CN117752324A - Scoliosis detection method and system based on muscle current signals - Google Patents
Scoliosis detection method and system based on muscle current signals Download PDFInfo
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Abstract
The invention provides a scoliosis detection method and system based on muscle current signals. The detection scheme provided by the invention judges whether the scoliosis exists or not by detecting and analyzing the deviation condition of muscle current at two sides of the spine, can realize accurate monitoring of the primary scoliosis, does not harm the body, and has a good market application prospect.
Description
Technical Field
The invention relates to the technical field of spine health detection, in particular to a scoliosis detection method, a system, electronic equipment and a computer storage medium based on muscle current signals.
Background
Currently, there are various detection methods for scoliosis, physical observation, CT, magnetic resonance detection, and the like. The physical observation method can only detect scoliosis symptoms to a certain extent, and cannot detect the initial scoliosis condition; although the CT and magnetic resonance detection method can clearly acquire the true condition of scoliosis at each stage, the detection mode has certain harm to human body. The solution of the present invention aims to solve or improve this technical problem.
Disclosure of Invention
In order to at least solve the technical problems in the background art, the invention provides a scoliosis detection method, a system, electronic equipment and a computer storage medium based on muscle current signals.
The first aspect of the invention provides a scoliosis detection method based on muscle current signals, which comprises the following steps:
under the motion state of a measured object, a first current signal and a second current signal of muscles at two sides of the spine are detected through a first muscle current detector and a second muscle current detector;
and after the first current signal and the second current signal are processed, calculating to obtain current deviation data, judging whether scoliosis exists according to the current deviation data, and outputting a judging result.
Further, the first muscle current detector and the second muscle current detector are adhered to the skin corresponding to the muscles on both sides of the spine.
Further, the first muscle current detector and the second muscle current detector transmit the first current signal and the second current signal to a scoliosis analysis host in a wireless transmission mode.
Further, after the first current signal and the second current signal are processed, current deviation data is obtained through calculation, and whether scoliosis exists or not is judged according to the current deviation data, including:
determining current signals of the first current signal and the second current signal in a starting stage and a stopping stage, and reserving the remaining current signals to obtain a third current signal and a fourth current signal respectively;
performing current salient feature extraction and different frequency pairing on the third current signal and the fourth current signal according to the stride data of the tested object to obtain a fifth current signal and a sixth current signal respectively;
calculating the difference value between the paired current salient features in the fifth current signal and the sixth current signal, and fitting each difference value into a deviation curve;
and calculating an approximation value of the deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
Further, the determining that the first current signal and the second current signal are in a current signal of a start phase and a stop phase includes:
identifying current peaks of the first current signal and the second current signal in an initial stage and an end stage respectively, and determining a first initial time and a first end time of the first current signal, and a second initial time and a second end time of the second current signal according to the current peaks;
acquiring scene image data of the tested object, determining a test distance of a test area according to the scene image data, and determining a first fine tuning coefficient and a second fine tuning coefficient according to the test distance;
the first initial time and the second initial time are adjusted to be a third initial time and a fourth initial time by using the first fine tuning coefficient; and adjusting the first end time and the second end time to a third end time and the fourth end time using the second trimming coefficient;
determining and obtaining current signals of the first current signal in a starting stage and a stopping stage according to the third initial time and the third ending time; and determining the current signals of the second current signal in the starting stage and the stopping stage according to the fourth initial time and the fourth ending time.
Further, when the test distance is greater than or equal to a distance threshold, setting the first trimming coefficient and the second trimming coefficient to be 1; and when the test distance is smaller than a distance threshold value, setting the first fine tuning coefficient to be in negative correlation with the test distance, and setting the second fine tuning coefficient to be in positive correlation with the test distance.
