CN115153505A - Biological feedback type spinal joint correction training method and device - Google Patents
Biological feedback type spinal joint correction training method and device Download PDFInfo
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- 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
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- 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
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1079—Measuring physical dimensions, e.g. size of the entire body or parts thereof using optical or photographic means
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V40/20—Movements or behaviour, e.g. gesture recognition
Abstract
The invention discloses a biological feedback type spine joint correction training method and device, wherein a first action position threshold value is determined according to first matching training course information and first basic information; acquiring a first image real-time acquisition result through image acquisition equipment; performing action characteristic identification on the mobile terminal, and obtaining a first correction feedback parameter according to an action characteristic identification result and a first action position threshold value; obtaining a first duration parameter according to the action characteristic identification result, and obtaining a second correction feedback parameter; obtaining heart rate change information of the first user through a biofeedback wearable device to obtain a third correction feedback parameter; and performing correction training management based on the first correction feedback parameter, the second correction feedback parameter and the third correction feedback parameter. The problem of prior art in the in-process that carries out the training of correcting of backbone joint, lack to carry out intelligent correction training supervision, and then make the problem feedback that can't in time accurately carry out the training correct, lead to correcting the not good technical problem of training effect.
Description
Technical Field
The invention relates to the field related to data identification and data representation, in particular to a biological feedback type spinal joint correction training method and device.
Background
Scoliosis refers to a spinal deformity in which one or more spinal segments of the spine are bent laterally with vertebral body rotation, has a high incidence rate in modern people, and is closely related to working and living habits. The scoliosis not only affects the body shape and organ functions of the human body, but also affects the normal growth and physiological health, so that the timely and accurate correction training is very important.
However, in the process of implementing the technical scheme of the invention in the application, the technology at least has the following technical problems:
the in-process of carrying out the training of backbone joint correction lacks and carries out intelligent correction training supervision prior art, and then makes the problem feedback that can't in time accurately train correct, leads to correcting the not good technical problem of training effect.
Disclosure of Invention
This application is through providing a biological reaction formula backbone joint correction training method and device, solved prior art and in-process carrying out backbone joint correction training, lack and carry out intelligent correction training supervision, and then make the problem feedback that can't in time accurately train correct, lead to correcting the not good technical problem of training effect, reach and carry out intelligent monitoring aassessment to user's correction training, and then in time the accuracy carries out the feedback that the user corrected the training, realize improving the technological effect who corrects the training effect.
In view of the above problems, the present application provides a training method and device for correcting spinal joints by using biological feedback.
In a first aspect, the application provides a biological feedback type spine joint correction training method, which is applied to an intelligent correction training monitoring system, wherein the system is in communication connection with an image acquisition device and a biological feedback wearable device, and the method comprises the following steps: obtaining first basic information of a first user, wherein the first basic information comprises spine detection information of the first user; obtaining first matching training course information of the first user, and determining a first action position threshold according to the first matching training course information and the first basic information; acquiring images of the correction training of the first user through the image acquisition equipment to obtain a real-time acquisition result of a first image; performing action characteristic recognition on the first image real-time acquisition result, and obtaining a first correction feedback parameter according to an action characteristic recognition result and the first action position threshold; obtaining a first duration parameter according to the action characteristic identification result, and obtaining a second correction feedback parameter according to the first duration parameter; obtaining heart rate change information of the first user through the biofeedback wearable device, and obtaining a third correction feedback parameter according to the heart rate change information; performing corrective training management based on the first corrective feedback parameter, the second corrective feedback parameter, and the third corrective feedback parameter.
