maximum left inclination angle sequence, and obtaining an effective left inclination angle sequence after the maximum left inclination angle sequence is traversed.
It can be understood that the first mean square error can reflect the discrete degree of each maximum left inclination angle in the maximum left inclination angle sequence, and the validity of each maximum left inclination angle in the maximum left inclination angle sequence is judged by using the first mean square error and a second threshold. If the difference between a certain maximum left-leaning angle and the first mean square error is larger than or equal to a second threshold, the maximum left-leaning angle is larger or smaller, and the data belongs to invalid data, and the data is removed from the maximum left-leaning angle sequence. If the difference value between a certain maximum left inclination angle and the first mean square error is smaller than a second threshold value, the maximum left inclination angle is normal, belongs to valid data, and is reserved. And obtaining the effective left inclination angle sequence after comparing each maximum left inclination angle sequence in the maximum left inclination angle sequences.
The second threshold may be an empirical value set by a person skilled in the art according to actual situations, and is not particularly limited herein.
In this embodiment, the second threshold is adopted, each maximum left-leaning angle is screened by combining the first mean square error, invalid data is screened out, and valid data is reserved as a valid left-leaning angle sequence, so that the valid left-leaning angle sequence for calculating the left-leaning average angle is more reliable, and the accuracy of the left-leaning average angle is improved.
In some embodiments, the foregoing "deleting invalid data in the sequence of maximum right-leaning angles to obtain the sequence of valid right-leaning angles" comprises: calculating a second mean square error of each maximum right-dip angle in the maximum right-dip angle sequence; and traversing the maximum right-dip angle sequence, if the difference between a certain maximum right-dip angle and the first mean square error is greater than or equal to a second threshold value, removing the maximum right-dip angle from the maximum right-dip angle sequence, and obtaining an effective right-dip angle sequence after the maximum right-dip angle sequence is traversed.
It can be understood that the second mean square error can reflect the discrete degree of each maximum right inclination angle in the maximum right inclination angle sequence, and the validity of each maximum right inclination angle in the maximum right inclination angle sequence is judged by using the second mean square error and a second threshold. If the difference value between a certain maximum right dip angle and the second mean square error is larger than or equal to the second threshold value, the maximum right dip angle is larger or smaller, which is the invalid data, and the data is removed from the maximum right dip angle sequence. If the difference value between a certain maximum right-leaning angle and the second mean square error is smaller than a second threshold value, the maximum right-leaning angle is normal, belongs to valid data, and is reserved. And obtaining an effective right dip angle sequence after each maximum right dip angle sequence in the maximum right dip angle sequences is compared.
In this embodiment, the second threshold is adopted, each maximum right-leaning angle is screened by combining the second mean square error, the invalid data is screened out, and the valid data is reserved as the valid right-leaning angle sequence, so that the valid right-leaning angle sequence for calculating the right-leaning average angle is more reliable, and the accuracy of the right-leaning average angle is improved.
S40: and if the current angle difference value is greater than or equal to the first threshold value, prompting the scoliosis risk.
It is to be understood that the current angle difference value may be the absolute value of the difference between the left bank angle and the right bank average angle. The current angle difference value can reflect the degree difference of the swing inclination of the human trunk (spine) to the left side and the right side in the current monitoring period, namely the current scoliosis degree.
If the current angle difference value is larger than or equal to the first threshold value, the difference of the left swing amplitude and the right swing amplitude is large in the walking process, high shoulders and low shoulders exist, scoliosis occurs, the risk of scoliosis is prompted, and the posture of a user can be corrected in time. It will be appreciated that the first threshold may be an empirical value set by a person skilled in the art as a practical matter, and in some embodiments the first threshold may be 6 °.
In this embodiment, adopt above-mentioned mode, the difference of amplitude of oscillation about using intelligent knapsack walking period monitoring human trunk to confirm the height shoulder, can accurately monitor the scoliosis condition, suggestion scoliosis risk, thereby, can effectively prevent the scoliosis.
