CN114642424A

CN114642424A - Physical ability assessment method and device based on somatosensory interaction technology

Info

Publication number: CN114642424A
Application number: CN202210285230.1A
Authority: CN
Inventors: 何玉
Original assignee: Beijing Lantian Medical Equipment Co ltd
Current assignee: Beijing Lantian Medical Equipment Co ltd
Priority date: 2022-03-22
Filing date: 2022-03-22
Publication date: 2022-06-21

Abstract

The application discloses a physical ability assessment method and device based on a somatosensory interaction technology, wherein user activity information is acquired through a 3D motion imaging sensor; constructing a user activity data category library; analyzing the test requirement according to the test item information to obtain an item test requirement, wherein the item test requirement comprises tracking parameter information; performing parameter matching in a user activity data category library according to the tracking parameter information to determine a project parameter, and acquiring test project parameter information based on the project parameter; inputting the parameter information of the test items into the physical ability evaluation model, obtaining user evaluation results and recommending the sports based on the user evaluation results. The technical problems that effective assessment guidance means is lacked for disease prevention and control of the old people and daily health management of the old people is not facilitated in the prior art are solved. The physical ability of the user can be accurately evaluated, and exercise guidance is provided for the user in a targeted manner, so that the morbidity probability is reduced, and the technical effect of effective body health management is realized.

Description

Physical fitness evaluation method and device based on somatosensory interaction technology

Technical Field

The application relates to the technical field of data identification and analysis, in particular to a physical ability assessment method and device based on a somatosensory interaction technology.

Background

At present, China is seriously aged, and physical discomfort or various diseases, such as the increase of people with high blood pressure, high blood sugar and high blood sugar, the increase of people with high blood sugar, high blood sugar and high blood sugar, the occurrence of senile dementia and the like, can occur along with the decline of physical functions in the aging process. However, symptoms are often presented for carrying out corresponding attention and treatment, an effective early warning and prevention guidance method is lacked, and the body state of the elderly is monitored and the elderly are guided in exercise, so that the elderly are helped to keep the muscle quality through continuous and targeted exercise, and the disease risk of diseases and senile dementia is reduced.

The above-mentioned techniques have been found to have at least the following technical problems:

the prior art is lack of effective assessment guidance means for disease prevention and control of the old, and is not beneficial to the daily health management of the old.

Disclosure of Invention

The application aims to provide a physical ability assessment method and device based on a somatosensory interaction technology, and the method and device are used for solving the technical problems that in the prior art, effective assessment guidance means are lacked for disease prevention and control of the old people, and daily health management of the old people is not facilitated. The physical ability of the user is accurately evaluated, different indexes are evaluated through different test items, and exercise guidance is provided for the user in a targeted manner, so that the morbidity probability is reduced, and the technical effect of effective body health management is realized.

In view of the foregoing problems, the present application provides a physical ability assessment method and apparatus based on a somatosensory interaction technology.

In a first aspect, the present application provides a physical ability assessment method based on a somatosensory interaction technology, where the method is applied to a physical ability assessment system, the system includes a 3D motion imaging sensor, and the method includes: acquiring and obtaining user activity information through a 3D motion imaging sensor; identifying and classifying the user activity information, and determining the category of the activity information to construct a user activity data category database; obtaining test item information; analyzing the test requirement according to the test project information to obtain a project test requirement, wherein the project test requirement comprises tracking parameter information; performing parameter matching on the user activity data category library according to the tracking parameter information, determining a project parameter, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameter; inputting the test item parameter information into a physical ability evaluation model, obtaining a first output result of the physical ability evaluation model, wherein the first output result comprises a user evaluation result, and performing exercise recommendation based on the user evaluation result.

On the other hand, the present application further provides a physical ability assessment apparatus based on somatosensory interaction technology, configured to execute the physical ability assessment method based on somatosensory interaction technology according to the first aspect, where the apparatus includes:

the first obtaining unit is used for acquiring and obtaining user activity information through a 3D motion imaging sensor;

the first determining unit is used for identifying and classifying the user activity information, determining the category of the activity information and constructing a user activity data category database;

a second obtaining unit for obtaining test item information;

a third obtaining unit, configured to perform test requirement analysis according to the test item information to obtain an item test requirement, where the item test requirement includes tracking parameter information;

the first execution unit is used for performing parameter matching on the user activity data category library according to the tracking parameter information, determining project parameters and acquiring test project parameter information through the 3D action imaging sensor based on the project parameters;

the first evaluation unit is used for inputting the test item parameter information into a physical fitness evaluation model, obtaining an output result of the physical fitness evaluation model, wherein the output result comprises a user evaluation result, and performing exercise recommendation based on the user evaluation result.