Further, the movement state comprises a plurality of walking modes;
calculating an approximation of the deviation curve and the standard deviation curve, determining that there is scoliosis when the approximation is lower than an approximation threshold, otherwise determining that there is no scoliosis, including:
fitting the deviation curve under each walking mode into an equivalent deviation curve according to the corresponding weight;
and calculating an approximation value of the equivalent deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
The second aspect of the invention provides a scoliosis detection system based on muscle current signals, which comprises a first muscle current detector, a second muscle current detector and a scoliosis analysis host, wherein the scoliosis analysis host is in wireless connection with the first muscle current detector and the second muscle current detector;
the first muscle current detector and the second muscle current detector are respectively used for acquiring a first current signal and a second current signal of muscles on two sides of the spine of a tested object in a motion state, and wirelessly transmitting the first current signal and the second current signal to the scoliosis analysis host;
the scoliosis analysis host is used for obtaining and outputting a scoliosis judgment result by calling executable computer program codes and executing the method according to any one of the previous claims.
A third aspect of the present invention provides an electronic device comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method of any one of the preceding claims.
A fourth aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs a method as claimed in any one of the preceding claims.
A fifth aspect of the invention provides a computer program product which, when executed by a processor of a computing device, implements a method as claimed in any preceding claim.
The invention has the beneficial effects that:
the detection scheme provided by the invention judges whether the scoliosis exists or not by detecting and analyzing the deviation condition of muscle current at two sides of the spine, can realize accurate monitoring of the primary scoliosis, does not harm the body, and has a good market application prospect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a scoliosis detection method based on muscle current signals according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a scoliosis detection system based on muscle current signals according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to the flow chart shown in fig. 1, an embodiment of the invention provides a scoliosis detection method based on muscle current signals, which comprises the following steps:
under the motion state of a measured object, a first current signal and a second current signal of muscles at two sides of the spine are detected through a first muscle current detector and a second muscle current detector;
and after the first current signal and the second current signal are processed, calculating to obtain current deviation data, judging whether scoliosis exists according to the current deviation data, and outputting a judging result.
As described in the background art, the existing detection mode cannot detect the initial scoliosis or can cause a certain harm to the human body. Aiming at the defects of the existing detection mode, the scheme of the invention designs a novel scoliosis detection mode. After scoliosis occurs, the tension degrees of muscles at two sides of the spine are different, and the current intensities of muscle currents sent by the muscles with different tension degrees are different. In addition, the existing detection modes are all static, but the tension deviation of muscles at two sides is not obvious in the initial stage of scoliosis, the current deviation of the muscles at two sides is not obvious in the static state, and the detection result is easy to be wrong due to the influence of some interference current. In contrast, the invention detects the muscle current in the movement state of the detected object, and the movement amplitude of the muscle is larger in the movement state, and the tension degree difference caused by scoliosis can be amplified, so that the invention is characterized in that the muscle current is obviously different. Therefore, the detection scheme of the invention can realize accurate monitoring of the initial scoliosis, and the detection mode can not cause harm to the body, thereby having better market application prospect.
Further, the first muscle current detector and the second muscle current detector are adhered to the skin corresponding to the muscles on both sides of the spine.
In this embodiment, two main ways of myoelectric signal surface myoelectric signal and needle electrode myoelectric signal acquisition are:
1) Surface electromyographic signals (semgs) are bioelectric currents produced by the contraction of human surface muscles. The nervous system controls the movement (contraction or relaxation) of the muscles, and at the same time different muscle fiber movement units on the surface skin produce mutually different signals. The surface electromyography is also actually a temporal, spatial combined signal consisting of individual motor unit electrophysiological signals, reflecting the electrophysiological properties of the whole muscle (all muscle fibers involved in contraction).
2) Needle electrode electromyography (dEMG) can further explore the bioelectrical signal characteristics of individual exercise units controlling muscle activity, including exercise unit activity conditions based on recruitment timing, one-by-one display of individual exercise unit discharge timing, change characteristics of exercise unit recruitment and recruitment states, and discharge rate (pulse number/s) of individual exercise units over time, muscle force contraction curve, and the like. Therefore, compared with the conventional sEMG, the dEMG can more deeply discuss the details of the muscle contraction activity, can more clearly reflect the discharge characteristic of a certain motion unit, and more deeply discuss the characteristic of the cell functional level than the sEMG, but the collection process can cause damage to the human body.
In order to further reduce the injury to human bodies, the invention preferably adopts a surface electromyographic signal (sEMG) mode. In specific implementation, the first muscle current detector and the second muscle current detector can be provided with conductive patch structures, and the conductive surfaces of the first muscle current detector and the second muscle current detector are only required to be attached to the skin corresponding to muscles on two sides of the spine when the spine is used.