In some embodiments, the method S100 further comprises:
s50: and determining the current inclination trend according to the current angle difference and the plurality of historical angle differences.
It will be appreciated that each time the intelligent backpack is used, an angular difference is calculated. Here, the historical angle difference value refers to an angle difference value generated by using the smart backpack before the smart backpack is currently used. Every time an angle difference value is generated, the angle difference value is stored in a memory of the intelligent backpack.
The current tilt trend may be determined based on the current angle difference and a plurality of historical angle differences. The degree difference of swinging and inclining of the human body trunk (spine) to the left side and the right side in a certain monitoring period can be reflected on the basis of the angle difference, namely the scoliosis degree in the monitoring period. Thus, the current inclination trend can reflect the trend of the change of the scoliosis degree in a period of time (a period of time, such as one week, in which the intelligent backpack is used multiple times).
In some embodiments, the step S50 specifically includes: eliminating invalid data in the current angle difference value and the plurality of historical angle difference values to obtain a plurality of valid angle difference values; and performing linear fitting on the effective angle difference values, acquiring the slope of a linear function obtained by fitting, and taking the slope as the current inclination trend.
And performing effectiveness screening on the current angle difference value and the plurality of historical angle difference values based on the fact that the limb movement has sporadic nature, removing invalid data in the current angle difference value and the plurality of historical angle difference values, and keeping the valid angle difference values to obtain a plurality of valid angle difference values.
It should be noted that "culling" herein does not delete data, but does not involve the fitting operation on the angle difference value determined as invalid data this time. For example, when linear fitting is performed for the ith time, if the historical angle difference values generated by using the backpack for the (i) th time to the (j) th time belong to invalid data, the historical angle difference values for the (i) th time to the (j) th time are eliminated, and the remaining historical angle difference values and the angle difference values for the ith time are used as a plurality of valid angle difference values to perform linear fitting. With the use of the intelligent backpack, when the linear fitting is carried out at the kth time (k is larger than i), the distribution changes due to the increase of data of the angle difference, the historical angle difference between the ith time and the jth time may not belong to invalid data, and the historical angle difference between the ith time and the jth time is taken as an effective angle difference to participate in the linear fitting. By the method, the effective angle difference values subjected to linear fitting at each time have reliability, and misjudgment of the overall conclusion caused by an abnormal result at a certain time can be effectively reduced.
In some embodiments, the aforementioned "removing invalid data from the current angle difference and the multiple historical angle differences to obtain multiple valid angle differences" specifically includes: calculating a third mean square error of the current angle difference value and a plurality of historical angle difference values; and traversing the current angle difference value and the plurality of historical angle difference values, removing the angle difference value from the current angle difference value and the plurality of historical angle difference values if the difference between a certain angle difference value and the third mean square error is larger than or equal to a third threshold value, and obtaining a plurality of effective angle difference values after traversing.
It can be understood that the third mean square error can reflect the degree of dispersion of the current angle difference value and each of the plurality of historical angle difference values, and the third mean square error and a third threshold are used to determine the validity of the angle difference values. If the difference between a certain angle difference value and the third mean square error is larger than or equal to the third threshold, the angle difference value is larger or smaller, the angle difference value belongs to invalid data, and the invalid data is removed from the current angle difference value and the plurality of historical angle difference values. If the difference between a certain angle difference value and the third mean square error is smaller than a third threshold value, the angle difference value is normal and belongs to valid data, and the valid data is reserved. And obtaining a plurality of effective angle difference values after traversing the current angle difference value and the plurality of historical angle difference values.
The third threshold may be an empirical value set by a person skilled in the art according to actual situations, and is not particularly limited herein. Note that the "removal" herein does not delete data, but does not involve the fitting operation on the angle difference value determined as invalid data this time.
In this embodiment, a third threshold is adopted, and a third mean square error is combined to screen each angle difference value of the current angle difference value and the plurality of historical angle difference values, so as to screen out invalid data, and valid data is kept as a plurality of valid angle difference values, so that the plurality of valid angle difference values used for fitting and calculating the current inclination trend are more reliable, and the accuracy of the current inclination trend is improved, and therefore, misjudgment of a general conclusion caused by a certain abnormal result can be reduced.
