CN113194829A - Human body mental stress testing method and system - Google Patents
Human body mental stress testing method and system Download PDFInfo
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Abstract
A method and a system for testing mental stress of a human body are provided, the method comprises the following steps: arranging at least one detection sensor on a certain movable emotion releasing object, and detecting the behavior of sample people on the emotion releasing object to obtain a sample detection data set (step 1); presetting a stress assessment algorithm, wherein the stress assessment algorithm has at least one assessment parameter, performing stress assessment on the samples in the sample detection data set by using the stress assessment algorithm, training and adjusting the assessment parameter so that the assessment error of the stress assessment algorithm with the finally determined assessment parameter statistically meets the error range of medical approval (step 2); the training and adjustment of the evaluation parameters are stopped, and the test sample is subjected to test evaluation by using the finally determined evaluation parameters, so that a test pressure score which can be approved by medical science is obtained (step 3). The human body mental stress testing method and the system solve the problems that only professional and few personnel can evaluate the mental stress of people, the cost is high and the application range is small in the prior art.
Description
The invention relates to the field of basic public health, in particular to a method and a system for testing mental stress of a human body.
The social rhythm is faster and faster, and the pressure of people on working, learning and the like is also increased. However, except for psychological counseling and psychologists, no method and equipment for testing mental stress are provided, people's stress cannot be concerned with all the time, social problems are caused, family problems are serious, and a solution capable of monitoring in real time and intervening as soon as possible is not provided.
Disclosure of Invention
The invention aims to provide a method and a system for testing human mental stress, and aims to solve the problems that only professional personnel can evaluate the mental stress of people, the cost is high, and the application range is small in the prior art.
The invention provides a human body mental stress testing method, which comprises the following steps:
step 1: arranging at least one detection sensor on a certain emotion disclosing object, and detecting the behavior of sample people on the emotion disclosing object to obtain a sample detection data set;
step 2: presetting a stress assessment algorithm, wherein the stress assessment algorithm is provided with at least one assessment parameter, performing stress assessment on the samples in the sample detection data set by using the stress assessment algorithm, and training and adjusting the assessment parameter so that the assessment error of the stress assessment algorithm with the finally determined assessment parameter statistically meets the medically approved error range;
and step 3: stopping the training and adjustment of the evaluation parameters, and performing detection evaluation on the detection sample by using the finally determined evaluation parameters so as to obtain a test pressure score which can be approved by medical science.
In the invention, firstly, the monitoring of human behaviors, such as beating, shouting, kneading and the like, can be achieved through monitoring the emotion-releasing object, the behaviors are recorded through the detection sensor, for example, the detection sensor is a force sensor, the force record is detected at different parts during beating, the force record is converted into detection data, and other behaviors are included, the detection data is formed for a human body sample, the mental stress of the sample is evaluated by using a preset mental stress evaluation algorithm, evaluation parameters are trained and adjusted after evaluation, the evaluation parameters are calculated again for multiple times, the evaluation parameters which can be approved by medicine are determined, and then the evaluation parameters which are determined in the same way are used for making a test pressure score which can be approved by medicine for the sample group. So far, once the final evaluation parameter is confirmed, the result made by using the evaluation parameter is relatively accurate, and few professionals are not needed to evaluate the mental stress, so that the problems in the prior art are solved.
By utilizing the method to test the mental stress of the human body, the corresponding emotion-releasing object can be arranged in the basic health center, thereby replacing doctors to make detection evaluation.
Certainly, the catharsis object can be even made into a doll, a robot and other images are placed in families, offices and entertainment places, and the mental stress can be detected and evaluated by monitoring human body behaviors at any time and any place; compared with the traditional evaluation of the current short-time form which only can go to a medical institution, the method can realize real-time monitoring and long-time monitoring for doctor supervisor judgment, and can form a plurality of data according to a plurality of monitoring data, thereby facilitating evaluation analysis and statistics.
