CN114758747A - Big data based remote rehabilitation guidance system and method - Google Patents
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Abstract
The invention discloses a remote rehabilitation guidance system and a remote rehabilitation guidance method based on big data; belonging to the technical field of remote rehabilitation guidance; different instructors and rehabilitators are respectively subjected to portrait in the early stage, so that instructors with different abilities can perform portrait analysis on rehabilitators with different states to obtain an instructor coefficient and a rehabilitative coefficient, the instructor effect can be evaluated and graded based on the instructor coefficient, instructors with different grades can perform corresponding rehabilitation training on rehabilitators with different degrees of severity, initial states of the rehabilitators during training can be evaluated and graded based on the rehabilitative coefficient, and rehabilitators with different grades can be matched with instructors with different instructors; the invention solves the technical problem that the whole guidance rehabilitation effect between a director and a rehabilitee is poor because the director and the rehabilitee can not be dynamically adjusted according to the guidance condition of the director and the training condition of the rehabilitee in the existing scheme.
Description
Technical Field
The invention relates to the technical field of remote rehabilitation guidance, in particular to a remote rehabilitation guidance system and method based on big data.
Background
The rehabilitation guidance is used for accelerating the recovery process of the injured person, preventing and relieving the degree of the residual dysfunction, and enabling the injured person to participate in the return to the society as much as possible.
When the existing remote rehabilitation guidance scheme is implemented, images of different instructors and rehabilitators are not taken in the early stage, and the guidance effect of the instructors is not evaluated and graded, so that the instructors with different grades cannot perform corresponding rehabilitation training on the rehabilitators with different degrees of severity; and the guidance of the instructor and the training of the rehabilitee are not dynamically adjusted according to the guidance condition of the instructor and the training condition of the rehabilitee, so that the technical problem of poor overall guidance rehabilitation effect between the instructor and the rehabilitee is solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a remote rehabilitation guidance system and method based on big data, which are used for solving the technical problem that the overall guidance rehabilitation effect between a instructor and a rehabilitee is poor because the guidance of the instructor and the training of the rehabilitee cannot be dynamically adjusted according to the guidance condition of the instructor and the training condition of the rehabilitee in the existing scheme.
The purpose of the invention can be realized by the following technical scheme:
The remote rehabilitation guidance system based on big data comprises a personnel statistics module, a monitoring analysis module and an adjustment module;
the personnel counting module is used for counting first information of a mentor implementing remote rehabilitation guidance and second information of a rehabilitee needing remote rehabilitation guidance; the first information comprises the guide type, the number of the guide people, the guide duration and the favorable comment number of the guide; the second information comprises the age, rehabilitation type and training grade of the rehabilitee;
the monitoring analysis module comprises a personnel analysis unit and a behavior analysis unit;
the personnel analysis unit is used for carrying out data processing and training on the collected first information of the instructor and the second information of the rehabilitee to obtain an instruction analysis set containing an instruction coefficient and a rehabilitation analysis set containing a rehabilitation coefficient;
the behavior analysis unit is used for monitoring and evaluating the guiding behavior of the instructor and the rehabilitation training of the rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitation monitoring set containing a rehabilitation evaluation value;
the adjusting module is used for combining the guidance monitoring set and the rehabilitation monitoring set according to the guidance analysis set and the rehabilitation analysis set to dynamically adjust the guidance of the instructor and the training of the rehabilitee in a preset monitoring period.
Further, the specific steps of processing and training the collected first information of the instructor comprise:
acquiring the guidance type, the number of guidance people, the guidance duration and the favorable comment number of the instructor in the first information;
matching the guidance type with a guidance type table to obtain a corresponding guidance weight, and marking the value as A1; the system comprises a guidance type table, a guidance weight table and a display table, wherein the guidance type table is composed of a plurality of guidance types and corresponding guidance weights thereof, and different guidance types are preset with different guidance weights;
respectively extracting the numerical values of the number of the instructors, the instruction duration and the number of the praise and marking the numerical values as A2, A3 and A4; wherein the unit of the instruction duration is hour;
the guide weight, the guide number, the guide duration and the favorable score of the mark form guide processing information;
normalizing each item of data marked in the guidance processing information, taking values, and training through a guidance function to obtain a guidance coefficient ZDX;
marking the instructor corresponding to the guidance coefficient smaller than the guidance threshold value as a first instructor; marking the instructor corresponding to the guidance coefficient not less than the guidance threshold as a second instructor; the guiding effect of the second instructor is higher than that of the first instructor;
the guidance coefficients form a guidance analysis set with the first and second instructors.