And performing linear fitting on the effective angle difference values, acquiring the slope of a linear function obtained by fitting, and taking the slope as the current inclination trend. In this embodiment, a least square method may be used to fit the relationship between the effective angle differences and the number of times the intelligent backpack is used, so as to obtain a linear function. As shown in fig. 7, the abscissa is the number of times the intelligent backpack is used, and the ordinate is the effective angle difference.
It will be appreciated that the trend of the linear function can reflect the current inclination trend, i.e. the scoliosis trend. Thus, the slope of the linear function can be taken as the current inclination trend.
S60: and according to the trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time, prompting the worsening condition of the scoliosis trend.
It can be understood that, when the intelligent backpack is used each time, an inclination trend is generated and stored in the memory of the intelligent backpack, which is convenient for subsequent statistical monitoring. Here, the "inclination tendency when the smart backpack is used last time" is an inclination tendency generated before the smart backpack is currently used.
Thus, the trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time can be calculated, and the worsening condition of the scoliosis trend can be prompted based on the trend difference.
Here, the trend difference may be a difference between a current inclination trend (a slope of a currently fitted linear function) and a last inclination trend (a slope of a last fitted linear function).
In some embodiments, the step S60 specifically includes: if the trend difference is within the preset range, indicating that the scoliosis trend is not worsened; and if the trend difference is not within the preset range, prompting that the scoliosis trend is worsened.
Here, the preset range is an empirical value for evaluating whether or not the scoliosis tendency is deteriorated. If the trend difference falls into the preset range, the scoliosis trend is not changed seriously, and the prompt is that the scoliosis trend is not worsened. At the moment, the intelligent backpack can be controlled to remind the user to keep a good posture habit, and suggestions for preventing scoliosis are provided.
If the trend difference does not fall into the preset range, the tendency of the scoliosis is serious, and the deterioration of the scoliosis tendency is prompted. At this moment, can control intelligent knapsack and remind the user to correct the physique.
In this embodiment, if the current angle difference is greater than or equal to the third threshold, the current inclination trend is determined according to the current angle difference and the plurality of historical angle differences, and the worsening condition of the scoliosis trend is prompted according to the trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time, so that scoliosis can be effectively prevented and the occurrence of scoliosis can be prevented.
In some embodiments, in the case that the current angle difference value is greater than or equal to the fifth threshold value, if the trend difference is within a preset range, it is indicated that the scoliosis trend is not worsened; and if the trend difference is not within the preset range, prompting that the scoliosis trend is worsened. In this embodiment, the fifth threshold is a critical value for triggering the worsening comparison of the scoliosis trend, and the current angle difference value and the trend difference are combined to perform the reminding, so that the reminding feedback is more accurate.
In some embodiments, the method S100 further comprises:
s70: determining the change rate of the inclination trend according to the current inclination trend and a plurality of historical inclination trends; if the change rate is greater than or equal to the fourth threshold, prompting the scoliosis risk; and if the change rate is smaller than a fourth threshold value, prompting that good posture habit is kept.
It can be understood that, when the intelligent backpack is used each time, an inclination trend is generated and stored in the memory of the intelligent backpack, which is convenient for follow-up statistical monitoring. Here, "historical tilt trend" refers to a tilt trend generated by using the smart backpack before the smart backpack is currently used.
The change rate of the inclination trend refers to the change speed of the current inclination trend relative to a plurality of historical inclination trends, and can reflect the change speed of the scoliosis trend.
Specifically, linear fitting is performed on the current inclination trend and the plurality of historical inclination trends, and the slope of a linear function obtained through fitting is the change rate of the inclination trend.
If the change rate is larger than or equal to the fourth threshold value, the trend of the scoliosis is relatively fast, and the scoliosis is likely to develop, and the risk of the scoliosis is prompted; and if the change rate is smaller than the fourth threshold value, the change of the scoliosis trend is relatively slow, and no risk of scoliosis exists, and the good posture habit is prompted to be kept.