The invention also provides a human body pressure test system, which comprises: a movable mood-releasing subject; the detection sensors are arranged on the emotion releasing object and used for detecting the behavior of the human body on the emotion releasing object and sending detection data; the system comprises a data calculation center with a preset mental stress assessment algorithm, wherein the calculation algorithm is used for calculating detection data through at least one preset assessment parameter, assessing the mental stress of a human body, continuously training and adjusting the assessment parameter, and determining a final assessment parameter, so that the assessment error of the mental stress assessment algorithm with the final assessment parameter statistically meets the medically approved error range; and the data computing center evaluates the mental stress of the human body by using a mental stress evaluation algorithm with final evaluation parameters to obtain a test stress score.
FIG. 1 is a flow chart of a method for testing stress of human body according to the present invention;
FIG. 2 is a process diagram of the current training adjustment method of the present invention;
FIG. 3 is a process diagram of the statistical training adjustment method of the present invention;
fig. 4 is a schematic block diagram of the human stress testing system of the present invention.
The invention will be further elucidated and described with reference to the embodiments and drawings of the specification:
referring to fig. 1, the present invention provides a method for testing mental stress of a human body. The method comprises the following steps:
step 1: and arranging at least one detection sensor on a movable emotion releasing object, and detecting the behavior of the sample population on the emotion releasing object to obtain a sample detection data set.
Referring to fig. 2, for example, in fig. 2, the sample data set includes a plurality of sample data α1，α 2，α 3… …, each test sample, i.e., each subject in need of mental stress testing, will generate a sample data αN(ii) a And each sample data comprises a plurality of sample elements, e.g. sample data alpha2Including a sample element of (X)2，Y 2…Z 2) Each sample element is from a detection sensor, and the detection content of the detection sensor on the leakage object comprises: the method comprises the steps of obtaining sample data by using position, force, frequency and other data, specifically dividing different areas of a catharsis object, arranging sensors in different areas according to a position sensor, a force sensor, a counting sensor and a combination mode of the position sensor, the force sensor and the counting sensor, detecting the position of the sensors for catharsis, monitoring catharsis force and catharsis frequency to obtain sample elements, and forming the sample data by using the sample elements of a plurality of detection sensors.
In an actual application, the catharsis object is a human head structure made of plastic materials, the eyes and nose area of the human head structure are divided into a first area, the face and the mouth are divided into a second area, and the hair covering part and the neck area are divided into a third area; then position sensors and force sensors are arranged in the areas; when the detected person beats and twists the catharsis object, the sensors in the areas record the position and force of the beating respectively; and correspondingly forming sample data.
Step 2: presetting a stress assessment algorithm, wherein the stress assessment algorithm is provided with assessment parameters, performing stress assessment on the samples in the sample detection data set by using the stress assessment algorithm, and training and adjusting the assessment parameters so that the assessment error of the stress assessment algorithm with the finally determined assessment parameters statistically meets the error range approved by medical science.
Specifically, the mental stress assessment algorithm may have a plurality of assessment parameters, and how many of the assessment parameters are set according to the detection data, for example, in the above application, there should be two assessment parameters corresponding to the position and the strength of the detection data flapping, such as: the position data is that the first area, the second area and the third area respectively correspond to an evaluation parameter A1I.e. A1In some embodiments, the evaluation parameter may be a weighted value calculated by mathematical statistics, and in still other embodiments, the evaluation parameter may also be a boolean value, a logical operation, or the like. By preset evaluation parameter A1The mental stress exerted by different areas of the human head structure in the process of flapping is endowed. Evaluation of parameters A from the application1I.e. A1It can be seen that the weight in the first region is greater than that in the third region, i.e. we preset that the mental pressure value exhibited by the beating of the eyes and nose parts is greater than that of the hair covering part, the neck region, when calculating the mental pressure.