In a third aspect, the present application further provides a physical ability assessment apparatus based on somatosensory interaction technology, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the program.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

1. determining project parameters by performing parameter matching in the user activity data category library according to tracking parameter information, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameters; the test item parameter information is input into the physical fitness evaluation model, the user evaluation result is obtained for exercise recommendation, the physical fitness of the user is accurately evaluated, different indexes are evaluated through different test items, exercise guidance is provided for the user in a targeted mode, the morbidity probability is reduced, and the technical effect of effective body health management is achieved.

2. Performing data probability distribution learning through a training data set, and constructing a data generation evaluation model; obtaining a second output result of the data generation evaluation model; inputting the second output result into a discrimination model to obtain a third output result; and optimizing the data generation evaluation model according to the third output result until the output result of the judgment model meets the training end requirement, determining the physical ability evaluation model, wherein the physical ability evaluation model comprises a data generation evaluation model and a judgment model, performing physical ability evaluation on the user through the data generation evaluation model, and performing judgment analysis on the evaluation result by using the judgment model, so that the technical effects of effectively improving the reliability of the evaluation result, improving the operation efficiency and laying a foundation for accurate physical ability evaluation and motion recommendation are achieved.

3. Acquiring a user project acquisition requirement according to the test project parameter information and the user posture information; acquiring a project acquisition interval according to the equipment acquisition range and the user project acquisition requirement; based on the project acquisition interval, third adjustment information is obtained and used for adjusting the position and/or the angle of the 3D action imaging sensor according to the project acquisition interval, so that the adjustment control of an acquisition scheme aiming at the characteristics of a user is carried out according to the posture characteristics of the user, the acquisition position and the angle of the 3D action imaging sensor are adjusted, the comprehensive data acquisition of the user is ensured, and the technical effect of reliability of an evaluation result is further ensured.

4. Constructing a user database based on the user evaluation result, the recommended movement information and the user movement record information; acquiring a data analysis requirement according to the user database, and generating a data analysis instruction based on the data analysis requirement, wherein the data analysis instruction is used for carrying out data analysis on the user database according to the data analysis requirement; and obtaining a data analysis result, and updating the user database according to the data analysis result. The technical effects of effectively managing the motion record of the user, analyzing the motion state according to the motion record, adjusting and recommending the motion in a targeted manner and helping the user to carry out reliable daily health management are achieved.

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

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a schematic flowchart of a physical ability assessment method based on a somatosensory interaction technology according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a physical ability assessment apparatus based on a somatosensory interaction technology according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

Description of reference numerals: a first obtaining unit 11, a first determining unit 12, a second obtaining unit 13, a third obtaining unit 14, a first executing unit 15, a first evaluating unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, a bus interface 305.

Detailed Description

The embodiment of the application provides a physical ability assessment method and device based on a somatosensory interaction technology, and solves the technical problems that in the prior art, effective assessment guidance means is lacked for disease prevention and control of the old people, and daily health management of the old people is not facilitated.

In the following, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings.

The technical scheme provided by the application has the following general idea:

acquiring and obtaining user activity information through a 3D motion imaging sensor; identifying and classifying the user activity information, and determining the category of the activity information to construct a user activity data category database; obtaining test item information; analyzing the test requirement according to the test project information to obtain a project test requirement, wherein the project test requirement comprises tracking parameter information; performing parameter matching on the user activity data category library according to the tracking parameter information, determining a project parameter, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameter; inputting the test item parameter information into a physical ability evaluation model, obtaining a first output result of the physical ability evaluation model, wherein the first output result comprises a user evaluation result, and performing exercise recommendation based on the user evaluation result. The physical ability of the user is accurately evaluated, different indexes are evaluated through different test items, and exercise guidance is provided for the user in a targeted manner, so that the morbidity probability is reduced, and the technical effect of effective body health management is realized.

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

Referring to fig. 1, an embodiment of the present application provides a method for assessing physical ability based on somatosensory interaction technology, where the method is applied to a physical ability assessment system, the system includes a 3D motion imaging sensor, and the method includes:

specifically, physical fitness evaluation system sets up in physical fitness evaluation equipment, and the user stands in physical fitness evaluation equipment's acquisition region or wears corresponding induction equipment, is applicable to physical examination center, endowment center, recovered center etc. carries out high accuracy, high definition action to the whole body through 3D action imaging sensor and catches, and 3D action imaging sensor has can be to the motion in-process image information of article gather and the transmission, and every second surpasss the accurate discernment of 24 frames motion. And sending the collected user data to a physical ability evaluation system, and determining the physical strength and activities of the whole body according to the user data, wherein the physical strength and activities comprise human perception, motion tracking of joints of 25 parts of the body, various physical strength test modes, various motion determination modes and the like. And the evaluation result is utilized to guide the movement of the user so as to help the old to prevent diseases and improve the rehabilitation effect.