Further, the guidance function is ZDX ═ a1 [ (a1 × a2+ a2 × A3+ A3 × a4)/(a1+ a2+ A3) ], and a1, a2 and A3 are different scaling factors and have values in the range of (0, 1).
Further, the specific steps of carrying out data processing and training on the acquired second information of the rehabilitee comprise:
acquiring the age, the rehabilitation type and the training grade of the rehabilitee in the second information;
matching the rehabilitation type with the guidance type table to obtain corresponding rehabilitation weight and marking the value as B1;
extracting the value of the age and marking as B2;
acquiring training weights associated with the training levels and marking the training weights as B3;
the marked rehabilitation weight, the age and the training weight form rehabilitation processing information;
carrying out normalization processing on various items of data marked in the rehabilitation processing information, taking values, and training through a rehabilitation function to obtain a rehabilitation coefficient KFX;
marking the rehabilitee corresponding to the rehabilitation coefficient smaller than the rehabilitation threshold as a first rehabilitee; marking the rehabilitee not less than the rehabilitation coefficient as a second rehabilitee; the training difficulty of the second rehabilitee is higher than that of the first rehabilitee;
the rehabilitation factor and the first rehabilitee and the second rehabilitee form a rehabilitation analysis set.
Further, the rehabilitation function is KFX ═ B1 × B1+ B2 × B2+ B3 × B3, and B1, B2, and B3 are different proportionality coefficients and have a value range of (0, 10).
Further, the specific steps of monitoring and evaluating the guiding behavior of the guider comprise:
recording the whole process of the voice guidance of the instructor to obtain guidance recording data;
performing text conversion on the guide recording data to obtain guide text data;
matching the guidance text data with a pre-constructed project guide table and a behavior evaluation table to obtain project matching data and behavior evaluation data; the item matching data and the behavior evaluation data form a guide matching set of a guide;
extracting the total item matching number in the item matching data and the total behavior evaluation number in the behavior evaluation data, and respectively taking values to mark as XP and YP;
carrying out normalization processing on various marked data and training through a guide evaluation function to obtain a guide evaluation value ZP; the guideline assessment function is ZP ═ a1 [ c1 (XP/XP0) + c2 (YP/YP0) ]; c1 and c2 are different proportionality coefficients and have value ranges of (0, 5); XP0 is the total number of item phrases in the item guide table; YP0 is the total number of evaluation phrases in the behavior evaluation table;
setting the guidance corresponding to the guidance evaluation value which is not greater than the guidance evaluation threshold value as the guidance not reaching the standard, and adding one to the times of the guidance not reaching the standard;
Setting guidance corresponding to the guidance evaluation value smaller than the guidance evaluation threshold value as standard-reaching guidance, and adding one to the number of times of the standard-reaching guidance;
counting the number of non-standard guidance total times and the number of standard guidance total times within the monitoring period to obtain guidance statistical data; and the guidance evaluation value and the guidance statistical data form a guidance monitoring set.
Further, the specific steps of monitoring and evaluating the rehabilitation training of the rehabilitee comprise:
recording the whole process of the rehabilitation action training to obtain training video data;
acquiring frame images in training video data and arranging and combining the frame images according to a time sequence to obtain video splitting data;
extracting action characteristics of frame images in video splitting data based on an image recognition algorithm;
matching the action characteristics with a pre-constructed sample action table, acquiring the maximum overlapping area of the action characteristics and the action characteristics of the sample in the sample action set, and marking the value as CMi, wherein i is 1, 2, 3. n is the total number of training actions; n is a positive integer;
acquiring training time and marking the training time as XSi; the unit of the training time is minutes;
carrying out normalization processing on various marked data and training through a rehabilitation evaluation function to obtain a rehabilitation evaluation value PG; the rehabilitation evaluation function is
d1 and d2 are different proportionality coefficients and have value ranges of (0, 10); CMi0 is the standard overlapping area corresponding to different training items, XSi0 is the standard training duration corresponding to different training items, and the unit is minutes;
setting the rehabilitation training corresponding to the rehabilitation evaluation value which is not greater than the rehabilitation evaluation threshold value as non-standard training, and adding one to the times of the non-standard training;
setting the rehabilitation training corresponding to the rehabilitation evaluation value larger than the rehabilitation evaluation threshold value as standard-reaching training, and adding one to the times of the standard-reaching training;
counting the total number of times of non-standard training and the total number of times of standard training within a monitoring period to obtain rehabilitation statistical data; and the rehabilitation evaluation value and the rehabilitation statistical data form a rehabilitation monitoring set.