The mental stress assessment algorithm comprises a mathematical calculation formula, and the mathematical calculation formula is different under different application scenes and calculation; during calculation, preset evaluation parameters are brought into the calculation formula, and then detection data are recorded, so that the estimated pressure value is obtained. However, in the process of first calculation using the calculation formula, since the evaluation parameters and the mathematical calculation formula are affected by human subjective experience and evaluation, the calculated estimated pressure score is not accurate or has poor relevance.
In practical application, it is necessary to train and adjust the evaluation parameters and determine the final evaluation parameters, where the final evaluation parameters are that the test pressure scores evaluated by the evaluation parameters are very close to the actual evaluation scores, have close statistical significance, are approved by medicine, and can be used as the final evaluation scores.
Specifically, the training and adjustment of the evaluation parameters include two methods, namely a current training adjustment method and a statistical training adjustment method, which are sequentially implemented.
The current training adjustment method is to calculate a certain sample data for multiple times and adjust the evaluation parameters, so that the calculation result obtained by calculating the current sample data according to the currently determined evaluation parameters can meet the error range of medical requirements. Simply, the estimated pressure score obtained by calculation is considered to be relatively accurate by medicine when the evaluation parameter is adjusted to a certain value for many times.
Specifically, the current training adjustment method includes:
step 211: and calculating certain sample data in the sample detection data set by using a mental stress evaluation algorithm to obtain a pre-estimated stress value.
Step 212: and carrying out artificial mental stress detection on the sample corresponding to the sample data to obtain an actual stress score.
Step 213: and evaluating the error level of the estimated pressure value and the actual pressure value.
As in fig. 2, the error levels are classified into 1-level, 2-level, 3-level and 4-level, although in other applications the error levels may be more. More error levels represent higher error requirements, higher computational complexity, and longer periods to train and adjust.
In the present embodiment, when the error level is set to not more than 2, the error is considered to be medically allowable and to be within the error range.
Step 214: and adjusting the evaluation parameters according to a preset corresponding relation table, wherein the corresponding relation table is prestored with a mapping relation between the error grade and the evaluation parameter adjustment value.
Step 215: and after the evaluation parameters are adjusted every time, calculating the sample data again by using a mental stress evaluation algorithm, and performing error grade evaluation on the estimated pressure value and the actual pressure value obtained by calculation until the error grade meets the preset grade requirement.
In this step, as shown in fig. 2, in the first calculation and evaluation, the estimated pressure score obtained by the evaluation is 89 points, the actual pressure score is 45 points, and the error thereof is 44 points, and when the error level is evaluated, the error level is considered to be at the error level of 4 and is greater than the preset error level of 2; the evaluation parameters need to be adjusted according to the evaluation adjustment parameter values {0.10,0.05} corresponding to the error level 4 level; this is the first adjustment.
Then, carrying out second evaluation after the first adjustment, calculating and evaluating the same detection data again by using the adjusted evaluation parameters, wherein the evaluated pressure score is 65 points, the actual pressure score is 45 points, the error is 20 points, and the evaluation is considered to be in an error grade 3 which is more than a preset error grade 2 when the error grade is evaluated; the evaluation parameters need to be adjusted according to the evaluation adjustment parameter values {0.05,0.05} corresponding to the error level 3 level; this is the second adjustment.
Carrying out third evaluation after the second adjustment, calculating and evaluating the same detection data again by using the evaluation parameters of the two adjustments, wherein the evaluation pressure score obtained by the evaluation is 40, the actual pressure score is 45, the error is 5, and the evaluation is considered to be in the error grade 2 and not more than the preset error grade 2 when the error grade is evaluated; the evaluation parameters no longer need to be adjusted according to the corresponding evaluation adjustment parameter values 0.01,0.02 of the error level 2 level.
After the third evaluation, the evaluation parameters are determined, and the determined evaluation parameters meet the requirements of the evaluation of the current detection data.
The statistical training adjustment method is to calculate a plurality of sample data by using the currently determined evaluation parameters, judge whether the calculation results of the plurality of samples statistically meet the error range of medical requirements, stop implementing the current training adjustment method if the calculation results of the plurality of samples meet the error range of the medical requirements, and complete training and adjustment of the evaluation parameters.