Step S100: and acquiring and obtaining user activity information through a 3D motion imaging sensor.

Further, before acquiring and obtaining the user activity information through the 3D motion imaging sensor, the method includes: acquiring user profile information, wherein the user profile information comprises user basic information and user image information; analyzing the user posture according to the user basic information and the user image information to obtain user posture information; judging whether the user posture information meets a preset condition or not; when the user posture information does not meet the requirement, determining an acquisition range according to the user posture information; and obtaining first adjusting information according to the acquisition range requirement, wherein the first adjusting information is used for adjusting the position and/or the angle of the 3D motion imaging sensor.

Specifically, when the user is subjected to image information acquisition through the 3D motion imaging sensor, corresponding automatic identification and control can be performed according to the posture characteristics of the user, user data information is basic posture information of the user and comprises height, weight and the like of the user, the user data information can be used for scanning the posture of the user through the 3D motion imaging sensor and can also be used for information input by the user, user basic information is description information of the weight, the height, the three-dimensional circumference and the like of the user, user image information is posture acquisition image information of a multi-angle user, comprehensive analysis is performed by using the user basic information and the user image information to determine the posture information of the user, whether the user posture information belongs to a special posture is judged, namely whether preset conditions are met, and the special posture mainly comprises overhigh height, too short height, too fat weight, too high body posture, body posture scanning and the like, The size is too large, the 3D action imaging sensor has a certain shooting range when being set, in order to avoid the problems of integrity and reliability of acquired data, the position of the 3D action imaging sensor is intelligently adjusted and controlled for different users, when the posture of the user is not in the currently set acquisition range, analysis and judgment are carried out according to the posture information of the user and the set standard position, the requirement for adjustment is obtained, first adjusting information is generated, the first adjusting information comprises the acquisition range corresponding to the posture characteristic requirement of the user and the acquisition position of the current 3D action imaging sensor corresponding to the acquisition range requirement, which parameters need to be adjusted, such as height, left and right positions, angles and the like, and the control of the positions and angles is utilized to ensure that the omnibearing data acquisition can be provided for the user.

Step S200: and identifying and classifying the user activity information, determining the category of the activity information and constructing a user activity data category database.

Specifically, the user activity information is a data result of comprehensively collecting the user, and comprises a data information set of each part of the user body, the positions of each joint, part and the like of the user can be positioned, collected and identified by using the collected user imaging information, each corresponding part corresponds to different parameter identifications, each joint and part are identified and distinguished, the data of all joints and parts form the user activity information, an activity database of the user is constructed by using each parameter in the identified user activity information, namely the corresponding data, the user activity database is classified according to different attributes, such as the parameter identification of the user identification part, the user represents the measurement index data of the body fat, the physical strength and the like of the part representing the body state, and the test measurement data representing the motion degree, the amplitude or the capability of the user, and classifying the user activity data in the user activity information according to the described data with specific content to construct a user activity data category database, so that corresponding analysis processing can be conveniently carried out according to different data parameter categories, and a foundation is laid for accurate body state assessment. When the activity data is classified, the data is classified by using the definition format of the parameters, namely, the data with different attributes is represented by using different characters when the parameters are defined, and the different characters represent different types, so that the classification of the activity data is realized.

Step S300: test item information is obtained.

Specifically, the method provided in the embodiment of the present application corresponds to a plurality of modes of tests, including body index measurement, a walking jump test, a balance exercise test, an aerobic exercise test, and the like, where the body index measurement is a physical exercise assessment, including: body posture measurement: the gravity center distribution of the upper and lower half bodies is measured quickly. And (6) testing the grip strength. And (3) balance ability test: one leg stands closed (left and right legs). Core strength test: sit up. And (3) testing the bouncing capability: and (4) longitudinal jumping. Testing the cardiopulmonary endurance: and (5) performing step training for 2 minutes. And (3) agility testing: when the lower limbs react. Flexibility test: kicking the legs of the lower limbs. The walking jump test includes: lateral striding, lateral assault, lateral jumping, knee lifting, cardiopulmonary endurance measurement (2 minutes), in-situ jumping, and aerobic jumping. A balance exercise test comprising: blind walking, standing on one leg, body trunk rotating, frankenstein walking, up-and-down movement, leg lifting (flexibility). Aerobic exercise testing comprising: lifting and deep squatting, Bobby jumping, bowing (outside), step testing and accelerated testing. High leg lift test, knee joint test. Different test modes correspond to different motion measurement models, and more than twenty test modes can realize physical fitness evaluation in aspects of body balance and gravity distribution, grip strength, flexibility, core muscle endurance, longitudinal jumping, balance, reaction time, cardiopulmonary endurance and the like. The corresponding selection can be performed according to the requirement, and the test item information is the selected test mode and the corresponding motion determination content. The test item information can be selected by the user according to personal requirements, and recommended measurement content given by body state analysis can be also analyzed according to data collected in the user activity information.