Further, the specific steps of dynamically adjusting the guidance of the instructor include:
performing overall evaluation on the guidance of the instructor according to the guidance analysis set and the guidance monitoring set in the monitoring period; acquiring and analyzing the ratio of the total times of non-standard guidance and the total times of standard guidance in the guidance monitoring set, and if the ratio is greater than k, judging that the guidance effect of a guide is excellent and generating a first maintenance instruction; k is a real number greater than zero;
if the ratio is not greater than k, judging that the guiding effect of the instructor is not good and generating a first adjusting instruction;
When the instructor corresponds to a first instructor and a second instructor for instructing the monitoring set, the instructor keeps the instruction identity of the instructor unchanged;
and stopping the guidance of the first instructor and training when the instructor corresponds to the first instructor and the second instructor for guiding the monitoring set and corresponds to the first adjustment instruction, and adjusting the second instructor to be the first instructor.
Further, the specific steps of dynamically adjusting the training of the rehabilitee include:
performing integral evaluation on the training of the rehabilitee according to the rehabilitation analysis set and the rehabilitation monitoring set in the monitoring period; acquiring and analyzing the ratio of the total times of non-standard training and the total times of standard training in the rehabilitation monitoring set, and if the ratio is greater than p, judging that the training effect of a rehabilitee is excellent and generating a second maintenance instruction; p is a real number greater than zero;
if the ratio is not greater than p, judging that the training effect of the rehabilitee is not good and generating a second adjusting instruction;
when the first rehabilitee and the second rehabilitee which correspond to the rehabilitation monitoring set both correspond to the second maintenance instruction, keeping the training intensity of the rehabilitee unchanged;
and when the rehabilitee corresponds to the first rehabilitee and the second rehabilitee of the rehabilitation monitoring set, stopping the remote rehabilitation training of the first rehabilitee based on the big data technology and performing text guidance, and adjusting the second rehabilitee to the first rehabilitee to perform low-difficulty training.
The big data-based remote rehabilitation guidance method comprises the following steps:
the method comprises the following steps: counting first information of a mentor implementing remote rehabilitation guidance and second information of a rehabilitee needing the remote rehabilitation guidance; the first information comprises the guide type, the number of the guide people, the guide duration and the number of the favorable comments of the guide; the second information comprises the age, rehabilitation type and training grade of the rehabilitee;
step two: carrying out data processing and training on the collected first information of the instructor and the second information of the rehabilitee to obtain an instruction analysis set containing the instruction coefficient and a rehabilitation analysis set containing the rehabilitation coefficient;
step three: monitoring and evaluating the guiding behaviors of the instructor and the rehabilitation training of the rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitation monitoring set containing a rehabilitation evaluation value;
step four: and combining the guidance monitoring set and the rehabilitation monitoring set according to the guidance analysis set and the rehabilitation analysis set to dynamically adjust the guidance of the instructor and the training of the rehabilitee in a preset monitoring period.
Compared with the prior art, the invention has the beneficial effects that:
the invention respectively figures different instructors and rehabilitators in the early stage, so that the instructors with different abilities can perform figure analysis on the rehabilitators with different states to obtain the guidance coefficient and the rehabilitation coefficient, the guidance effect of the instructors can be evaluated and graded based on the guidance coefficient, the instructors with different grades can perform corresponding rehabilitation training on the rehabilitators with different severity degrees, the initial state of the rehabilitators during training can be evaluated and graded based on the rehabilitation coefficient, and the rehabilitators with different grades can be matched with the instructors with different guidance abilities; meanwhile, data acquisition and analysis are carried out on the guiding process of the instructor and the training process of the rehabilitee, so that whether the guiding state of the instructor is qualified or not can be judged, and the effect of monitoring and evaluating the guidance of the instructor is achieved; the training state of the rehabilitee is evaluated so as to dynamically adjust the training intensity, and the corresponding instructors can be matched for guidance, so that different instructors can perform differential guidance training on different rehabilitees, and the overall effect of the remote rehabilitation guidance based on big data is effectively improved.
Drawings
Fig. 1 is a schematic block diagram of a big data-based remote rehabilitation guidance system according to the present invention.
Fig. 2 is a schematic flow chart of a big data-based remote rehabilitation guidance method according to the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or restrictive of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1, a schematic block diagram of a big data-based remote rehabilitation guidance system according to an embodiment of the present invention is shown. In the embodiment of the present invention, the big data is also called huge data, which means that the size of the related data is too large to be captured, managed, processed and organized into information for helping the user to more actively achieve the purpose through the mainstream software tools in a reasonable time.