Further, the first muscle current detector and the second muscle current detector transmit the first current signal and the second current signal to a scoliosis analysis host in a wireless transmission mode.
In this embodiment, when the muscle current detector is adhered to the skin, the muscle current detector with a certain component may interfere with the measured object, resulting in unexpected contraction and expansion of the muscle; and, in the movement process of the measured object, the matched transmission line can also be pulled, and the unexpected contraction and expansion of muscles can be caused. All the above factors affect the generation of muscle current, which easily leads to distortion of detection results. On the other hand, the muscle current detector is light and small as far as possible, and can be preheated to the body temperature when in use; on the other hand, the electric signals detected by the muscle current detector are sent to the scoliosis analysis host computer in a wireless transmission mode, so that the influence of pulling on muscle current is avoided, the implementation of dynamic detection is facilitated by the wireless transmission mode, the detected object can freely move in a larger range, and the scoliosis analysis host computer performs scoliosis analysis based on more and more reliable muscle current data.
The scoliosis analysis host can be constructed through an arduino platform.
Further, after the first current signal and the second current signal are processed, current deviation data is obtained through calculation, and whether scoliosis exists or not is judged according to the current deviation data, including:
determining current signals of the first current signal and the second current signal in a starting stage and a stopping stage, and reserving the remaining current signals to obtain a third current signal and a fourth current signal respectively;
performing current salient feature extraction and different frequency pairing on the third current signal and the fourth current signal according to the stride data of the tested object to obtain a fifth current signal and a sixth current signal respectively;
calculating the difference value between the paired current salient features in the fifth current signal and the sixth current signal, and fitting each difference value into a deviation curve;
and calculating an approximation value of the deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
In this embodiment, in the start phase and the stop phase, the muscles on both sides of the spine of the measured object are in an unconventional stress state, and the contraction of the muscles on both sides is different when the left foot is first out and the right foot is first out, so the difference of the muscle currents in the above phases is relatively large, but this cannot reflect the relation with scoliosis. Therefore, the invention firstly removes partial current signals of the starting stage and the stopping stage, and only retains the current signals of the middle part, namely the third current signal and the fourth current signal.
Then, extracting current salient features in the third current signal and the fourth current signal, such as a peak area of current intensity; meanwhile, when a person walks, the left leg and the right leg are alternated, so that the third current signal and the fourth current signal are also required to be matched in different frequencies according to stride data, the purpose of performing deviation calculation on muscle current in a leg-taking stage and performing deviation calculation on muscle current in a leg-receiving stage is achieved, and the reliability of a deviation calculation result is ensured.
The invention designs the extraction of the current salient features according to the stride data, in particular, the stride data can determine the walking rhythm of the tested object, and the fluctuation of the muscle electric signals at two sides of the spine is also related to the rhythm, so that the extraction of the salient features according to the stride data can reduce the influence of the interference electric signals compared with the direct identification of the peaks/troughs in the electric signals as the current salient features.
Finally, because the collected fifth current signal and the sixth current signal are actually data matrixes of a plurality of current salient features, the data are integrated and then are subjected to overall analysis, so that the influence of individual abnormal current salient features on an analysis result is reduced. Specifically, the invention calculates the difference value between the corresponding current significant characteristics in the fifth current signal and the sixth current signal, then fits the difference values into a difference curve, if the approximation value of the difference curve and the standard deviation curve is high enough, the tested object is judged to be normal, no scoliosis exists, and otherwise, the scoliosis situation exists.
Further, the determining that the first current signal and the second current signal are in a current signal of a start phase and a stop phase includes:
identifying current peaks of the first current signal and the second current signal in an initial stage and an end stage respectively, and determining a first initial time and a first end time of the first current signal, and a second initial time and a second end time of the second current signal according to the current peaks;
acquiring scene image data of the tested object, determining a test distance of a test area according to the scene image data, and determining a first fine tuning coefficient and a second fine tuning coefficient according to the test distance;
the first initial time and the second initial time are adjusted to be a third initial time and a fourth initial time by using the first fine tuning coefficient; and adjusting the first end time and the second end time to a third end time and the fourth end time using the second trimming coefficient;
determining and obtaining current signals of the first current signal in a starting stage and a stopping stage according to the third initial time and the third ending time; and determining the current signals of the second current signal in the starting stage and the stopping stage according to the fourth initial time and the fourth ending time.