Step S400: and analyzing the test requirement according to the test item information to obtain item test requirements, wherein the item test requirements comprise tracking parameter information.

Specifically, it is determined which part and joint motion condition of the user needs to be tracked and collected in the test according to the test item information, the item test requirement is that which part and parameters need to be tracked and collected and analyzed by the test item, if some items need to track the bending state of the joint, the parameter requirement of the item is knee joint and muscle stress data, and the tracking parameter information is knee joint and muscle stress data.

Step S500: and performing parameter matching on the user activity data category library according to the tracking parameter information, determining a project parameter, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameter.

Further, the acquiring of test item parameter information by the 3D motion imaging sensor based on the item parameter further includes: determining a user parameter position according to the project parameter, and marking the user parameter position to obtain marking information; and generating tracking acquisition information based on the marking information, wherein the tracking acquisition information is used for controlling the 3D motion imaging sensor to acquire data of the marking information according to the marking information to obtain the parameter information of the test item.

Specifically, matching of corresponding category parameters is carried out in a user activity data category library according to parameter information needing to be tracked and collected by a test item to obtain specific parameter character codes, the monitoring position of the user is determined by utilizing the character codes and the whole body position collected in the user activity information, the monitored parameters and the position generate corresponding control instructions to control and collect the 3D action imaging sensor, and tracking parameter monitoring data corresponding to the test item are obtained. And synchronously transmitting the data acquired in the test process to the physical fitness evaluation system in real time to establish a test monitoring data set of the project of the user, namely the parameter information of the test project.

Step S600: inputting the test item parameter information into a physical ability evaluation model, obtaining a first output result of the physical ability evaluation model, wherein the first output result comprises a user evaluation result, and performing exercise recommendation based on the user evaluation result.

Further, before inputting the test item parameter information into the physical fitness evaluation model, the method comprises: obtaining a training data set; performing data probability distribution learning on the training data set, and constructing a data generation evaluation model; obtaining a second output result of the data generation evaluation model; inputting the second output result into a discrimination model to obtain a third output result; and optimizing the data generation evaluation model according to the third output result until the output result of the discrimination model reaches the training end requirement, and determining the physical fitness evaluation model.

Further, the inputting the test item parameter information into a physical ability evaluation model to obtain a first output result of the physical ability evaluation model, the method includes: generating an evaluation model by data in the physical ability evaluation model, and processing the parameter information of the test item to obtain an evaluation result; inputting the evaluation result into the discrimination model to perform result discrimination to obtain a discrimination result; when the judgment result meets the accuracy requirement, outputting the evaluation result as a first output result; when the judgment result does not meet the accuracy requirement, returning the judgment result to the data generation evaluation model and carrying out operation processing again based on the judgment result to obtain an updated evaluation result; and inputting the updating evaluation result into the judging model for judging to obtain an updating judging result, and outputting the updating judging result as a first output result when the updating judging result meets the accuracy requirement.