The embodiment of the invention monitors and analyzes the guidance of the instructor and the training of the rehabilitee based on the big data, and adjusts the guidance progress of the instructor and the training progress of the rehabilitee, so that the instructors with different guidance effects are dynamically matched with the rehabilitee with different rehabilitation degrees, and the overall effects of remote rehabilitation and guidance are improved.
The remote rehabilitation guidance system based on big data comprises a personnel statistics module, a monitoring analysis module and an adjustment module;
the personnel counting module is used for counting first information of a mentor implementing remote rehabilitation guidance and second information of a rehabilitee needing the remote rehabilitation guidance;
the first information comprises the guide type, the number of the guide people, the guide duration and the favorable comment number of the guide; the second information comprises the age, rehabilitation type and training grade of the rehabilitee;
the monitoring analysis module comprises a personnel analysis unit and a behavior analysis unit;
the personnel analysis unit is used for carrying out data processing and training on the collected first information of the instructor and the second information of the rehabilitee to obtain an instruction analysis set containing the instruction coefficient and a rehabilitation analysis set containing the rehabilitation coefficient;
the specific steps of processing and training the collected first information of the instructor comprise:
Acquiring the guidance type, the number of guidance people, the guidance duration and the favorable comment number of the instructor in the first information;
matching the guidance type with a guidance type table to obtain corresponding guidance weight, and marking the value as A1; it should be noted that the guidance type table is composed of a plurality of guidance types and corresponding guidance weights, and different guidance types are preset with different guidance weights;
it should be noted that the purpose of presetting different instruction weights is to digitally process areas which different instructors are good at and instruction types so as to combine data of different aspects to perform overall evaluation on the instructors; the type of guidance may be based on existing guidance big data acquisition; the guidance types include but are not limited to postoperative action guidance and congenital disease action guidance, and the guidance difficulty of different guidance types is different, so that digital processing is required;
respectively extracting the numerical values of the number of the instructors, the instruction duration and the number of the good scores, and marking the numerical values as A2, A3 and A4; wherein the unit of the instruction duration is hour;
the guide weight, the guide number, the guide duration and the favorable score of the mark form guide processing information;
normalizing each item of data marked in the guidance processing information, taking values, and training through a guidance function to obtain a guidance coefficient ZDX;
The guiding function is ZDX ═ a1 ═ a1 × a2+ a2 × A3+ A3 × a4)/(a1+ a2+ A3) ];
a1, a2 and a3 are different proportional coefficients, a1 can be 0.352, a2 can be 0.684, and a3 can be 0.973;
matching and analyzing the guidance coefficient and a preset guidance threshold value; the mentoring threshold may be set based on all mentor-guided big data;
marking the instructor corresponding to the instruction coefficient smaller than the instruction threshold value as a first instructor;
marking the instructor corresponding to the instruction coefficient not less than the instruction threshold value as a second instructor; wherein the coaching effect of the second coaching person is higher than the coaching effect of the first coaching person;
the guideline coefficients form a guideline analysis set with the first and second guidelines.
It should be noted that the guidance coefficient is a numerical value for integrally evaluating the guidance ability of each item of data guided by the instructor; evaluating and grading the guiding effect of the instructor based on the guiding coefficient, so that instructors with different grades can perform corresponding rehabilitation training on rehabilitators with different degrees of severity; compared with the undifferentiated guidance and training in the existing scheme, the embodiment of the invention can realize a more efficient rehabilitation training effect;
The specific steps of carrying out data processing and training on the acquired second information of the rehabilitee comprise:
acquiring the age, the rehabilitation type and the training grade of the rehabilitee in the second information;
matching the rehabilitation type with the guidance type table to obtain corresponding rehabilitation weight and marking the value as B1; it should be noted that the guidance type table is composed of a plurality of guidance types and corresponding rehabilitation weights thereof, and different guidance types are preset with a corresponding rehabilitation weight so as to perform training calculation on different guidance types;
extracting the value of the age and marking the value as B2;
acquiring training weights associated with the training levels and marking the training weights as B3;
the training grades can comprise three grades, namely a first training grade, a second training grade and a third training grade, corresponding training weight values are sequentially increased, and the training requirements of the training grades are increased progressively; the training weight corresponding to the training grade can be preset;
the marked rehabilitation weight, the age and the training weight form rehabilitation processing information;
it should be noted that training receiving abilities of different ages are different and recovery speeds of rehabilitation are also different, different rehabilitation types need to be matched with corresponding training types, training grades are used for performing training in different degrees, and meanwhile, the rehabilitation states of rehabilitators can be represented;
Carrying out normalization processing on each item of data marked in the rehabilitation processing information, taking values, and training through a rehabilitation function to obtain a rehabilitation coefficient KFX;
the rehabilitation function is KFX ═ B1 × B1+ B2 × B2+ B3 × B3;
b1, b2 and b3 are different proportional coefficients, a1 can be 3.625, a2 can be 2.351, and a3 can be 1.967;
matching and analyzing the rehabilitation coefficient and a preset rehabilitation threshold value;
marking the rehabilitee corresponding to the rehabilitation coefficient smaller than the rehabilitation threshold as a first rehabilitee; marking the recovered person not less than the recovery coefficient as a second recovered person; the training difficulty of the second rehabilitee is higher than that of the first rehabilitee;
the rehabilitation factor and the first and second rehabilitators form a rehabilitation analysis set.