In another aspect, the present application also provides a bio-feedback spinal joint correction training device, the device comprising: a first obtaining unit, configured to obtain first basic information of a first user, where the first basic information includes spine detection information of the first user; a second obtaining unit, configured to obtain first matching course information of the first user, and determine a first action position threshold according to the first matching course information and the first basic information; a third obtaining unit, configured to perform image acquisition of the correction training of the first user through an image acquisition device, and obtain a real-time acquisition result of a first image; the first identification unit is used for performing action characteristic identification on the real-time acquisition result of the first image and obtaining a first correction feedback parameter according to the action characteristic identification result and the first action position threshold value; a fourth obtaining unit, configured to obtain a first duration parameter according to the motion feature recognition result, and obtain a second correction feedback parameter according to the first duration parameter; a fifth obtaining unit, configured to obtain heart rate variation information of the first user through a biofeedback wearable device, and obtain a third correction feedback parameter according to the heart rate variation information; a first management unit configured to perform correction training management based on the first correction feedback parameter, the second correction feedback parameter, and the third correction feedback parameter.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining first basic information of a first user, setting an action threshold according to the first basic information and first matching training course information of the first user, obtaining a first action position threshold according to a set result, carrying out image acquisition of a correction training process of the first user through an image acquisition device, obtaining a real-time acquisition result of a first image according to an acquisition result of the image, carrying out identification and synthesis on action characteristics of the first user according to acquisition angle parameters of each image of the first real-time image acquisition result, comparing the identification and synthesis result with the first action position threshold, obtaining a first correction feedback parameter according to a comparison result, obtaining a second correction feedback parameter according to action duration time information, obtaining heart rate change information of the user in the correction process through intelligent wearable equipment, obtaining a third correction feedback parameter according to the heart rate change information, carrying out correction training management through the first correction feedback parameter, the second correction feedback parameter and the third correction feedback parameter, achieving correction evaluation on correction of the user, monitoring and monitoring on correction of the user accurately in time, and improving the correction effect of the training.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a training method for correcting spinal joints with biological feedback according to the present application;
fig. 2 is a schematic flow chart of the method for training correction of spinal joints with biological feedback according to the present application to obtain a real-time acquisition result of the first image;
fig. 3 is a schematic flow chart of the biological feedback type spinal joint correction training method for obtaining the action feature recognition result according to the present application;
FIG. 4 is a schematic flow chart of refining and obtaining a first correction feedback parameter in a training method for correction of spinal joints using biological feedback;
FIG. 5 is a schematic structural diagram of a bio-feedback training device for spinal joint correction according to the present application;
fig. 6 is a schematic structural diagram of an electronic device according to the present application.
Description of the reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first identifying unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a first managing unit 17, an electronic device 50, a processor 51, a memory 52, an input device 53, and an output device 54.
Detailed Description
This application is through providing a training method and device are corrected to biological feedback formula backbone joint, solved prior art and in-process carrying out the backbone joint and correct the training, lack and carry out intelligent correction training supervision, and then make the problem feedback that can't in time accurately train correct, lead to correcting the not good technical problem of training effect, reach and carry out intelligent monitoring aassessment to user's correction training, and then in time the accuracy carries out the feedback that the user corrected the training, realize improving the technological effect of correcting the training effect. Embodiments of the present application are described below with reference to the accompanying drawings. As can be appreciated by those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the present application are also applicable to similar technical problems.
The terms "first," "second," and the like in the description and claims of the present application and in the foregoing drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Summary of the application
Scoliosis is an abnormal curvature of the spine. The normal spine has a posterior curvature in the shoulder and an anterior curvature in the lumbar region. Typical scoliosis includes three-dimensional spinal and rib deformities. Depending on the degree of curvature, the spine bends from the side and sometimes the vertebrae rotate slightly, resulting in imbalance in the hip or shoulder. Scoliosis is relatively common in the general population and causes include congenital, acquired or degenerative problems. The in-process of carrying out the training of backbone joint correction lacks and carries out intelligent correction training supervision prior art, and then makes the problem feedback that can't in time accurately train correct, leads to correcting the not good technical problem of training effect.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides a biological feedback type spinal joint correction training method, which is applied to an intelligent correction training monitoring system, wherein the system is in communication connection with an image acquisition device and a biological feedback wearable device, and the method comprises the following steps: the method comprises the steps of setting an action threshold value according to first basic information of a first user and first matching training course information of the first user, obtaining a first action position threshold value according to a setting result, carrying out image acquisition of a correction training process of the first user through an image acquisition device, carrying out action characteristic identification and synthesis of the first user based on acquisition angle parameters of images of a first real-time image acquisition result, comparing the identification and synthesis result with the first action position threshold value, obtaining a first correction feedback parameter according to a comparison result, obtaining a second correction feedback parameter according to action duration information, obtaining heart rate change information of the user in the correction process through intelligent wearable equipment, obtaining a third correction feedback parameter according to the heart rate change information, and carrying out correction training management through the first correction feedback parameter, the second correction feedback parameter and the third correction feedback parameter.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a method for training correction of spinal joints with biological feedback, the method is applied to an intelligent correction training monitoring system, the system is in communication connection with an image acquisition device and a biological feedback wearable device, and the method includes:
step S100: obtaining first basic information of a first user, wherein the first basic information comprises spine detection information of the first user;
step S200: obtaining first matching training course information of the first user, and determining a first action position threshold value according to the first matching training course information and the first basic information;
specifically, the intelligent correction training monitoring system is an intelligent monitoring and error correction device for correction training of a user, and corrects the correction process of the user by acquiring data of the correction training of the user and combining information of the user and a preset threshold value. The image acquisition equipment is generally a plurality of, and a plurality of image acquisition equipment set up the position different, lays through a plurality of image acquisition equipment, carries out multi-angle image acquisition and synthesis to target monitoring user, and then makes action acquisition information to the user more comprehensive, biological feedback wearing equipment is for carrying out the biological equipment of user's rhythm of the heart characteristic monitoring, just intelligence is corrected training monitoring system with image acquisition equipment biological wearing feedback communication connection can carry out mutual information interaction.