Specifically, the test item parameter information obtained by monitoring and collecting parameters required by the item in real time in the test process is input into the physical ability evaluation model for physical ability evaluation, so that the physical ability evaluation information of the user is obtained, and an accurate physical state evaluation result and corresponding healthy exercise guidance are provided for the user. The physical ability evaluation model is obtained by training a large amount of training data, learning and calculating are carried out through the relationship between the monitoring data and the evaluation result of the user in a large amount of historical data to obtain the relationship between the monitoring data and the evaluation result, evaluating the input monitoring data to give an evaluation result, wherein the evaluation result mainly comprises the evaluation result of each physical ability index, such as body balance and gravity center distribution, grip strength, flexibility, core muscle endurance, longitudinal jumping, balance, reaction time, cardiopulmonary endurance, the data generation and evaluation model is used for modeling the joint probability, representing the distribution condition of the data from the statistical angle, having high convergence rate, the data generation evaluation model is obtained through training, the probability distribution of real data in a training data set is continuously learned by the data generation evaluation model in training, and input random noise is converted into data which can be equivalent to the real data by a target. Judging whether the output of the data generation evaluation model is accurate or not by utilizing the constructed judgment model, wherein the judgment model is modeled by utilizing the conditional probability P (Y | X) P (Y | X), does not care how the data is generated, mainly searches the optimal classification surface among different classes, such as LR, SVM, the discrimination data, the reliability of the result given by the evaluation model, if the accurate probability reaches a preset threshold, such as the discrimination probability is above 80%, the evaluation result is accurate, if the evaluation result does not meet the set requirement, the result return value data generation evaluation model continues to train and learn until the output result given by the data generation evaluation model passes the judgment of the judgment model, namely, when the judging accuracy probability reaches the preset requirement, the data generates the output result of the evaluation model and finally provides the user evaluation result for outputting. The physical ability evaluation model is determined through training and convergence of a large amount of training data, and therefore the physical ability evaluation model comprises two parts, namely a data generation evaluation model and a discrimination model. When the physical state of a user is evaluated, collected parameter information of a test item is used as input information, the input information enters a data generation evaluation model to give an evaluation result, the input result is judged by a judgment model, if the probability requirement is met, the evaluation result is used as a first output result to be output, if the probability requirement is not met, the evaluation is continued until the evaluation result reaches the set requirement of the judgment model, the evaluation result is used as a first output result to be output, the physical state is predicted and analyzed according to the output evaluation result of the user, the potential morbidity situation of the existing physical problem is predicted according to the physical result evaluated by the user, corresponding motion guidance information is given according to the result of the prediction analysis, the body management is strengthened through motion guidance, the morbidity probability is reduced, and the obtained evaluation result of the user, the physical prediction state and the recommended motion guidance recommendation information are fed back finally, the system is printed or sent to a user in an electronic format, the corresponding motion mode can be obtained by matching in a motion mode stored in the evaluation system for recommendation and playing, the user can move along with the played content, data collection and recording are carried out on the motion process of the user, the motion process is evaluated and analyzed in the later stage, and the corresponding content such as motion consumption condition, motion accuracy, motion suggestion and guidance is given. Accurate assessment on physical ability of a user is achieved, assessment of different indexes is conducted through different test items, exercise guidance is provided for the user in a targeted mode, the morbidity probability is reduced, the technical effect of effective body health management is achieved, the technical problems that in the prior art, disease prevention and control of the old people is lack of effective assessment guidance means, and daily health management of the old people is not facilitated are solved.

Further, the method further comprises: acquiring an equipment acquisition range; judging whether the acquisition range requirement exceeds the equipment acquisition range; when the equipment exceeds the acquisition range, acquiring acquisition partition information according to the equipment acquisition range and the acquisition range requirement; and obtaining second adjusting information according to the acquisition partition information, wherein the second adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the acquisition partition information.

Particularly, in order to ensure the requirements suitable for various users in the process of acquiring data of the users through the 3D motion imaging sensor, particularly for users with special physical statures, if the users are too high in height or too heavy and large in body width, the acquisition range of the 3D motion imaging sensor is exceeded, as the acquisition range of the 3D motion imaging sensor is limited due to the fact that the 3D motion imaging sensor is easy to place and save space in order to ensure small size of equipment, the acquisition position and the angle of the 3D motion imaging sensor can be adjusted through the physical stature conditions of the users with characteristic physical statures so as to meet the data acquisition requirements of the users, for the users with the length or width exceeding the range, the acquisition range requirements required by the activities of the users are partitioned into two or more partition spaces for acquiring data step by step, and generating an adjusting strategy according to the position relation of the partition nodes for acquiring the partition information, performing one-time adjustment on each partition, and adopting an adjusting scheme when the first partition is acquired, but adjusting the acquisition position and angle of the 3D motion imaging sensor according to the acquisition requirement of the second partition after the first partition is acquired so as to ensure that the comprehensive data acquisition is performed on the user and lay a foundation for reliable evaluation.

Further, the method further comprises: acquiring a user project acquisition requirement according to the test project parameter information and the user posture information; acquiring a project acquisition interval according to the equipment acquisition range and the user project acquisition requirement; and obtaining third adjusting information based on the item acquisition interval, wherein the third adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the item acquisition interval.

Specifically, in the specific test item data acquisition, a specific acquisition position is determined according to test item parameter information and user posture information, the user item acquisition requirement is that according to the user posture and the acquisition data measurement requirement of the current test item of the user, if the current test item is a lower limb kicking, the position interval of the lower limb needs to be positioned according to the user posture, the requirement of acquiring data for the test item of the lower limb kicking of the user is obtained, the relation between the acquisition requirement range and the equipment acquisition range is judged to position how to adjust the 3D action imaging sensor to carry out complete and effective data acquisition, the third adjustment information is that according to the specific test item, the posture characteristics of the user are combined to carry out the position positioning of item acquisition, the acquisition position and the angle of the 3D action imaging sensor are adjusted to realize the accurate data acquisition of the item of the user, thereby enabling reliable project assessment.