Referring to fig. 3, the statistical training method includes:
step 221: after the evaluation parameter of a certain sample data calculation is determined, the calculated sample data is calculated again by using the evaluation parameter, or a plurality of sample data are randomly extracted from the sample detection data set and calculated again by using the evaluation parameter, so that a plurality of estimated pressure values are obtained.
For example, in fig. 2, the same sample data is detected 3 times by the current training adjustment method, and the finally determined evaluation parameter is { a }1,A 2In the method, the evaluation parameter { A } is still followed1,A 2Recalculating the calculated sample data or the randomly extracted sample data to obtain a plurality of evaluation pressure scores.
Step 222: referring to fig. 3, the obtained estimated pressure scores are compared with the actual pressure scores corresponding to the samples, and the number of different error levels between the estimated pressure scores and the actual pressure scores is counted.
Step 223: presetting a statistical threshold percentage. In this embodiment, the statistical threshold percentage for already calculated sample data is set to 92%, i.e. only in these already calculated sample data is considered according to the evaluation parameter { A }1,A 2Evaluating the ratio of the error level of the calculated evaluation pressure score to the actual pressure score being not greater than level 2 being greater than 92%, and considering it as having the evaluation parameter { A }1,A 2The mental stress assessment algorithm of (1) } is medically approved and statistically significant.
Step 224: the number of samples meeting the medically approved error level is calculated as a ratio of the number of samples recalculated in step 222 and compared to a statistical threshold percentage.
When the occupancy is less than the statistical threshold percentage,
as shown in fig. 3, in the present embodiment, for re-evaluation of already calculated sample data, if the percentage of error level statistics is 95%, training and adjusting of evaluation parameters are completed, and the current sample training and adjusting method is not executed again when sample data is next entered into the mental stress evaluation algorithm; after the detection data is recorded again, the detection data is directly recorded according to the evaluation parameter { A1,A 2And assessing the mental stress by a mental stress assessment algorithm, wherein the assessed stress score obtained after assessment is in accordance with medical statistical significance and is considered to be effective.
As shown in fig. 3, in the present embodiment, for re-evaluation of randomly extracted sample data, the error level statistics is 83%, and 83% is less than 90% of the preset statistics threshold percentage, then training and adjusting of the evaluation parameters are not completed; and executing the current sample training and adjusting method again when sample data is input into the mental stress assessment algorithm next time, and continuing training and adjusting the assessment parameters until the error grade statistics percentage is not less than the statistics threshold percentage.
In this embodiment, when the error level is gradually decreased, the adjustment value of the evaluation parameter is also decreased accordingly. The gradually reduced error grade adjustment value can gradually reduce the amplitude of each adjustment, gradually approaches to the actual evaluation result, and does not generate large fluctuation.
In this embodiment, the method further includes setting a matching intervention table, where the matching intervention table is provided with a plurality of intervention measures corresponding to the evaluation pressure scores, and obtaining the intervention measures corresponding to the evaluation pressure scores calculated according to the trained and adjusted evaluation parameters and implementing the intervention. For example: grading the evaluation pressure score, and evaluating the evaluation score of 0-60 as serious intervention according to a score standard of 0-100; evaluating 61-75 points as risk, and needing to be tracked; score 76-85 was evaluated as normal and no treatment was done; the score was 86-100 assessed as healthy. After evaluation, corresponding warning or information is directly sent by using the obtained scores, and the independent evaluation, tracking and intervention of mental stress are realized in a corresponding system, so that social risks and living stress can be greatly reduced.
By utilizing the method to test the mental stress of the human body, the corresponding emotion-releasing object can be arranged in the basic health center, thereby replacing doctors to make detection evaluation.