In this embodiment, the measured object will perform acceleration start with force in the start stage and perform acceleration stop with force in the stop stage, the current signal will show relatively obvious peak, and the current signal in the normal walking stage between start and stop will be regular fluctuation. Thus, the invention initially determines the first initial time and the first end time of the first current signal, and the second initial time and the second end time of the second current signal according to the identification of the wave peaks of the front section and the rear end.
However, in practice, the above-mentioned time node is not completely accurate, because the "effort" of starting and stopping the tested object is limited by the external condition, mainly the length of the test distance, for example, when the test distance is large enough, the starting and stopping of the tested object will be more graceful, and accordingly, the "effort" of starting and stopping will be larger; when the test distance is smaller, the tested object worries about exceeding the test area, so that the starting and stopping steps are more cautious, and accordingly, the "force" for starting and stopping steps is smaller. Obviously, the latter situation can lead to the boundary between the starting stage and the middle normal walking stage of the muscle electric signals of the tested object to be more fuzzy, and the electric signals of the middle normal walking stage can not be accurately screened out. In this regard, the invention further sets up and lays the pick-up device in the test scene, confirm the test distance of the test area (can discern through identifying the yellow line of the test area) according to scene image data shot, confirm and get the fine tuning coefficient according to the test distance, use this fine tuning coefficient to come to confirm the aforesaid initial moment and finish moment that confirm to the middle appropriate regulation all, in this way, can guarantee that the electric signal data that all draw is middle normal walking stage through the moderate reduction of the data length of the electric signal data of middle normal walking stage, finally guarantee the accuracy of the identification result of subsequent scoliosis.
In addition, the foregoing stride data may actually be obtained by extracting the scene image data captured by the image capturing device, and the specific extraction process relates to a conventional technology in the image recognition field, which is not described herein.
The camera device can be integrally arranged with the scoliosis analysis host, an external scheme can be adopted, and the camera device is communicated with the scoliosis analysis host in a wired or wireless mode.
Further, when the test distance is greater than or equal to a distance threshold, setting the first trimming coefficient and the second trimming coefficient to be 1; and when the test distance is smaller than a distance threshold value, setting the first fine tuning coefficient to be in negative correlation with the test distance, and setting the second fine tuning coefficient to be in positive correlation with the test distance.
In this embodiment, when the test distance is sufficiently large, starting and stopping of the tested object will be more "power-consuming", and the current peaks at the front and rear ends of the muscle current signal and the current at the middle section have more obvious distinction, that is, the electrical signal data of the middle normal walking stage can be accurately determined according to the current peaks at the front and rear ends at this time, so that the fine tuning coefficient is set to 1, that is, the initial time and the end time are not adjusted. When the test distance is not enough, the start and stop of the tested object are more cautious, so that the similarity between the current peaks at the front section and the rear end and the current peak at the middle section is higher, and the distinction becomes difficult. In this case, the first trimming coefficient is set to be inversely related to the test distance, that is, the smaller the test distance is, the larger the trimming coefficient is, so that the initial time is closer to the middle; setting the second trimming coefficient positively correlates with the test distance, i.e., the smaller the test distance is, the smaller the trimming coefficient is, so that the ending time is closer to the middle.
Further, the movement state comprises a plurality of walking modes;
calculating an approximation of the deviation curve and the standard deviation curve, determining that there is scoliosis when the approximation is lower than an approximation threshold, otherwise determining that there is no scoliosis, including:
fitting the deviation curve under each walking mode into an equivalent deviation curve according to the corresponding weight;
and calculating an approximation value of the equivalent deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
In this embodiment, the foregoing embodiment is actually a scoliosis test performed in a motion state in which the subject is walking forward, but the reliability of the single test mode may not be sufficient for initial scoliosis. Therefore, the invention tests through more walking modes, such as forward walking, backward walking, left-right shaking walking, fast walking and the like, then fits the deviation curve under each walking mode into an equivalent deviation curve according to corresponding weights, then calculates the approximate value of the equivalent deviation curve and the standard deviation curve, and finally obtains the judgment result of scoliosis.