Further, the first user is a user who performs correction training, the basic information of the first user includes detection information of the first user, namely information of a scoliosis direction, a scoliosis angle and the like, and data support is provided for accurate user action threshold and monitoring evaluation in the following process by obtaining the first basic information of the first user.
Further, the first matching training course is a correction training course formulated by the first user according to the diagnosis information of the first user after the first user is diagnosed by a doctor. And setting an action position threshold value of each action according to the established first matching training course and by combining the basic information of the first user, namely the first action position threshold value. Furthermore, in order to make the assistance and positioning more accurate for the user, the user may be placed in a device comprising an upper and lower frame, an auxiliary joystick, an auxiliary air cushion, so that the user's recovery actions may be better assisted by correction and positioning assistance. The corrective exercises for the user should be progressive, so the first action position threshold is gradually adjusted as the first user continues to rehabilitate. Through the setting of the first action position threshold value, the actual exercise requirement of the first user is matched with the self condition of the user, and data support is provided for subsequent accurate monitoring and early warning.
Step S300: acquiring an image of the correction training of the first user through the image acquisition equipment to obtain a real-time acquisition result of a first image;
step S400: performing action characteristic recognition on the first image real-time acquisition result, and obtaining a first correction feedback parameter according to an action characteristic recognition result and the first action position threshold;
specifically, the position distribution of the image acquisition devices is performed according to the space where the first user is rehabilitated, that is, it is required that the acquired images of two adjacent image acquisition devices have a coincident part, and the images of the key monitoring positions can be observed by at least two image acquisition devices at the same time. And carrying out multi-angle image acquisition of the correction exercise of the first user through the distributed image acquisition equipment, and obtaining a real-time acquisition result of the first image according to the acquisition result of the image.
Furthermore, each image in the first image real-time acquisition result has a respective position identifier, a time identifier is acquired, image clustering is performed through the images with the time and position identifiers, action feature recognition is performed based on the clustering result, and the first correction feedback parameter is obtained according to deviation information between track information of the action feature and the first action position threshold value. The method comprises the steps of comparing the characteristics of real-time images of users, evaluating the action completion degree according to a comparison result and a preset action position threshold value, obtaining a first correction feedback parameter according to an evaluation result, and feeding back data for better correction training of the first user, so that the correction training of the users has better effect.
Step S500: obtaining a first duration parameter according to the action characteristic recognition result, and obtaining a second correction feedback parameter according to the first duration parameter;
step S600: obtaining heart rate change information of the first user through the biofeedback wearing equipment, and obtaining a third correction feedback parameter according to the heart rate change information;
step S700: performing corrective training management based on the first corrective feedback parameter, the second corrective feedback parameter, and the third corrective feedback parameter.
Specifically, according to the action feature recognition result, it is determined that the first user continues to operate for the duration time information after reaching the holding position of the current action, the first duration time parameter is obtained according to the accumulation of the duration time information, it is determined whether the duration time of the first user meets a predetermined requirement based on the first duration time parameter and the first matching course information, and the second correction feedback parameter is obtained according to the evaluation result.