Certainly, the catharsis object can be even made into a doll, a robot and other images are placed in families, offices and entertainment places, and the mental stress can be detected and evaluated by monitoring human body behaviors at any time and any place; compared with the traditional evaluation of the current short-time form which only can go to a medical institution, the method can realize real-time monitoring and long-time monitoring for doctor supervisor judgment, and can form a plurality of data according to a plurality of monitoring data, thereby facilitating evaluation analysis and statistics.
Referring to fig. 4, the present invention also provides a system for testing mental stress of a human body, the system comprising: a movable mood-releasing subject 10; the detection sensors 11 are arranged on the emotion releasing object 10, and the detection sensors 11 are used for detecting the behavior of the human body on the emotion releasing object 10 and sending detection data; a data calculation center 20 with a preset mental stress assessment algorithm built in, wherein the calculation algorithm calculates the detection data through at least one preset assessment parameter, assesses the mental stress of the human body, continuously trains and adjusts the assessment parameter, and determines a final assessment parameter, so that the assessment error of the mental stress assessment algorithm with the final assessment parameter statistically meets the error range approved by medicine; the data computing center 20 obtains a test stress score for the human body stress evaluation by using a stress evaluation algorithm with final evaluation parameters.
Further, the system comprises: the data calculation center 20 calculates the detection data and obtains a pre-estimated pressure score 30; the actual stress score obtained after passing the artificial stress test 40; an error evaluation unit for evaluating at least one error level of the actual pressure score 40 and the estimated pressure score 30; a parameter feedback modification unit 50 for adjusting the evaluation parameter according to the corresponding relation table of the preset error grade and the evaluation parameter adjustment value according to the error grade; the data calculation center 20 re-evaluates the human body mental pressure according to the evaluation parameters adjusted by the parameter feedback modification unit 50, and continuously evaluates the human body pressure after multiple adjustments, thereby gradually reducing the error between the actual pressure score 40 and the estimated pressure score 30 until the error is within a medically approved controllable range.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
- A human body mental stress test method is characterized by comprising the following steps:step 1: arranging at least one detection sensor on a movable emotion releasing object, and detecting the behavior of sample people on the emotion releasing object to obtain a sample detection data set;step 2: presetting a stress assessment algorithm, wherein the stress assessment algorithm is provided with at least one assessment parameter, performing stress assessment on the samples in the sample detection data set by using the stress assessment algorithm, and training and adjusting the assessment parameter so that the assessment error of the stress assessment algorithm with the finally determined assessment parameter statistically meets the medically approved error range;and step 3: stopping the training and adjustment of the evaluation parameters, and performing detection evaluation on the detection sample by using the finally determined evaluation parameters so as to obtain a test pressure score which can be approved by medical science.
- The method according to claim 1, wherein the sample data set comprises a plurality of sample data, each of the detection samples generates a sample data, each of the sample data comprises a plurality of sample elements, and each of the detection sensors obtains a sample element after detection.
- The human stress testing method of claim 2, wherein the emotion releasing subject is divided into a plurality of different areas, and the detecting sensors include a position sensor, a force sensor, and a counting sensor, and combinations thereof; and detecting the abreaction positions of the sensors, obtaining the sample elements according to abreaction force and abreaction frequency, and forming the sample data by the sample elements of the plurality of detection sensors.
- The human stress testing method of claim 1, wherein the method of training and adjusting the evaluation parameters in step 2 comprises a current training adjustment method and a statistical training adjustment method, which are sequentially performed; the current training adjustment method is to calculate certain sample data for multiple times and adjust the evaluation parameters, so that the calculation result obtained by calculating the sample data according to the currently determined evaluation parameters can meet the error range of medical requirements; the statistical training adjustment method is to calculate a plurality of sample data by using the currently determined evaluation parameters, judge whether the calculation results of the plurality of samples statistically meet the error range of medical requirements, stop implementing the current training adjustment method if the calculation results of the plurality of samples meet the error range of the medical requirements, and finish training and adjusting the evaluation parameters.