The fourth threshold is an empirical value set by a person skilled in the art according to actual conditions, and is not limited herein.
In the embodiment, the change speed of the scoliosis trend is monitored by calculating the change rate of the inclination trend, so that the development of the scoliosis trend can be effectively prevented, a user can be helped to keep a good posture, and the scoliosis risk is reduced from the source.
To sum up, the method for monitoring scoliosis provided by the embodiment of the application is applied to the intelligent backpack, the method acquires the angle time sequence by acquiring the inclination angle of the intelligent backpack in the using process in real time, and the inclination angle of the intelligent backpack can reflect the inclination angle of the human body (spine) based on the intelligent backpack bearing the human body in the using process, so that the angle time sequence can reflect the real-time inclination angle of the human body (spine) in the walking process. The human trunk swings left and right periodically in the walking process, the angle time sequence is also periodic, and therefore, the single step period can be identified according to the periodicity of the angle time sequence, namely, the left step and the right step are identified, the maximum left inclination angle in each left step period is captured to form the maximum left inclination angle sequence, and the maximum right inclination angle in each right step period is captured to form the maximum right inclination angle sequence. And calculating a current angle difference value according to the maximum left inclination angle sequence and the maximum right inclination angle sequence (the current angle difference value is used for indicating the angle difference of the left inclination angle and the right inclination angle of the user). If the current angle difference value is larger than or equal to the first threshold value, it is indicated that the left swing amplitude difference and the right swing amplitude difference are large in the walking process, high shoulders and low shoulders exist, scoliosis is generated, the scoliosis risk is prompted, and the posture of a user can be corrected in time. Adopt above-mentioned mode, monitoring human trunk in the walking period of using intelligent knapsack and controlling the swing amplitude difference to confirm the height shoulder, can accurately monitor the scoliosis condition, suggestion scoliosis risk, thereby, can effectively prevent the scoliosis.
Embodiments of the present application also provide a computer-readable storage medium, such as a memory, comprising program code executable by a processor to perform the method of monitoring scoliosis of the above embodiments. For example, the computer readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CDROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
Embodiments of the present application also provide a computer program product including one or more program codes stored in a computer readable storage medium. The program code is read from the computer readable storage medium by a processor of the electronic device, and the program code is executed by the processor to perform the method steps of the method of monitoring scoliosis provided in the embodiments described above.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by hardware associated with program code, and the program may be stored in a computer readable storage medium, where the above mentioned storage medium may be a read-only memory, a magnetic or optical disk, etc.
It should be noted that the above-described embodiments of the apparatus are merely illustrative, where the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; within the context of the present application, where technical features in the above embodiments or in different embodiments may also be combined, the steps may be implemented in any order and there are many other variations of the different aspects of the present application described above which are not provided in detail for the sake of brevity; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of monitoring scoliosis for use with an intelligent backpack, the method comprising:
acquiring an angle time sequence generated when the intelligent backpack is used currently, wherein the angle time sequence is time sequence data of angles arranged according to the generated time sequence, and the angles reflect the inclination angle of the intelligent backpack in the using process;
identifying a single step period according to the periodicity of the angle time sequence, acquiring the maximum left inclination angle corresponding to each left single step to form a maximum left inclination angle sequence, and forming a maximum right inclination angle sequence according to the maximum right inclination angle corresponding to each right single step;
calculating a current angle difference value according to the maximum left dip angle sequence and the maximum right dip angle sequence, wherein the current angle difference value is used for indicating the angle difference between the left dip angle and the right dip angle of the user;
and if the current angle difference value is larger than or equal to a first threshold value, prompting the scoliosis risk.