Further, the method further comprises: obtaining recommended movement information according to the user evaluation result and the test item information; generating a motion guidance image according to the recommended motion information, and carrying out data acquisition and recording on the motion process of the user through a 3D motion sensor according to the motion guidance image to obtain user motion record information; constructing a user database based on the user evaluation result, the recommended movement information and the user movement record information; acquiring a data analysis requirement according to the user database, and generating a data analysis instruction based on the data analysis requirement, wherein the data analysis instruction is used for carrying out data analysis on the user database according to the data analysis requirement; and obtaining a data analysis result, and updating the user database according to the data analysis result.

Specifically, according to the user evaluation result, the body quality, physical ability and illness probability corresponding to the test evaluation result of the current user are predicted and analyzed by combining with the test item information, a recommended movement is correspondingly given, such as the movement content and intensity for daily health management, the content and intensity for rehabilitation movement are determined, and the recommended movement information is determined to generate a corresponding guide image, the user follows the guide image to move, meanwhile, the collection of pictures and data in the movement process of the user is stored in a personal movement database of the user through a 3D motion sensor, a movement database of the user is constructed, wherein the user evaluation result, the recommended movement information and the user movement record information are recorded, the movement process and the rehabilitation process of the user are conveniently monitored and managed, and the user is guided to carry out effective movement and rehabilitation, the effects of daily health management and rehabilitation training of the user are improved. If the physical fitness of the current user is poor and the user is at risk of suffering from senile dementia, exercise training aiming at the physical fitness test result is carried out aiming at the tested physical fitness so as to strengthen the physical fitness part and reduce the risk of suffering from the disease. All the evaluation results of the user and the data of the exercise process are stored in an exercise database of the user, and can be analyzed and evaluated periodically or according to a certain index, wherein the data analysis instruction refers to corresponding data analysis operation performed on the user database, and can perform corresponding functional analysis, such as exercise amount statistics, exercise duration statistics, exercise plan evaluation and formulation, timing analysis and the like.

In summary, the present embodiment at least has the following technical effects:

Example two

Based on the method for evaluating physical ability based on the somatosensory interaction technology in the foregoing embodiment, the invention also provides a physical ability evaluation device based on the somatosensory interaction technology, referring to fig. 2, where the device includes:

a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain user activity information through a 3D motion imaging sensor;

a first determining unit 12, where the first determining unit 12 is configured to perform recognition and classification on the user activity information, determine a category of the activity information, and construct a user activity data category database;

a second obtaining unit 13, wherein the second obtaining unit 13 is used for obtaining the test item information;

a third obtaining unit 14, where the third obtaining unit 14 is configured to perform test requirement analysis according to the test item information to obtain an item test requirement, where the item test requirement includes tracking parameter information;

the first execution unit 15 is configured to perform parameter matching in the user activity data category library according to the tracking parameter information, determine a project parameter, and acquire test project parameter information through the 3D motion imaging sensor based on the project parameter;

a first evaluation unit 16, wherein the first evaluation unit 16 is configured to input the test item parameter information into a physical fitness evaluation model, obtain an output result of the physical fitness evaluation model, the output result includes a user evaluation result, and perform exercise recommendation based on the user evaluation result.

Further, the apparatus further comprises:

a fourth obtaining unit, configured to obtain user profile information, where the user profile information includes user basic information and user image information;

a fifth obtaining unit, configured to perform user posture analysis according to the user basic information and the user image information, and obtain user posture information;

the first judging unit is used for judging whether the user posture information meets a preset condition or not;

the second determining unit is used for determining the acquisition range requirement according to the user posture information when the user posture information is not met;

the first adjusting unit is used for obtaining first adjusting information according to the acquisition range requirement, and the first adjusting information is used for adjusting the position and/or the angle of the 3D motion imaging sensor.

Further, the apparatus further comprises:

a sixth obtaining unit, configured to obtain an equipment acquisition range;

the second judgment unit is used for judging whether the acquisition range requirement exceeds the equipment acquisition range;

a seventh obtaining unit, configured to, when the device exceeds the acquisition range, obtain acquisition partition information according to the acquisition range of the device and the acquisition range requirement;

and the second adjusting unit is used for obtaining second adjusting information according to the acquisition partition information, and the second adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the acquisition partition information.

Further, the apparatus further comprises:

the eighth obtaining unit is used for obtaining the user project acquisition requirement according to the test project parameter information and the user posture information;

a ninth obtaining unit, configured to obtain a project collection interval according to the device collection range and the user project collection requirement;

and the third adjusting unit is used for obtaining third adjusting information based on the item acquisition interval, and the third adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the item acquisition interval.