The weight of the deviation curve under each walking mode can be determined according to the action amplitude of the walking mode, the greater the action amplitude is, the greater the tension degree change of muscles at two sides of the spine is, the greater the corresponding change of the muscle electric signals is, the more the difference of the muscles at two sides is more easily represented, and the greater the corresponding weight is set; otherwise, setting the corresponding weight smaller. For example, if the motion ranges corresponding to the forward, backward, fast, and side-to-side shake lines are sequentially increased, the weights corresponding thereto are sequentially increased.
As shown in fig. 2, the embodiment of the invention also discloses a scoliosis detection system based on muscle current signals, which comprises a first muscle current detector, a second muscle current detector and a scoliosis analysis host, wherein the scoliosis analysis host is in wireless connection with the first muscle current detector and the second muscle current detector;
the first muscle current detector and the second muscle current detector are respectively used for acquiring a first current signal and a second current signal of muscles on two sides of the spine of a tested object in a motion state, and wirelessly transmitting the first current signal and the second current signal to the scoliosis analysis host;
the scoliosis analysis host is used for obtaining and outputting a scoliosis judgment result by calling executable computer program codes and executing the method according to any one of the previous claims.
The embodiment of the invention also discloses an electronic device, which comprises: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method as described in the previous embodiment.
The embodiment of the invention also discloses a computer storage medium, and a computer program is stored on the storage medium, and when the computer program is run by a processor, the computer program executes the method according to the previous embodiment.
An apparatus/system according to an embodiment of the present disclosure may include a processor, a memory for storing program data and executing the program data, a persistent memory such as a disk drive, a communication port for processing communication with an external apparatus, a user interface apparatus, and the like. The method is implemented as a software module or may be stored on a computer readable recording medium as computer readable code or program commands executable by a processor. Examples of the computer-readable recording medium may include magnetic storage media (e.g., read-only memory (ROM), random-access memory (RAM), floppy disks, hard disks, etc.), optical read-out media (e.g., CD-ROMs, digital Versatile Disks (DVDs), etc.), among others. The computer readable recording medium may be distributed among computer systems connected in a network, and the computer readable code may be stored and executed in a distributed manner. The medium may be computer-readable, stored in a memory, and executed by a processor.
Embodiments of the present disclosure may be directed to functional block components and various processing operations. Functional blocks may be implemented as various numbers of hardware and/or software components that perform the specified functions. For example, embodiments of the present disclosure may implement direct circuit components, such as memory, processing circuitry, logic circuitry, look-up tables, and the like, that may perform various functions under the control of one or more microprocessors or other control devices. The components of the present disclosure may be implemented by software programming or software components. Similarly, embodiments of the present disclosure may include various algorithms implemented by a combination of data structures, processes, routines, or other programming components, and may be implemented by a programming or scripting language (such as C, C ++, java, assembler, or the like). The functional aspects may be implemented by algorithms executed by one or more processors. Further, embodiments of the present disclosure may implement related techniques for electronic environment setup, signal processing, and/or data processing. Terms such as "mechanism," "element," "unit," and the like may be used broadly and are not limited to mechanical and physical components. These terms may refer to a series of software routines associated with a processor or the like.
Specific embodiments are described in this disclosure as examples, and the scope of the embodiments is not limited thereto.
Although embodiments of the present disclosure have been described, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the following claims. Accordingly, the above-described embodiments of the present disclosure should be construed as examples and are not limited in all respects. For example, each component described as a single unit may be performed in a distributed manner, and as such, components described as distributed may be performed in a combined manner.
All examples or example terms (e.g., etc.) are used in embodiments of the disclosure for the purpose of describing the embodiments of the disclosure and are not intended to limit the scope of the embodiments of the disclosure.
Moreover, unless explicitly stated otherwise, expressions such as "necessary", "important", etc. associated with certain components may not indicate that the components are absolutely required.
Those of ordinary skill in the art will understand that the embodiments of the present disclosure can be implemented in modified forms without departing from the spirit and scope of the disclosure.
