CN113081656A - Intelligent massage chair and control method thereof - Google Patents
Intelligent massage chair and control method thereof Download PDFInfo
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Abstract
The invention discloses an intelligent massage chair and a control method thereof, wherein the intelligent massage chair acquires physiological data of a user in real time, obtains the corresponding emotional condition of the user through model conversion calculation, and then obtains the change condition of the relaxation degree of the user in the massage process, so that the working state of the massage chair can be automatically adjusted according to the change condition of the relaxation degree under the condition that the user selects an automatic regulation and control mode, and different and targeted massage schemes are provided for different users.
Description
Technical Field
The invention relates to the technical field of massage equipment, in particular to an intelligent massage chair capable of monitoring massage effects and automatically adjusting massage states and a control method thereof.
Background
The massage chair is a leisure household device. With the development of society, the work and life of people are accelerated, and more people use the massage armchair to massage, relax the body and mind and adjust the physiological state of the body.
The existing massage chair generally adjusts gears according to the autonomous control of a user or adjusts working gears according to a set program, and the working gears cannot be adjusted in real time according to the physical conditions of different users, so that the use experience of some users in special states is poor, and the specific massage time is difficult to control according to the physical conditions of the users; for example, for users with extremely high fatigue levels, it is difficult to relieve their fatigue in their general massage gears, and for users with particularly sensitive body, the higher gear shift levels can lead to excessive sensations such as pain.
For the above reasons, the present inventors have made intensive studies on the existing massage chairs, and have awaited to design an intelligent massage chair and a control method thereof, which can solve the above problems, start a massage work at a lower gear, and adjust a working gear in real time according to a feedback state of a user.
Disclosure of Invention
In order to overcome the problems, the inventor of the present invention has made intensive research and designed an intelligent massage chair and a control method thereof, wherein the intelligent massage chair acquires physiological data of a user in real time, obtains a corresponding emotional condition of the user through model conversion calculation, and obtains a change condition of a relaxation degree of the user in a massage process according to the emotional condition, so that the working state of the massage chair can be automatically adjusted according to the change condition of the relaxation degree under the condition that the user selects an automatic regulation and control mode, thereby providing different and targeted massage schemes for different users, and completing the present invention.
Specifically, the invention aims to provide an intelligent massage armchair, which comprises an RRI acquisition module 1, an emotion recognition module 2, a relaxation feedback module 3 and a control module 4;
the RRI acquisition module 1 is configured to acquire the heart beat interval of the user in real time,
the emotion recognition module 2 is used for judging the emotion condition of the user in real time according to the heart beating interval of the user;
the relaxation feedback module 3 is used for measuring and calculating the relaxation degree of the user in real time according to the emotional condition of the user;
the control module 4 is used for controlling the working state of the massage chair according to the relaxation degree of the user in real time.
The RRI acquisition module 1 keeps synchronous with the action of the massage chair in the data acquisition process, and only uses the data measured in a non-extrusion state.
Wherein, the emotion recognition module 2 is obtained by learning through the following steps:
step 4, selecting available data from the basic data in the unified format meeting the requirements;
and 5, acquiring an emotion recognition module according to the available data in the step 4.
Wherein, the step 3 of judging whether the basic data in the unified format meets the requirement comprises the following substeps:
the substep 2, using the data in the learning group to flush the model, verifying the model one by using each data in the inspection group, and respectively recording the verification result of each data in the inspection group;
and substep 4, calculating the total passing rate of all basic data verification results in the unified format, wherein when the total passing rate is greater than 80%, the basic data in the unified format meets the requirement, otherwise, deleting the basic data in the unified format, and repeating the step 1 and the step 2.
The substep a is repeated for multiple times in substeps 1-3, and a test group consisting of different basic data in a uniform format is obtained when substep 1 is repeated each time; enabling the basic data in each uniform format to correspond to a plurality of verification results, and then respectively calculating the average passing rate corresponding to the basic data in each uniform format;
substep b, finding and hiding 1 piece of basic data with the lowest average passing rate and in the unified format, executing substeps 1-4 again by using the remaining basic data in the unified format, observing whether the total passing rate is increased compared with that before hiding the data, if the total passing rate is increased, deleting the hidden basic data in the unified format, and executing substep c; if the total passing rate is not improved, recovering the hidden data, selecting and hiding basic data in a uniform format with the second lowest average passing rate, and repeating the above processes until the total passing rate is improved;
and c, after the total passing rate is improved, repeating the substep a and the substep b on the basis of the residual basic data in the unified format, and after the total passing rate is improved, continuously repeating the substep a and the substep b on the basis of the current residual basic data in the unified format until the total passing rate reaches over 90 percent or the deleted basic data in the unified format reaches 20 percent of the basic data in the unified format, wherein the residual basic data in the unified format is the available data.