It should be noted that the rehabilitation coefficient is a numerical value for integrally evaluating the initial state of the rehabilitee during rehabilitation training by combining the training data of the rehabilitee; the initial state of the rehabilitee during training can be evaluated and graded based on the rehabilitation coefficient, so that the rehabilitees in different grades can be matched with instructors with different guiding abilities, not only can the different rehabilitees be trained in a targeted manner, but also the training effects of the instructors with different abilities can be improved.
The behavior analysis unit is used for monitoring and evaluating the guiding behaviors of the instructor and the rehabilitation training of the rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitation monitoring set containing a rehabilitation evaluation value;
the specific steps of monitoring and evaluating the guiding behavior of the guider comprise:
recording the whole process of the voice guidance of the instructor to obtain guidance recording data;
performing text conversion on the guide recording data to obtain guide text data; the technology of converting the text by voice is the conventional technical means, and the specific steps are not described herein;
matching the guidance text data with a pre-constructed project guide table and a behavior evaluation table to obtain project matching data and behavior evaluation data; the item matching data and the behavior evaluation data form a guide matching set of a guide;
it is worth noting that the project guide table is composed of training contents corresponding to a plurality of different training types, the different training types correspond to a complete training process, and the project guide table plays a role in monitoring and evaluating the guide steps of a director; the project guide table and the behavior evaluation table can be obtained based on the existing guide big data; the behavior evaluation table is composed of a plurality of phrases of personal care, including but not limited to 'true stick', 'you are the best stick', 'refuel', 'continuous effort' and the like, and plays a role in monitoring and evaluating the personal care aspect of a director;
Extracting the total item matching number in the item matching data and the total behavior evaluation number in the behavior evaluation data, and respectively taking values to mark as XP and YP;
carrying out normalization processing on each item of marked data and training through a guidance evaluation function to obtain a guidance evaluation value ZP; the guideline assessment function is ZP ═ a1 [ [ c 1: (XP/XP0) + c2 [ (YP/YP0) ];
c1 and c2 are different proportional coefficients, c1 can be 0.467, and c2 can be 0.533; XP0 is the total number of term phrases in the term guide sheet; YP0 is the total number of evaluation phrases in the behavior evaluation table;
setting the guidance corresponding to the guidance evaluation value which is not greater than the guidance evaluation threshold value as the guidance not reaching the standard, and adding one to the times of the guidance not reaching the standard;
setting the guidance corresponding to the guidance evaluation value smaller than the guidance evaluation threshold value as standard-reaching guidance, and adding one to the number of times of the standard-reaching guidance;
counting the number of non-standard guidance total times and the number of standard guidance total times within the monitoring period to obtain guidance statistical data; and the guidance evaluation value and the guidance statistical data form a guidance monitoring set.
It should be noted that, in the embodiment of the present invention, the guidance evaluation value is a value that evaluates the guidance state of the instructor by associating various items of data during training; whether the guiding state of the guiding person is qualified or not can be judged based on the guiding evaluation value, and the function of monitoring and evaluating the guiding of the guiding person is achieved.
The specific steps of monitoring and evaluating the rehabilitation training of the rehabilitee comprise:
recording the whole process of the rehabilitation action training to obtain training video data;
acquiring frame images in training video data and arranging and combining the frame images according to a time sequence to obtain video splitting data;
extracting action characteristics of frame images in video splitting data based on an image recognition algorithm;
matching the action characteristics with a pre-constructed sample action table, acquiring the maximum overlapping area of the action characteristics and the action characteristics of the sample in the sample action set, and marking the value as CMi, wherein i is 1, 2, 3. n is the total number of training actions; n is a positive integer;
acquiring training time and marking the training time as XSi; the unit of the training time is minutes;
it should be noted that the rehabilitation guidance in the embodiment of the present invention refers to guidance in terms of actions for recovering the action ability of the disabled, the image recognition algorithm may be an HOG feature algorithm, the pre-constructed sample action table is composed of standard person outlines corresponding to a plurality of training items, when an action of the disabled is matched with the standard person outline of the corresponding item during training, the training action standard of the disabled is determined, the matching is achieved according to the acquired overlapping area of the action features of the disabled and the standard person outlines, and the maximum overlapping area in the action matching of the disabled is subjected to matching analysis.