Further, in the process that the first user keeps and adjusts the movement action, the heart rate of the first user is collected through the biofeedback wearable device, and the respiratory variation is evaluated according to the collection result of the heart rate. In the process of carrying out the spinal correction training, keep even expiration and breathing in and can promote metabolism, the action is corrected to the cooperation backbone, massages because the internal organ of scoliosis or other reasons damage, and control breathing can help first user control good mood, improves the effect of correcting the training. Monitoring according to the heart rate change of the user, mapping and feeding back the respiration condition of the first user, and further obtaining a corresponding third correction feedback parameter for the part of the first user, which does not meet the preset requirement, for respiration, so as to remind the first user of respiration.
Through first correction feedback parameter, second correction feedback parameter and third correction feedback parameter carry out the warning of first user's correction training process, when pronunciation were reminded not directly perceived enough, go back accessible display device, with the correction action fit extremely display device to more directly perceived, more clear carry out user's correction and remind, and then laid a good foundation for improving the effect of correcting the training.
Further, as shown in fig. 2, step S300 of the present application further includes:
step S310: obtaining first correction space information of the first user;
step S320: determining the distribution position of the image acquisition equipment according to the first correction space information and the first matching training course information to obtain a first distribution result;
step S330: the image acquisition equipment according to the first distribution result carries out multi-angle image acquisition of the correction training of the first user, and the acquired image has a time identifier and an equipment identifier;
step S340: and acquiring a real-time acquisition result of the first image according to a multi-angle image acquisition result.
Specifically speaking, first correction space is the space that first user corrected and taken exercise, generally speaking, first correction space is that the hospital set for perhaps the space that can take exercise in first user's own family, works as before first user takes exercise, needs to gather the relevant data in first user's exercise space, information such as light, space size, according to the information of gathering, combine the action characteristic that first matching training course information needs to take exercise carries out the determination of image acquisition device's quantity and distribution position to guarantee the stability of image acquisition result. And obtaining the first distribution result according to the distribution mode of the image acquisition devices, wherein the first distribution result comprises a coordinate system established by using a first correction space, the position where the first user exercises is taken as an origin, coordinate information of each image acquisition device is obtained, and acquisition angle parameters of each image acquisition device relative to the first user are obtained according to the coordinate information. And acquiring images of the exercise process of the first user through each image acquisition device in the first distribution result, wherein the image acquisition result of each image acquisition device is provided with a corresponding image acquisition angle identifier and an acquisition time identifier. And forming a real-time acquisition result of the first image according to the acquired multi-angle image acquisition result. And multi-angle image acquisition is carried out through the angle and time identification of image acquisition, and data support is provided for subsequent accurate user characteristic identification.
Further, as shown in fig. 3, step S400 of the present application further includes:
step S410: acquiring image acquisition position information of the first image real-time acquisition result according to the first distribution result;
step S420: according to the distribution condition of the collected position information, constructing an action characteristic identification model with position angles;
step S430: and inputting the real-time acquisition result of the first image into the action characteristic recognition model to obtain the action characteristic recognition result.
Specifically, according to the first distribution result, acquiring position information of each image acquisition device relative to the first user is obtained, image acquisition of same position monitoring in big data is carried out based on the acquired position information, an action feature identification model with a position identifier of the user is constructed according to the image acquisition result of the same position monitoring, the action feature identification model is a model for feature comparison in machine learning, images of all angles are integrated, the action feature identification models of the corresponding angles are respectively input, and identification results of all action features are obtained.
Further, according to the recognition result of the motion feature of each angle, in combination with the recognition results of the motion features of other angles at the same time, whether the motion track of the first user is consistent with a preset motion track is judged; it may also be determined whether the current motion amplitude satisfies a predetermined amplitude. And obtaining the action characteristic identification result according to the two dimensions of the action track and the action amplitude. Through the construction of the action characteristic recognition models at different angles, the recognition result of the action characteristics at each angle is more accurate, and a foundation is laid for obtaining an accurate action characteristic recognition result.
Further, as shown in fig. 4, step S400 of the present application further includes:
step S440: obtaining a spatial position recognition result of the action feature of the first user according to the action feature recognition model;
step S450: generating a correction action track of the first user according to the spatial position identification result to obtain first action track information;
step S460: obtaining a first track deviation correction parameter according to the first action track information and the first matching training course information;
step S470: obtaining a first position deviation correction parameter according to the first action track information and the first action position threshold;
step S480: and obtaining the first correction feedback parameter according to the first trajectory deviation correction parameter and the first position deviation correction parameter.