In the relaxation feedback module 3, the degree of relaxation is divided into at least three grades according to the specific numerical value of the degree of relaxation, namely, the degree of relaxation is not obtained, the degree of relaxation is obtained, but the degree of fatigue is still obtained, and the degree of maximum relaxation is obtained.
Wherein, in the control module 4, the working state of the massage chair is controlled according to the change rate of the relaxation degree of the user in a specific time period after the massage is started and the level of the relaxation degree of the user in the specific time period.
When the relaxation degree of the user is not relaxed and the change rate of the relaxation degree of the user in a specific time period after the massage is started is below a standard value, the massage force is increased through the control module 4 until the change rate of the relaxation degree of the user reaches the standard value, and then the massage force is adjusted to an initial state;
when the user does not relax and the massage process is finished, the massage time is prolonged by the control module 4.
Wherein the specific time is 2 to 3 minutes, preferably 2 minutes.
Wherein, intelligence massage armchair still includes display device, and it is used for showing the massage scheme of relaxation degree numerical value and control module 4 control in real time.
The invention also provides a control method of the intelligent massage armchair, which comprises the following steps:
step one, the heart beat interval of the user is collected in real time through an RRI collection module 1,
step two, judging the emotional condition of the user in real time according to the heart beating interval of the user through an emotion recognition module 2;
thirdly, calculating the relaxation degree of the user in real time according to the emotional condition of the user through a relaxation feedback module 3;
and step four, controlling the working state of the massage chair in real time according to the relaxation degree of the user through the control module 4.
The invention has the advantages that:
(1) the intelligent massage armchair provided by the invention can monitor the relaxation degree of a user in real time, so that the working state of the massage armchair can be adjusted in real time according to the relaxation degree;
(2) according to the intelligent massage armchair provided by the invention, based on the heart beating interval of the user, the change condition of the relaxation degree is deduced by adopting the model obtained by washing a large number of samples, and the change condition has extremely high accuracy, so that the adjustment of the working state of the massage armchair is scientific and effective.
Drawings
Fig. 1 is a block diagram illustrating an overall structure of an intelligent massage chair according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating cardiac beat intervals of a user in an embodiment of the present invention;
FIG. 3 shows a schematic diagram of the emotional condition of a user in an embodiment of the invention;
FIG. 4 is a schematic diagram illustrating the degree of relaxation of a user in an embodiment of the present invention;
fig. 5 is a schematic view showing the working state of the massage chair in the embodiment of the invention.
The reference numbers illustrate:
1-acquisition Module
2-emotion recognition module
3-relaxation feedback module
4-control Module
Detailed Description
The invention is explained in more detail below with reference to the figures and examples. The features and advantages of the present invention will become more apparent from the description.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
According to the intelligent massage armchair provided by the invention, as shown in fig. 1, the intelligent massage armchair comprises an RRI acquisition module 1, an emotion recognition module 2, a relaxation feedback module 3 and a control module 4;
the RRI acquisition module 1 is configured to acquire the heart beat interval of the user in real time,
the emotion recognition module 2 is used for judging the emotion condition of the user in real time according to the heart beating interval of the user;
the relaxation feedback module 3 is used for measuring and calculating the relaxation degree of the user in real time according to the emotional condition of the user;
the control module 4 is used for controlling the working state of the massage chair according to the relaxation degree of the user in real time.
In a preferred embodiment, the squeezing or vibration during the massage process may cause the blood flow in the human body to change, and the contact part between the human body and the sensor may rub due to displacement, so the RRI acquisition module 1 keeps synchronization with the action of the massage chair during the data acquisition process, and only uses the data measured in the non-squeezing state, thereby ensuring the accuracy of the acquired data and eliminating the influence of the acquired noise on the control effect.
The squeezing/non-squeezing state on the massage chair is transmitted to the RRI acquisition module through the data path by the massage chair, so that the RRI acquisition module can acquire the specific time for data acquisition. The RRI acquisition module can be wrapped on the wrist of a user so as to acquire the pulse beating condition of the wrist of the user in real time.
In a preferred embodiment, the heart beating intervals of the user detected by the RRI acquisition module in real time are input into the emotion recognition module, so that the emotional condition of the user, including the emotional agitation degree and the emotional valence, can be obtained in real time. The emotion recognition module is mainly a calculation model obtained through washing of a large number of samples.
The emotion recognition module 2 is obtained by learning through the following steps:
step 4, selecting available data from the basic data in the unified format meeting the requirements;
and 5, acquiring an emotion recognition module according to the available data in the step 4.