- The human stress testing method of claim 3, wherein the current training adjustment method comprises:step 211: calculating certain sample data in the sample detection data set by using a mental stress evaluation algorithm to obtain a pre-estimated stress value;step 212: carrying out artificial mental stress detection on the sample corresponding to the sample data to obtain an actual stress score;step 213: evaluating the error grade of the estimated pressure value and the actual pressure value;step 214: adjusting the evaluation parameters according to a preset corresponding relation table, wherein the corresponding relation table is prestored with a mapping relation between the error grade and the evaluation parameter adjustment value;step 215: and after the evaluation parameters are adjusted every time, calculating the sample data again by using a mental stress evaluation algorithm, and performing error grade evaluation on the estimated pressure value and the actual pressure value obtained by calculation until the error grade meets the preset grade requirement.
- The human stress testing method of claim 3 or 4, wherein the statistical training method comprises:step 221: after determining the evaluation parameter of a certain sample data calculation, recalculating the calculated sample data by using the evaluation parameter, or randomly extracting a plurality of sample data in a sample detection data set by using the evaluation parameter to recalculate to obtain a plurality of estimated pressure values;step 222: comparing the obtained estimated pressure scores with actual pressure scores corresponding to the samples, and counting the number of different error grades;step 223: presetting a statistical threshold percentage;step 224: calculating a ratio of the number of samples meeting the medically approved error level to the number of samples recalculated in step 222, and comparing with a threshold percentage;when the ratio is less than the threshold percentage, continuing to execute the current sample training adjustment method when sample data is input into the mental stress assessment algorithm next time; otherwise, training and adjusting the evaluation parameters are finished, and the current sample training and adjusting method is not executed when sample data is input into the mental stress evaluation algorithm next time.
- The method as claimed in claim 6, wherein the adjustment value of the evaluation parameter is decreased when the error level is decreased gradually.
- The human mental stress testing method of any one of claims 1 to 5, wherein a matching intervention table is provided, a plurality of intervention measures corresponding to the evaluation pressure scores are provided in the matching intervention table, and the intervention measures corresponding to the evaluation pressure scores calculated according to the trained and adjusted evaluation parameters are obtained and the intervention is performed.
- Human stress testing system, for implementing the human stress testing method of claims 1 to 8, comprising:a mood letdown object, the mood letdown object being mobile;the detection sensors are arranged on the emotion releasing object and used for detecting the behavior of the human body on the emotion releasing object and sending detection data;the system comprises a data calculation center with a preset mental stress assessment algorithm, wherein the calculation algorithm is used for calculating detection data through at least one preset assessment parameter, assessing the mental stress of a human body, continuously training and adjusting the assessment parameter, and determining a final assessment parameter, so that the assessment error of the mental stress assessment algorithm with the final assessment parameter statistically meets the medically approved error range;and the data computing center evaluates the mental stress of the human body by using a mental stress evaluation algorithm with final evaluation parameters to obtain a test stress score.
- The human stress-testing system of claim 9, wherein the system comprises:the data calculation center calculates the detection data and obtains the pre-estimated pressure value;actual stress scores obtained after passing the artificial stress test;the error evaluation unit is used for evaluating at least one error grade of the actual pressure value and the estimated pressure value;a parameter feedback modification unit for adjusting the evaluation parameter according to the corresponding relation table of the preset error grade and the evaluation parameter adjustment value;the data computing center reevaluates the human body mental pressure according to the evaluation parameters adjusted by the parameter feedback modification unit, and continuously evaluates the human body pressure after multiple adjustments, so that the error between the actual pressure value and the estimated pressure value is gradually reduced until the error is within a medically approved controllable range.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116636846A (en) * | 2023-05-29 | 2023-08-25 | 秦皇岛市惠斯安普医学系统股份有限公司 | Mental stress monitoring and intervention management system |
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| WO2021097731A1 (en) | 2021-05-27 |
| CN113194829B (en) | 2023-07-07 |
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