Carrying out normalization processing on various marked data and training through a rehabilitation evaluation function to obtain a rehabilitation evaluation value PG; the rehabilitation evaluation function is
d1 and d2 are different proportionality coefficients, d1 can be 1.268, and d2 can be 0.634; CMi0 is the standard overlap area corresponding to different training items, and XSi0 is the standard training duration corresponding to different training items, and the unit is minutes.
It should be noted that the rehabilitation assessment value is a numerical value for integrally assessing the training state of a rehabilitee by connecting the action characteristic data of the rehabilitee during training with the training duration; the training state of the rehabilitee can be evaluated based on the rehabilitation evaluation value so as to dynamically adjust the training intensity, and meanwhile, the training can be guided by matching with corresponding instructors, so that different instructors can perform differential guidance training on different rehabilitees, and the embodiment of the invention is different from the undifferentiated guidance and the no dynamic adjustment in the existing scheme, and can realize a more efficient guidance effect and a more efficient training effect;
the formulas in the embodiment of the invention are all a formula which removes dimensions, takes the numerical value of the dimension to calculate, and obtains the closest real situation by acquiring a large amount of data and carrying out software simulation, wherein the proportionality coefficient and the preset threshold value in the formula are set by technicians in the field according to the actual situation or are obtained by simulating a large amount of data;
Acquiring a rehabilitation evaluation threshold corresponding to the rehabilitation evaluation value;
setting the rehabilitation training corresponding to the rehabilitation evaluation value which is not greater than the rehabilitation evaluation threshold value as non-standard training, and adding one to the times of the non-standard training;
setting the rehabilitation training corresponding to the rehabilitation evaluation value larger than the rehabilitation evaluation threshold value as standard-reaching training, and adding one to the times of the standard-reaching training;
counting the total times of non-standard training and the total times of standard training within the monitoring period to obtain rehabilitation statistical data; and the rehabilitation evaluation value and the rehabilitation statistical data form a rehabilitation monitoring set.
The adjusting module is used for integrating the guidance monitoring set and the rehabilitation monitoring set according to the guidance analysis set and the rehabilitation analysis set to dynamically adjust the guidance of the instructor and the training of the rehabilitee in a preset monitoring period;
the method comprises the following specific steps of dynamically adjusting the guidance of a mentor:
performing overall evaluation on the guidance of the instructor according to the guidance analysis set and the guidance monitoring set in the monitoring period; acquiring and analyzing the ratio of the total times of non-standard guidance and the total times of standard guidance in the guidance monitoring set, and if the ratio is greater than k, judging that the guidance effect of a guide is excellent and generating a first maintenance instruction; k is a real number greater than zero; the monitoring period can be two weeks, namely, the analysis and evaluation are carried out on the guiding state of the guiding person every two weeks;
If the ratio is not greater than k, judging that the guiding effect of the guider is not good and generating a first adjusting instruction;
when the instructor corresponds to a first instructor and a second instructor for instructing the monitoring set, the instructor keeps the instruction identity of the instructor unchanged;
when the instructor corresponds to the first instructor and the second instructor in the instruction monitoring set, the instructor stops the instruction of the first instructor and trains the first instructor, and the second instructor is adjusted to be the first instructor.
The specific steps of dynamically adjusting the training of the rehabilitee include:
performing overall evaluation on the training of the rehabilitee according to the rehabilitation analysis set and the rehabilitation monitoring set in the monitoring period; acquiring and analyzing the ratio of the total times of non-standard training and the total times of standard training in the rehabilitation monitoring set, and if the ratio is greater than p, judging that the training effect of a rehabilitee is excellent and generating a second maintenance instruction; p is a real number greater than zero;
if the ratio is not greater than p, judging that the training effect of the rehabilitee is not good and generating a second adjusting instruction;
when the first rehabilitee and the second rehabilitee which correspond to the rehabilitation monitoring set both correspond to the second maintenance instruction, keeping the training intensity of the rehabilitee unchanged;
And when the rehabilitee corresponds to the first rehabilitee and the second rehabilitee of the rehabilitation monitoring set, stopping the remote rehabilitation training of the first rehabilitee based on the big data technology and performing text guidance, and adjusting the second rehabilitee to the first rehabilitee to perform low-difficulty training.