Specifically, when the user starts to exercise, if the first user needs to move the upper body from a first position to a second position, the first image real-time acquisition result includes an image acquisition result of the first user from the first position to the second position, whether the actual position of the first user is consistent at the first position is determined, and when the actual position of the first user is consistent, the motion trajectory monitoring of the first user is continuously performed, and the first motion trajectory information is obtained according to a trajectory set obtained by the monitoring.
Further, the first motion trajectory information reflects a motion standard of the first user in the correction training, that is, whether or not the motion standard occurs, the motion deviation during the correction motion, and whether or not the scoliosis remains. And acquiring a first track deviation correction parameter according to the first action track information. Further, the motion amplitude of the first user is monitored and evaluated according to the track change of the first motion track and the first motion position threshold, and the first position deviation correction parameter is obtained according to amplitude deviation information. Feedback parameters are obtained based on two dimensions of the action track and the action amplitude of the first user, so that the obtained first correction feedback parameters are more accurate, and more accurate feedback information support is provided for improving the correction training effect.
Further, step S500 of the present application further includes:
step S510: judging whether the first user has motion track deviation or not according to the motion characteristic identification result;
step S520: when the first user does not have action track deviation, identifying the action keeping time of the first user according to the action characteristic identification result, and obtaining the second correction feedback parameter according to the identification result;
step S530: performing action stability evaluation on the first user, and obtaining a fourth correction feedback parameter according to an evaluation result;
step S540: and carrying out correction training management according to the second correction feedback parameter and the fourth correction feedback parameter.
In particular, when performing the correction training supervision of the first user, the standard requirement of the movement is relatively high, because the movement with trajectory deviation is not only useless for correcting the scoliosis, but also may make the scoliosis worse, or cause other problems. Therefore, in the process of recognizing the action characteristics, firstly, the action track of the first user is recognized, when the action track of the first user is judged to meet a preset deviation threshold value, the first user is considered to have no action track deviation, at this time, the action holding time of the first user is recognized according to the action characteristic recognition result, the second correction feedback parameter is obtained according to the recognition result, the second correction feedback parameter reflects whether the action holding time of the first user reaching a target action point meets the preset time of a first matching training course in the action simulation process, and when the action holding time does not meet the preset time of the first matching training course, the second correction feedback parameter is generated.
Further, the process of performing the motion stability assessment is the assessment result of the motion stability of the first user during the whole correction training process. Generally speaking, a stability evaluation result is obtained by counting the number and degree of track anomalies and the number and degree of amplitude anomalies, and a fourth correction feedback parameter is obtained according to the stability evaluation result. And correcting the training management of the first user through the second correction feedback parameter and the fourth correction feedback parameter.
Further, step S510 of the present application further includes:
step S511: when the first user has motion track deviation, obtaining first deviation direction and deviation distance information;
step S512: and generating first real-time track deviation early warning information according to the deviation direction and the deviation distance information, and generating first real-time correction guide information according to the first deviation direction and the deviation distance information.
Specifically, when it is determined that the motion trajectory of the first user does not satisfy the predetermined deviation threshold, it is determined that there is a motion trajectory deviation in the first user, and at this time, real-time deviation correction is required. And generating first real-time track deviation early warning information according to the deviation angle, the deviation direction and the deviation distance of the first user.
Further, according to the motion action of the first user at present, a guidance deviation action image is generated by combining the relevant information of the deviation, and the guidance deviation action image is displayed through a display device, so that the first user is better guided to carry out deviation correction, and the effect of timely, accurately and clearly carrying out action early warning correction is achieved.
Further, step S800 of the present application further includes:
step S810: acquiring information of the correction feedback parameters of the first user to obtain a first acquisition result;
step S820: feeding back the first acquisition result to a second user, wherein the second user is a guiding user for correcting the first user;
step S830: and obtaining a first feedback result of the second user, and adjusting the threshold value of the first action position according to the first feedback result.
Specifically, the second user is a treating physician or a mentor teacher of the first user, the first user is periodically monitored and guided by the intelligent correction training monitoring system, data for monitoring and correcting the first user process is recorded, and a guidance parameter set of the first user is generated, that is, a first acquisition result is obtained. And feeding back the first acquisition result to the second user, analyzing and evaluating the performance of the first user by the second user in the whole correction training period of the first user, and re-adjusting information such as correction actions, action position thresholds and the like based on the analysis and evaluation result so that the training course and the monitoring threshold can be more adaptive to the physical state of the first user.