Further, the apparatus further comprises:

a tenth obtaining unit, configured to obtain a training data set;

the first construction unit is used for carrying out data probability distribution learning on the training data set and constructing a data generation evaluation model;

an eleventh obtaining unit configured to obtain a second output result of the data generation evaluation model;

a twelfth obtaining unit, configured to input the second output result into a discriminant model, and obtain a third output result;

and the second execution unit is used for optimizing the data generation evaluation model according to the third output result until the output result of the judgment model reaches the training end requirement, and determining the physical fitness evaluation model.

Further, the apparatus further comprises:

a thirteenth obtaining unit, configured to generate an evaluation model from the data in the physical fitness evaluation model, and process the test item parameter information to obtain an evaluation result;

a fourteenth obtaining unit, configured to input the evaluation result into the discriminant model for result discrimination to obtain a discrimination result;

a third execution unit, configured to output the evaluation result as a first output result when the determination result meets an accuracy requirement;

the fourth execution unit is used for returning the judgment result to the data generation evaluation model and carrying out operation processing again based on the judgment result to obtain an updated evaluation result when the judgment result does not meet the accuracy requirement;

and the fifth execution unit is used for inputting the updating evaluation result into the judgment model for judgment to obtain an updating judgment result, and outputting the updating judgment result as a first output result until the updating judgment result meets the accuracy requirement.

Further, the apparatus further comprises:

the first marking unit is used for determining a user parameter position according to the project parameter, marking the user parameter position and obtaining marking information;

the first tracking unit is used for generating tracking acquisition information based on the marking information, and the tracking acquisition information is used for controlling the 3D action imaging sensor to acquire data of the marking information according to the marking information to obtain the parameter information of the test item.

Further, the apparatus further comprises:

a fifteenth obtaining unit, configured to obtain recommended exercise information according to the user evaluation result and the test item information;

the sixth execution unit is used for generating a motion guidance image according to the recommended motion information, and performing data acquisition and recording on the motion process of the user through a 3D motion sensor according to the motion guidance image to obtain user motion record information;

a second construction unit, configured to construct a user database based on the user evaluation result, the recommended movement information, and the user movement record information;

a seventh execution unit, configured to obtain a data analysis requirement according to the user database, and generate a data analysis instruction based on the data analysis requirement, where the data analysis instruction is used to perform data analysis on the user database according to the data analysis requirement;

the first updating unit is used for obtaining a data analysis result and updating the user database according to the data analysis result.

In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference of the embodiments that is expected, and the aforementioned physical ability assessment method based on the motion-sensing interaction technology in the first embodiment of fig. 1 and the specific example are also applicable to the physical ability assessment device based on the motion-sensing interaction technology in the present embodiment. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Exemplary electronic device

The electronic device of the embodiment of the present application is described below with reference to fig. 3.

Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.

Based on the inventive concept of the method for assessing physical ability based on somatosensory interaction technology in the foregoing embodiments, the invention further provides a device for assessing physical ability based on somatosensory interaction technology, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of any one of the methods for assessing physical ability based on somatosensory interaction technology described above.

Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.

The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the application provides a physical ability assessment method and a physical ability assessment device based on a somatosensory interaction technology, the method is applied to a physical ability assessment system, the system comprises a 3D action imaging sensor, and the method comprises the following steps: acquiring and obtaining user activity information through a 3D motion imaging sensor; identifying and classifying the user activity information, determining the category of the activity information and constructing a user activity data category database; obtaining test item information; analyzing the test requirement according to the test project information to obtain a project test requirement, wherein the project test requirement comprises tracking parameter information; performing parameter matching on the user activity data category library according to the tracking parameter information, determining a project parameter, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameter; inputting the parameter information of the test items into a physical ability evaluation model to obtain a first output result of the physical ability evaluation model, wherein the first output result comprises a user evaluation result, predicting and analyzing the physical state according to the output user evaluation result, predicting the potential morbidity of the existing physical problem according to the physical ability result evaluated by the user, giving corresponding motion guidance information according to the result of the prediction analysis, strengthening the physical management by motion guidance, reducing the morbidity probability, achieving the accurate evaluation of the physical ability of the user, evaluating different indexes through different test items, providing motion guidance for the user in a targeted manner to reduce the morbidity probability and realize the technical effect of effective physical health management, thereby solving the problem that the disease prevention and control of the old people in the prior art lacks effective evaluation guidance means, is not beneficial to the daily health management of the old.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the same technology as the present invention, it is intended that the present invention encompass such modifications and variations as well.