2. The method of claim 1, wherein said calculating a current angle difference from said sequence of maximum left dips and said sequence of maximum right dips comprises:
deleting invalid data in the maximum left-leaning angle sequence to obtain an effective left-leaning angle sequence, and calculating the mean value of left-leaning angles in the effective left-leaning angle sequence to obtain a left-leaning average angle;
deleting invalid data in the maximum right-leaning angle sequence to obtain an effective right-leaning angle sequence, and calculating the average value of right-leaning angles in the effective right-leaning angle sequence to obtain a right-leaning average angle;
determining the current angle difference value as a difference between the left-leaning average angle and the right-leaning average angle.
3. The method of claim 2, wherein said deleting invalid data from said sequence of maximum bank angles to obtain a sequence of valid bank angles comprises:
calculating a first mean square error of each maximum left-leaning angle in the maximum left-leaning angle sequence;
traversing the maximum left inclination angle sequence, if the difference value between a certain maximum left inclination angle and the first mean square error is larger than or equal to a second threshold value, removing the maximum left inclination angle from the maximum left inclination angle sequence, and obtaining the effective left inclination angle sequence after the maximum left inclination angle sequence is traversed;
deleting invalid data in the maximum right-leaning angle sequence to obtain an effective right-leaning angle sequence, wherein the effective right-leaning angle sequence comprises the following steps:
calculating a second mean square error of each maximum right-dip angle in the maximum right-dip angle sequence;
and traversing the maximum right dip angle sequence, if the difference value between a certain maximum right dip angle and the first mean square error is larger than or equal to the second threshold value, removing the maximum right dip angle from the maximum right dip angle sequence, and obtaining the effective right dip angle sequence after the maximum right dip angle sequence is traversed.
4. The method of claim 1, further comprising:
determining a current inclination trend according to the current angle difference and a plurality of historical angle differences;
and prompting the worsening condition of the scoliosis trend according to the trend difference between the current inclination trend and the inclination trend when the intelligent backpack is used last time.
5. The method of claim 4, wherein determining a current tilt trend based on the current angle difference and a plurality of historical angle differences comprises:
eliminating invalid data in the current angle difference value and the plurality of historical angle difference values to obtain a plurality of valid angle difference values;
and performing linear fitting on the effective angle difference values, acquiring the slope of a linear function obtained by fitting, and taking the slope as the current inclination trend.
6. The method of claim 5, wherein said culling invalid data from said current angular difference value and said plurality of historical angular difference values to obtain a plurality of valid angular difference values comprises:
calculating a third mean square error of the current angle difference and the plurality of historical angle differences;
traversing the current angle difference value and the plurality of historical angle difference values, if the difference between a certain angle difference value and the third mean square error is larger than or equal to a third threshold value, removing the angle difference value from the current angle difference value and the plurality of historical angle difference values, and obtaining the plurality of effective angle difference values after traversing.
7. The method according to claim 4, wherein the prompting of the worsening of scoliosis tendency based on the trend difference between the current inclination trend and the inclination trend of the last time the intelligent backpack was used comprises:
if the trend difference is within a preset range, prompting that the scoliosis trend is not worsened;
and if the trend difference is not within the preset range, prompting that the scoliosis trend is worsened.
8. The method of claim 5, further comprising:
determining the change rate of the inclination trend according to the current inclination trend and a plurality of historical inclination trends;
if the change rate is greater than or equal to a fourth threshold value, prompting the scoliosis risk;
and if the change rate is smaller than the fourth threshold value, prompting that good posture habit is kept.
9. An intelligent backpack, comprising:
the backpack comprises a backpack body, an inertia measuring unit and a timing module, wherein the backpack body is arranged on the backpack body; the intelligent backpack comprises a backpack body, an inertial measurement unit, a timing module and a control module, wherein the inertial measurement unit is positioned on a central axis of the backpack body and is used for acquiring the inclination angle of the intelligent backpack, and the timing module is used for acquiring real-time;
the at least one processor is respectively in communication connection with the inertial measurement unit and the timing module to acquire an angle time sequence generated when the intelligent backpack is used currently;
a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1-8.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a smart backpack, cause the smart backpack to perform the method of any of claims 1-8.
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