In a preferred embodiment, the collection device comprises a wearable bracelet and a smart watch. Preferably, the collecting device may further comprise a massage chair, a treadmill or the like. The physiological data are collected by the collecting device and the label data are recorded, all the data can be transmitted to a remote server in real time for statistical storage, and a storage chip can be integrated in the collecting device for real-time storage and calculation processing.
In a preferred embodiment, in step 1, two sets of data, namely an activity index of sympathetic nerves and an activity index of parasympathetic nerves, are output according to the collected corresponding conversion of each heart beat interval, so that the scheme in the application has finer time granularity.
In step 1, the two nerves jointly influence the heart beat and the periodic mutual pre-image sound of the nerve activity finally constitutes the heart rate variability.
In a preferred embodiment, the emotional arousal tag is provided with a plurality of values capable of representing emotional arousal degrees, and the corresponding values can be selected according to actual conditions, and preferably, the emotional arousal tag is provided with 5-10 value gears, and the closest value gear is selected according to actual conditions of participants. The emotional arousal label is characterized by emotional arousal degree, the lowest numerical value represents complete calmness, and the larger numerical value represents the more violent emotion.
The emotion valence tag is provided with a plurality of numerical values capable of representing emotion valence, the corresponding numerical values can be selected according to actual conditions, preferably, the emotion valence tag is provided with 2-10 numerical value gears, and the closest numerical value gear is selected according to actual conditions of participants. The emotion valence labels indicate the positive and negative degrees of emotion, the lowest value represents the most negative, and the larger value represents the more positive emotion. The data formats in the two emotion valence tags with the same numerical value gear are uniform, and the data formats in the two emotion awakening tags with the same numerical value gear are uniform.
Preferably, the normalized emotional arousal score is adopted in the emotional arousal tag as an original tag score;
preferably, the emotion titer tag adopts the PANAS standard score as an original tag score, wherein the positive emotion: average 29.7, standard deviation: 7.9; negative emotions: average 14.8, standard deviation 5.4.
Further preferably, in both the emotional arousal tag and the emotional valence tag, 10 parts are divided according to the frequency of data distribution by plus or minus 1.96 standard deviation ranges of the numerical range.
Preferably, in step 2, the emotional tags include an emotional arousal tag and an emotional valence tag, which may be provided separately or simultaneously in the form of coordinates or a chart. The emotion awakening tag is used for recording emotion awakening data, and the emotion valence tag is used for recording emotion valence data.
Preferably, in step 2, the integrated neural activity indicator is related to an activity indicator of sympathetic nerves and an activity indicator of parasympathetic nerves, and each integrated neural activity indicator includes one or more of the following data: an activity index of the sympathetic nerve, an activity index of the parasympathetic nerve, a quotient of the activity index of the sympathetic nerve and the activity index of the parasympathetic nerve, a sum of the activity index of the sympathetic nerve and the activity index of the parasympathetic nerve, a difference between the activity index of the sympathetic nerve and the activity index of the parasympathetic nerve, and the like.
In the application, the collection frequency of the comprehensive neural activity index data is high, and 60-90 or even more groups of the comprehensive neural activity index data can be provided every minute.
The emotional tags are collected relatively infrequently, and may be collected once an hour, or 2-5 times a day. Therefore, each emotion tag data corresponds to a plurality of integrated neural activity index data, and one emotion tag data and the plurality of integrated neural activity index data corresponding to the emotion tag data are combined together to form one basic data. Wherein each emotion tag data comprises emotional arousal data and emotional valence data.
In a preferred embodiment, the value steps in the emotional valence tag and the emotional arousal tag may be the same or different, and a mismatch or data misalignment problem may occur during data statistics, for this reason, in step 3, adjusting the format of the basic data mainly includes adjusting the value and the value step in the emotional tag data; specifically, the number of standard numerical value gears is set, if the number of standard numerical value gears is set to 5 numerical value gears, the numerical value gears in the adjustment basic data are adjusted to 5, the gear numerical value selected from the basic data is adjusted to the gear numerical value under the condition of 5 numerical value gears according to the proportion, and the gear numerical value is rounded up when the gear numerical value cannot be divided.