Claims

1. A physical fitness evaluation method based on a somatosensory interaction technology is applied to a physical fitness evaluation system, the system comprises a 3D motion imaging sensor, and the method comprises the following steps:

acquiring and obtaining user activity information through a 3D motion imaging sensor;

identifying and classifying the user activity information, and determining the category of the activity information to construct a user activity data category database;

obtaining test item information;

analyzing the test requirement according to the test project information to obtain a project test requirement, wherein the project test requirement comprises tracking parameter information;

performing parameter matching on the user activity data category library according to the tracking parameter information, determining a project parameter, and acquiring test project parameter information through the 3D action imaging sensor based on the project parameter;

inputting the test item parameter information into a physical ability evaluation model, obtaining a first output result of the physical ability evaluation model, wherein the first output result comprises a user evaluation result, and performing exercise recommendation based on the user evaluation result.

2. The method of claim 1, wherein prior to obtaining user activity information via 3D motion imaging sensor acquisition, the method comprises:

acquiring user profile information, wherein the user profile information comprises user basic information and user image information;

analyzing the user posture according to the user basic information and the user image information to obtain user posture information;

judging whether the user posture information meets a preset condition or not;

when the user posture information does not meet the requirement, determining an acquisition range according to the user posture information;

and obtaining first adjusting information according to the acquisition range requirement, wherein the first adjusting information is used for adjusting the position and/or the angle of the 3D motion imaging sensor.

3. The method of claim 2, wherein the method further comprises:

acquiring an equipment acquisition range;

judging whether the acquisition range requirement exceeds the equipment acquisition range;

when the equipment exceeds the acquisition range, acquiring acquisition partition information according to the equipment acquisition range and the acquisition range requirement;

and obtaining second adjusting information according to the acquisition partition information, wherein the second adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the acquisition partition information.

4. The method of claim 3, further comprising:

acquiring a user project acquisition requirement according to the test project parameter information and the user posture information;

acquiring a project acquisition interval according to the equipment acquisition range and the user project acquisition requirement;

and obtaining third adjusting information based on the item acquisition interval, wherein the third adjusting information is used for adjusting the position and/or the angle of the 3D action imaging sensor according to the item acquisition interval.

5. The method of claim 1, wherein prior to entering the test item parameter information into a fitness evaluation model, the method comprises:

obtaining a training data set;

performing data probability distribution learning on the training data set, and constructing a data generation evaluation model;

obtaining a second output result of the data generation evaluation model;

inputting the second output result into a discrimination model to obtain a third output result;

and optimizing the data generation evaluation model according to the third output result until the output result of the discrimination model reaches the training end requirement, and determining the physical fitness evaluation model.

6. The method of claim 5, wherein inputting the test item parameter information into a fitness evaluation model obtains a first output of the fitness evaluation model, the method comprising:

generating an evaluation model by data in the physical ability evaluation model, and processing the parameter information of the test item to obtain an evaluation result;

inputting the evaluation result into the discrimination model to perform result discrimination to obtain a discrimination result;

when the judgment result meets the accuracy requirement, outputting the evaluation result as a first output result;

when the judgment result does not meet the accuracy requirement, returning the judgment result to the data generation evaluation model and carrying out operation processing again based on the judgment result to obtain an updated evaluation result;

and inputting the updating evaluation result into the judging model for judging to obtain an updating judging result, and outputting the updating judging result as a first output result when the updating judging result meets the accuracy requirement.

7. The method of claim 1, wherein said acquiring test item parameter information by said 3D motion imaging sensor based on said item parameters, further comprises:

determining a user parameter position according to the project parameter, and marking the user parameter position to obtain marking information;

and generating tracking acquisition information based on the marking information, wherein the tracking acquisition information is used for controlling the 3D motion imaging sensor to acquire data of the marking information according to the marking information to obtain the parameter information of the test item.

8. The method of claim 1, wherein the method further comprises:

obtaining recommended movement information according to the user evaluation result and the test item information;

generating a motion guidance image according to the recommended motion information, and carrying out data acquisition and recording on the motion process of the user through a 3D motion sensor according to the motion guidance image to obtain user motion record information;

constructing a user database based on the user evaluation result, the recommended movement information and the user movement record information;

acquiring a data analysis requirement according to the user database, and generating a data analysis instruction based on the data analysis requirement, wherein the data analysis instruction is used for carrying out data analysis on the user database according to the data analysis requirement;

and obtaining a data analysis result, and updating the user database according to the data analysis result.

9. A physical fitness evaluation device based on a somatosensory interaction technology, the device comprising:

a second obtaining unit configured to obtain test item information;

10. A fitness evaluation device based on somatosensory interaction technology, 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 claims 1 to 8 when executing the program.