Claims (10)
1. The scoliosis detection method based on the muscle current signals is characterized by comprising the following steps of:
under the motion state of a measured object, a first current signal and a second current signal of muscles at two sides of the spine are detected through a first muscle current detector and a second muscle current detector;
and after the first current signal and the second current signal are processed, calculating to obtain current deviation data, judging whether scoliosis exists according to the current deviation data, and outputting a judging result.
2. The scoliosis detection method based on muscle current signals according to claim 1, wherein: the first muscle current detector and the second muscle current detector are adhered to the skin corresponding to the muscles on two sides of the spine.
3. The scoliosis detection method based on muscle current signals according to claim 2, wherein: the first muscle current detector and the second muscle current detector transmit the first current signal and the second current signal to a scoliosis analysis host in a wireless transmission mode.
4. The scoliosis detection method based on muscle current signals according to claim 1, wherein: after the first current signal and the second current signal are processed, current deviation data are obtained through calculation, and whether scoliosis exists or not is judged according to the current deviation data, and the method comprises the following steps:
determining current signals of the first current signal and the second current signal in a starting stage and a stopping stage, and reserving the remaining current signals to obtain a third current signal and a fourth current signal respectively;
performing current salient feature extraction and different frequency pairing on the third current signal and the fourth current signal according to the stride data of the tested object to obtain a fifth current signal and a sixth current signal respectively;
calculating the difference value between the paired current salient features in the fifth current signal and the sixth current signal, and fitting each difference value into a deviation curve;
and calculating an approximation value of the deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
5. The scoliosis detection method based on muscle current signals according to claim 4, wherein: the determining that the first current signal and the second current signal are in the current signals of the start phase and the stop phase comprises:
identifying current peaks of the first current signal and the second current signal in an initial stage and an end stage respectively, and determining a first initial time and a first end time of the first current signal, and a second initial time and a second end time of the second current signal according to the current peaks;
acquiring scene image data of the tested object, determining a test distance of a test area according to the scene image data, and determining a first fine tuning coefficient and a second fine tuning coefficient according to the test distance;
the first initial time and the second initial time are adjusted to be a third initial time and a fourth initial time by using the first fine tuning coefficient; and adjusting the first end time and the second end time to a third end time and the fourth end time using the second trimming coefficient;
determining and obtaining current signals of the first current signal in a starting stage and a stopping stage according to the third initial time and the third ending time; and determining the current signals of the second current signal in the starting stage and the stopping stage according to the fourth initial time and the fourth ending time.
6. The scoliosis detection method based on muscle current signals according to claim 5, wherein: when the test distance is greater than or equal to a distance threshold, setting the first fine tuning coefficient and the second fine tuning coefficient to be 1; and when the test distance is smaller than a distance threshold value, setting the first fine tuning coefficient to be in negative correlation with the test distance, and setting the second fine tuning coefficient to be in positive correlation with the test distance.
7. A scoliosis detection method based on muscle current signals according to claim 5 or 6, characterized in that: the motion state comprises a plurality of walking modes;
calculating an approximation of the deviation curve and the standard deviation curve, determining that there is scoliosis when the approximation is lower than an approximation threshold, otherwise determining that there is no scoliosis, including:
fitting the deviation curve under each walking mode into an equivalent deviation curve according to the corresponding weight;
and calculating an approximation value of the equivalent deviation curve and the standard deviation curve, and judging that the scoliosis exists when the approximation value is lower than an approximation threshold value, or judging that the scoliosis does not exist.
8. The scoliosis detection system based on the muscle current signals comprises a first muscle current detector, a second muscle current detector and a scoliosis analysis host, wherein the scoliosis analysis host is in wireless connection with the first muscle current detector and the second muscle current detector;
the first muscle current detector and the second muscle current detector are respectively used for acquiring a first current signal and a second current signal of muscles on two sides of the spine of a tested object in a motion state, and wirelessly transmitting the first current signal and the second current signal to the scoliosis analysis host;
the method is characterized in that: the scoliosis analysis host is used for obtaining and outputting a scoliosis judgment result by calling executable computer program codes and executing the method according to any one of claims 1-7.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the method is characterized in that: the processor invokes the executable program code stored in the memory to perform the method of any of claims 1-7.
10. A computer storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, performs the method of any of claims 1-7.
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