It should be noted that, in the embodiment of the present invention, a monitoring period is set to periodically evaluate the guiding state of the instructor and the training state of the rehabilitee, and the guiding of the instructor and the training of the rehabilitee are dynamically adjusted according to the evaluation result, so that instructors with different abilities can dynamically maintain an efficient guiding effect, and rehabilitees with different states can also dynamically maintain adaptive training intensity, thereby effectively improving the overall effect of remote rehabilitation guidance.
Fig. 2 is a schematic flow chart of a big data-based remote rehabilitation guidance method according to an embodiment of the present invention. In this embodiment, the method for remote rehabilitation guidance based on big data includes the following specific steps:
the method comprises the following steps: counting first information of a mentor implementing remote rehabilitation guidance and second information of a rehabilitee needing the remote rehabilitation guidance; the first information comprises the guide type, the number of the guide people, the guide duration and the number of the favorable comments of the guide; the second information comprises the age, rehabilitation type and training grade of the rehabilitee;
Step two: carrying out data processing and training on the acquired first information of the instructor and the acquired second information of the rehabilitee to obtain an instruction analysis set containing an instruction coefficient and a rehabilitation analysis set containing a rehabilitation coefficient;
step three: monitoring and evaluating the guiding behavior of a guiding person and the rehabilitation training of a rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitation monitoring set containing a rehabilitation evaluation value, wherein the guiding evaluation value is a numerical value for evaluating the guiding state of the guiding person by linking various data of the guiding person during training, and the rehabilitation evaluation value is a numerical value for integrally evaluating the training state of the rehabilitee by linking action characteristic data of the rehabilitee during training with the training duration;
step four: the guidance of the instructor and the training of the rehabilitee are dynamically adjusted in a preset monitoring period according to the guidance analysis set and the rehabilitation analysis set, so that the instructors with different abilities can dynamically keep an efficient guidance effect, and the rehabilitees in different states can also dynamically keep adaptive training intensity.
In the several embodiments provided in the present invention, it should be understood that modules described as separate components may or may not be physically separate, and components displayed as modules may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Claims (9)
1. The remote rehabilitation guidance system based on big data comprises a monitoring analysis module and an adjustment module, and is characterized in that the monitoring analysis module comprises a personnel analysis unit and a behavior analysis unit;
a person analysis unit: carrying out data processing and training on the collected first information of the instructor and the second information of the rehabilitee to obtain an instruction analysis set containing the instruction coefficient and a rehabilitation analysis set containing the rehabilitation coefficient;
a behavior analysis unit: monitoring and evaluating the guiding behaviors of the instructor and the rehabilitation training of the rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitation monitoring set containing a rehabilitation evaluation value;
An adjustment module: and combining the guidance monitoring set and the rehabilitation monitoring set according to the guidance analysis set and the rehabilitation analysis set to dynamically adjust the guidance of the instructor and the training of the rehabilitee in a preset monitoring period.
2. The big data-based tele-rehabilitation coaching system of claim 1, wherein the specific steps of processing and training the collected mentor's first information include:
acquiring the guidance type, the number of guidance people, the guidance duration and the favorable comment number of the instructor in the first information; acquiring corresponding guidance weight and value marking according to the guidance type; respectively extracting and marking the number of the instructors, the instruction duration and the number of the favorable comments; carrying out normalization processing on various data marked in the guidance processing information and carrying out vertical training to obtain a guidance coefficient; acquiring a first instructor and a second instructor according to the instruction coefficient; the guidance coefficients form a guidance analysis set with the first and second instructors.
3. The big-data based remote rehabilitation guidance system according to claim 2, wherein the specific steps of data processing and training the collected second information of the rehabilitee comprise:
acquiring the age, the rehabilitation type and the training grade of the rehabilitee in the second information; matching the rehabilitation type with the guidance type table to obtain corresponding rehabilitation weight and marking the value; extracting and marking a numerical value of the age; acquiring training weights associated with training levels and marking the training weights; carrying out normalization processing and vertical training on various data marked in the rehabilitation processing information to obtain a rehabilitation coefficient; acquiring a first rehabilitee and a second rehabilitee according to the rehabilitation coefficient; the training difficulty of the second rehabilitee is higher than that of the first rehabilitee; the rehabilitation factor and the first rehabilitee and the second rehabilitee form a rehabilitation analysis set.