Furthermore, a first feedback result of the second user is obtained, and the first action position threshold is adjusted according to the first feedback result, so that the subsequent supervision of the correction training of the first user is more accurate.
In summary, the biological feedback spinal joint correction training method and device provided by the present application have the following technical effects:
1. the method comprises the steps of obtaining first basic information of a first user, setting an action threshold according to the first basic information and first matching training course information of the first user, obtaining a first action position threshold according to a set result, carrying out image acquisition of a correction training process of the first user through an image acquisition device, obtaining a real-time image acquisition result according to an image acquisition result, carrying out identification and synthesis on action characteristics of the first user according to acquisition angle parameters of images of the first real-time image acquisition result, comparing the identification and synthesis result with the first action position threshold, obtaining a first correction feedback parameter according to a comparison result, obtaining a second positive feedback parameter according to action duration time information, obtaining heart rate change information of the user in the correction process through intelligent wearable equipment, obtaining a third correction feedback parameter according to the heart rate change information, carrying out correction management through the first correction feedback parameter, the second correction feedback parameter and the third correction feedback parameter, achieving correction evaluation on correction of the user, carrying out intelligent monitoring on feedback of correction of the user in time, and realizing improvement of the correction effect of training.
2. Through the construction of the action characteristic recognition models at different angles, the recognition result of the action characteristics at each angle is more accurate, and a foundation is laid for obtaining an accurate action characteristic recognition result.
3. Feedback parameters are obtained based on two dimensions of the action track and the action amplitude of the first user, so that the obtained first correction feedback parameters are more accurate, and more accurate feedback information support is provided for improving the correction training effect.
Example two
Based on the same inventive concept as the biological feedback type spinal joint correction training method in the foregoing embodiment, the present invention also provides a biological feedback type spinal joint correction training device, as shown in fig. 5, the device includes:
a first obtaining unit 11, configured to obtain first basic information of a first user, where the first basic information includes spine detection information of the first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first matching course information of the first user, and determine a first action position threshold according to the first matching course information and the first basic information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform image acquisition of the first user's correction training through an image acquisition device, and obtain a real-time acquisition result of a first image;
the first identification unit 14 is configured to perform motion feature identification on the real-time first image acquisition result, and obtain a first correction feedback parameter according to the motion feature identification result and the first motion position threshold;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain a first duration parameter according to the motion feature recognition result, and obtain a second correction feedback parameter according to the first duration parameter;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to obtain heart rate variation information of the first user through a biofeedback wearable device, and obtain a third correction feedback parameter according to the heart rate variation information;
a first management unit 17, wherein the first management unit 17 is configured to perform correction training management based on the first correction feedback parameter, the second correction feedback parameter, and the third correction feedback parameter.
Further, the apparatus further comprises:
a sixth obtaining unit, configured to obtain first correction space information of the first user;
a seventh obtaining unit, configured to determine a distribution position of the image acquisition device according to the first correction space information and the first matching course information, and obtain a first distribution result;
the first acquisition unit is used for acquiring multi-angle images of the correction training of the first user according to the image acquisition equipment of the first distribution result, and the acquired images have time marks and equipment marks;
an eighth obtaining unit, configured to obtain the real-time acquisition result of the first image according to a multi-angle image acquisition result.
Further, the apparatus further comprises:
a ninth obtaining unit, configured to obtain, according to the first distribution result, image acquisition position information of the first image real-time acquisition result;
the first construction unit is used for constructing an action characteristic recognition model with a position angle according to the distribution condition of the collected position information;
a tenth obtaining unit, configured to input the real-time first image acquisition result into the motion feature recognition model, and obtain the motion feature recognition result.
An eleventh obtaining unit, configured to generate a corrective action track of the first user according to the spatial position recognition result, and obtain first action track information;
a twelfth obtaining unit, configured to obtain a first trajectory deviation correction parameter according to the first motion trajectory information and the first matching course information;
a thirteenth obtaining unit, configured to obtain a first position deviation correction parameter according to the first motion trajectory information and the first motion position threshold;
a fourteenth obtaining unit, configured to obtain the first correction feedback parameter according to the first trajectory deviation correction parameter and the first position deviation correction parameter.