4. The big data-based tele-rehabilitation coaching system of claim 3, wherein the steps of monitoring and evaluating coaching behavior of the mentor comprise:
recording the whole process of the voice guidance of the instructor to obtain guidance recording data; performing text conversion on the guide recording data to obtain guide text data; matching the guide text data with a pre-constructed project guide table and a pre-constructed behavior evaluation table to obtain project matching data and behavior evaluation data; the project matching data and the behavior evaluation data form a guidance matching set of the instructor;
extracting the total item matching number in the item matching data and the total behavior evaluation number in the behavior evaluation data, and respectively evaluating and marking; carrying out normalization processing on various marked data and carrying out vertical training to obtain a guiding evaluation value; setting guidance corresponding to the guidance evaluation value which is not greater than the guidance evaluation threshold value as non-standard guidance, and adding one to the number of times of the non-standard guidance; setting guidance corresponding to the guidance evaluation value smaller than the guidance evaluation threshold value as standard-reaching guidance, and adding one to the number of times of the standard-reaching guidance;
counting the number of non-standard guidance total times and the number of standard guidance total times within the monitoring period to obtain guidance statistical data; and the guidance evaluation value and the guidance statistical data form a guidance monitoring set.
5. The big-data-based remote rehabilitation guidance system according to claim 4, wherein the specific steps of monitoring and evaluating the rehabilitation training of the rehabilitee comprise:
recording the whole process of the rehabilitation action training to obtain training video data; acquiring frame images in training video data and arranging and combining the frame images according to a time sequence to obtain video splitting data; extracting action characteristics of frame images in video splitting data based on an image recognition algorithm;
matching the action characteristics with a pre-constructed sample action table, acquiring the maximum overlapping area of the action characteristics and the action characteristics of the sample in the sample action set, and marking the maximum overlapping area; acquiring training duration and marking values; carrying out normalization processing on various marked data and carrying out vertical training to obtain a rehabilitation evaluation value; setting the rehabilitation training corresponding to the rehabilitation evaluation value which is not greater than the rehabilitation evaluation threshold value as non-standard training, and adding one to the times of the non-standard training; setting the rehabilitation training corresponding to the rehabilitation evaluation value larger than the rehabilitation evaluation threshold value as standard-reaching training, and adding one to the times of the standard-reaching training;
counting the total number of times of non-standard training and the total number of times of standard training within a monitoring period to obtain rehabilitation statistical data; and the rehabilitation evaluation value and the rehabilitation statistical data form a rehabilitation monitoring set.
6. The big data-based tele-rehabilitation coaching system of claim 5, wherein the specific steps of dynamically adjusting the coaching of the mentor comprise:
acquiring and analyzing a ratio of the number of non-standard guidance total times to the number of standard guidance total times in the guidance monitoring set to obtain a first maintenance instruction and a first adjustment instruction; and adjusting the guidance identity of the instructor according to the first maintaining instruction and the first adjusting instruction.
7. The big-data-based remote rehabilitation guidance system according to claim 6, wherein the specific steps of dynamically adjusting the rehabilitation training of the rehabilitee include:
acquiring and analyzing the ratio of the total number of times of non-standard training and the total number of times of standard training in the rehabilitation monitoring set to obtain a second maintaining instruction and a second adjusting instruction; and adjusting the training intensity of the rehabilitee according to the second maintaining instruction and the second adjusting instruction.
8. The big data-based tele-rehabilitation coaching system of claim 7, further comprising a people statistics module for counting first information of a mentor performing the tele-rehabilitation coaching and second information of a rehabilitee requiring the tele-rehabilitation coaching; the first information comprises the guide type, the number of the guide people, the guide duration and the favorable comment number of the guide; the second information includes the age, rehabilitation type, and training level of the rehabilitee.
9. A big data based tele-rehabilitation coaching method applied to the big data based tele-rehabilitation coaching system of any one of claims 1 to 8, comprising:
counting first information of a mentor implementing remote rehabilitation guidance and second information of a rehabilitee needing remote rehabilitation guidance; the first information comprises the guide type, the number of the guide people, the guide duration and the favorable comment number of the guide; the second information comprises the age, rehabilitation type and training grade of the rehabilitee;
carrying out data processing and training on the collected first information of the instructor and the second information of the rehabilitee to obtain an instruction analysis set containing the instruction coefficient and a rehabilitation analysis set containing the rehabilitation coefficient;
monitoring and evaluating the guiding behavior of a director and the rehabilitation training of a rehabilitee to obtain a guiding monitoring set containing a guiding evaluation value and a rehabilitative monitoring set containing a rehabilitative evaluation value;
and combining the guidance monitoring set and the rehabilitation monitoring set according to the guidance analysis set and the rehabilitation analysis set to dynamically adjust the guidance of the instructor and the training of the rehabilitee in a preset monitoring period.
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