Further, the apparatus further comprises:
the first judging unit is used for judging whether the first user has motion track deviation or not according to the motion characteristic identification result;
a fifteenth obtaining unit, configured to, when there is no motion trajectory deviation for the first user, perform motion holding time recognition for the first user according to the motion feature recognition result, and obtain the second correction feedback parameter according to the recognition result;
a sixteenth obtaining unit, configured to perform action stability evaluation on the first user, and obtain a fourth correction feedback parameter according to an evaluation result;
and the second management unit is used for carrying out correction training management according to the second correction feedback parameter and the fourth correction feedback parameter.
Further, the apparatus further comprises:
a seventeenth obtaining unit, configured to obtain a first deviation direction and deviation distance information when there is a deviation of an action trajectory of the first user;
and the eighteenth obtaining unit is used for generating first real-time track deviation early warning information according to the deviation direction and the deviation distance information and generating first real-time correction guide information according to the first deviation direction and the deviation distance information.
Further, the apparatus further comprises:
a nineteenth obtaining unit, configured to perform information acquisition on the correction feedback parameter of the first user, and obtain a first acquisition result;
the first feedback unit is used for feeding back the first acquisition result to a second user, wherein the second user guides the user to perform the first user correction.
A twentieth obtaining unit, configured to obtain a first feedback result of the second user, and perform the first action position threshold adjustment according to the first feedback result.
Various modifications and embodiments of a training method for correction of spinal joints in accordance with the first embodiment of fig. 1 are also applicable to a training device for correction of spinal joints in accordance with the present embodiment, and it is clear to those skilled in the art from the above detailed description of a training method for correction of spinal joints in accordance with the present embodiment that the method for training of spinal joints in accordance with the present embodiment is not described in detail herein for the sake of brevity of description.
Exemplary electronic device
The electronic device of the present application is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present application.
Based on the inventive concept of a biological feedback type spinal joint correction training method in the foregoing embodiment, the present invention also provides an electronic device, and hereinafter, the electronic device according to the present application is described with reference to fig. 6. The electronic device may be the removable device itself or a stand-alone device separate therefrom, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods as described hereinbefore.
As shown in fig. 6, the electronic device 50 includes one or more processors 51 and a memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.
The memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 51 to implement the methods of the various embodiments of the application described above and/or other desired functions.
In one example, the electronic device 50 may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The embodiment of the invention provides a biological feedback type spine joint correction training method, which is applied to an intelligent correction training monitoring system, wherein the system is in communication connection with an image acquisition device and a biological feedback wearable device, and the method comprises the following steps: obtaining first basic information of a first user, wherein the first basic information comprises spine detection information of the first user; obtaining first matching training course information of the first user, and determining a first action position threshold value according to the first matching training course information and the first basic information; acquiring an image of the correction training of the first user through the image acquisition equipment to obtain a real-time acquisition result of a first image; performing action characteristic recognition on the first image real-time acquisition result, and obtaining a first correction feedback parameter according to an action characteristic recognition result and the first action position threshold; obtaining a first duration parameter according to the action characteristic identification result, and obtaining a second correction feedback parameter according to the first duration parameter; obtaining heart rate change information of the first user through the biofeedback wearing equipment, and obtaining a third correction feedback parameter according to the heart rate change information; performing correction training management based on the first correction feedback parameter, the second correction feedback parameter, and the third correction feedback parameter. The problem of prior art in the in-process of carrying out the training is corrected to the backbone joint, lack to carry out intelligent correction training supervision, and then make the problem feedback that can't in time accurately carry out the training correct, lead to correcting the not good technical problem of training effect, reach and carry out intelligent monitoring aassessment to user's correction training, and then in time the accuracy carries out the feedback that the user corrected the training, realize improving the technical effect who corrects the training effect.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application or portions contributing to the prior art may be embodied in the form of a software product, where the computer software product is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for causing a computer device to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the present application are generated in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted from a computer-readable storage medium, which may be magnetic (e.g., floppy disks, hard disks, tapes), optical (e.g., DVDs), or semiconductor (e.g., solid State Disks (SSDs)), among others.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the present application.
Additionally, the terms "system" and "network" are often used interchangeably herein. The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
It should be understood that in this application, "B corresponding to A" means that B is associated with A, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. A biological feedback type spine joint correction training method is applied to an intelligent correction training monitoring system, the system is in communication connection with an image acquisition device and a biological feedback wearable device, and the method comprises the following steps:
obtaining first basic information of a first user, wherein the first basic information comprises spine detection information of the first user;
obtaining first matching training course information of the first user, and determining a first action position threshold value according to the first matching training course information and the first basic information;
acquiring images of the correction training of the first user through the image acquisition equipment to obtain a real-time acquisition result of a first image;
performing action characteristic recognition on the first image real-time acquisition result, and obtaining a first correction feedback parameter according to an action characteristic recognition result and the first action position threshold;
obtaining a first duration parameter according to the action characteristic identification result, and obtaining a second correction feedback parameter according to the first duration parameter;
obtaining heart rate change information of the first user through the biofeedback wearable device, and obtaining a third correction feedback parameter according to the heart rate change information;
performing correction training management based on the first correction feedback parameter, the second correction feedback parameter, and the third correction feedback parameter.
2. The method of claim 1, wherein the method comprises:
obtaining first correction space information of the first user;
determining the distribution position of the image acquisition equipment according to the first correction space information and the first matching training course information to obtain a first distribution result;
the image acquisition equipment according to the first distribution result carries out multi-angle image acquisition of the correction training of the first user, and the acquired image has a time identifier and an equipment identifier;
and acquiring a real-time acquisition result of the first image according to a multi-angle image acquisition result.
3. The method of claim 2, wherein the method comprises:
acquiring image acquisition position information of the first image real-time acquisition result according to the first distribution result;
according to the distribution condition of the collected position information, constructing an action characteristic identification model with position angles;
and inputting the real-time acquisition result of the first image into the action characteristic recognition model to obtain the action characteristic recognition result.
4. The method of claim 3, wherein the method comprises:
according to the action feature recognition model, obtaining a space position recognition result of the action feature of the first user;
generating a correction action track of the first user according to the spatial position recognition result to obtain first action track information;
obtaining a first track deviation correction parameter according to the first action track information and the first matching training course information;
obtaining a first position deviation correction parameter according to the first action track information and the first action position threshold;
and obtaining the first correction feedback parameter according to the first trajectory deviation correction parameter and the first position deviation correction parameter.
5. The method of claim 4, wherein the method comprises:
judging whether the first user has motion track deviation or not according to the motion characteristic identification result;
when the first user does not have action track deviation, identifying the action keeping time of the first user according to the action characteristic identification result, and obtaining the second correction feedback parameter according to the identification result;
performing action stability evaluation on the first user, and obtaining a fourth correction feedback parameter according to an evaluation result;
and carrying out correction training management according to the second correction feedback parameter and the fourth correction feedback parameter.
6. The method of claim 5, wherein the method comprises:
when the first user has motion track deviation, obtaining first deviation direction and deviation distance information;
and generating first real-time track deviation early warning information according to the deviation direction and the deviation distance information, and generating first real-time correction guidance information according to the first deviation direction and the deviation distance information.
7. The method of claim 1, wherein the method comprises:
acquiring information of the correction feedback parameters of the first user to obtain a first acquisition result;
feeding back the first acquisition result to a second user, wherein the second user is a guiding user for correcting the first user;
and obtaining a first feedback result of the second user, and adjusting the threshold value of the first action position according to the first feedback result.
8. A bio-feedback spinal joint correction training device, the device comprising:
a first obtaining unit, configured to obtain first basic information of a first user, where the first basic information includes spine detection information of the first user;
a second obtaining unit, configured to obtain first matching course information of the first user, and determine a first action position threshold according to the first matching course information and the first basic information;
the third obtaining unit is used for carrying out image acquisition of the correction training of the first user through image acquisition equipment to obtain a first image real-time acquisition result;
the first identification unit is used for performing action characteristic identification on the first image real-time acquisition result and obtaining a first correction feedback parameter according to the action characteristic identification result and the first action position threshold value;
a fourth obtaining unit, configured to obtain a first duration parameter according to the motion feature recognition result, and obtain a second correction feedback parameter according to the first duration parameter;
a fifth obtaining unit, configured to obtain heart rate variation information of the first user through a biofeedback wearable device, and obtain a third correction feedback parameter according to the heart rate variation information;
a first management unit, configured to perform correction training management based on the first correction feedback parameter, the second correction feedback parameter, and the third correction feedback parameter.
9. An electronic device comprising a processor and a memory; the memory is used for storing; the processor is used for executing the method of any one of claims 1 to 7 through calling.
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