In a preferred embodiment, the step 3 of determining whether the basic data in the unified format meets the requirement includes the following sub-steps:
the substep 2, flushing the model by using the data in the learning group, verifying the model one by using each data in the checking group, and respectively recording the verification result of each data in the checking group, wherein preferably, the verification result comprises a verification pass and a verification fail; the verification is passed by bringing the comprehensive nerve activity index data of the basic data in the same format in the test group into the model, and the obtained emotion label data is consistent with the emotion label data in the basic data, namely, the emotion excitement degree and the emotion valence are consistent; the verification failure refers to that the comprehensive nerve activity index data of the basic data in the test group is brought into the model, and the obtained emotion label data is inconsistent with the emotion label data in the basic data, namely, the emotion stimulating degree and/or the emotion titer are inconsistent;
substep 4, calculating the total passing rate of the verification results of all the basic data in the uniform format, wherein the total passing rate is the ratio of the verification number of the basic data in the uniform format to the verification number of the basic data in the uniform format; when the total passing rate is not more than 80%, the basic data in the unified format is considered to be not in accordance with the basic requirements, all the basic data are abandoned, the step 1 and the step 2 are repeated, and new basic data are obtained again; and when the result in the sub-step 4, namely the total passing rate is more than 80%, the basic data in the unified format is considered to meet the use requirement, and the next step of processing can be carried out.
In a preferred embodiment, the step 4 of obtaining available data comprises the following sub-steps:
substep a, repeating substeps 1-3 in step 3 for multiple times, and obtaining a checking group consisting of different basic data with uniform format when substep 1 is repeated each time, namely all checking groups are different; preferably, the substeps 1-3 are repeated for 8-15 times, so that each basic data in the unified format corresponds to a plurality of verification results, and then the average passing rate corresponding to each basic data in the unified format is respectively calculated; the average passing rate corresponding to the basic data in the unified format is the ratio of the number of passing verification in the verification results corresponding to the basic data in the unified format to the total number of the verification results corresponding to the basic data in the unified format.
Step b, finding and hiding 1 case of basic data with the lowest average passing rate and in the unified format, and hiding one case arbitrarily when the average passing rates of multiple cases of basic data with the unified format are consistent and lowest, wherein the hidden data do not participate in any calculation processing before being recovered; finding and utilizing the residual basic data in the unified format to execute substeps 1-4 again, observing whether the total passing rate is increased compared with that before hiding the data, if the total passing rate is increased, deleting the hidden basic data in the unified format, and executing substep c; if the total passing rate is not improved, recovering the hidden data, and selecting and hiding basic data in the unified format with the second lowest average passing rate, wherein if the average passing rates of a plurality of basic data in the unified format are the same and the lowest, the basic data in the unified format with the lowest hit rate can be selected; repeating the processes until the total passing rate is increased;
after the total passing rate is increased, repeating the substep a and the substep b on the basis of the remaining basic data in the unified format, and after the total passing rate is increased, continuously repeating the substep a and the substep b on the basis of the current remaining basic data in the unified format until the total passing rate reaches more than 90%, preferably more than 95%; or until the deleted basic data in the unified format reaches 20% of the total basic data in the unified format, the remaining basic data in the unified format is the available data.
Preferably, the models in the substep 2 include most models with supervised learning, and the washing process of the models includes comprehensive judgment of a plurality of supervised models, and the specific washing process includes, but is not limited to, washing methods using linear regression, support vector machine, gradient descent method, naive bayes classification, decision tree classification, AdaBoost, XGBoost, multilayer neural network, and the like. Preferably, the average value of 2 results which are closer to each other in the results of the 3 models of the multilayer neural network with the 3-4 layers, the C4.5 decision tree and the XGboost is used as the output value of each flushing, namely, the multilayer neural network with the 3-4 layers, the C4.5 decision tree and the XGboost are combined into the most preferable model, namely, the model with high ecological utility. Preferably, in the present application, the multi-layer neural network selects a DNN neural network.
In the step 5, in the process of obtaining the emotion recognition module, the comprehensive nerve activity index data and the emotion awakening data in each available data are spliced into a data segment which is used as a learning material, and an emotion awakening prediction model is obtained through machine learning;
splicing the comprehensive nerve activity index data and the emotion valence data in each available data into a data segment, using the data segment as a learning material, and obtaining an emotion valence prediction model through machine learning; the emotion recognition module comprises the emotion awakening prediction model and an emotion valence prediction model.
In a preferred embodiment, in step 5, in the learning process of the emotion awakening prediction model and the emotion valence prediction model, a multilayer neural network model, a C4.5 decision tree model and an XGboost model of a 3-4 layer structure are established by using the comprehensive neural activity indexes and the label data at the same time, so as to obtain a multilayer neural network model, a decision tree model and an XGboost calculation module model, a combination of the three models is used as an emotion recognition module, and the output of the emotion recognition module is the average value of two closest output values in the outputs of the three models. For example, for a set of data, three models each give an output of 8, 20, and 7, and the output 7 and the output 8 are close to each other, then the output 7 of the final model is, i.e., the average of 7 and 8, and rounded down.
In a preferred embodiment, in steps 1-5, 1000 participants of each age are tracked for 2 weeks to 2 months to obtain tracking data. The physiological data of the participants come from wearable devices such as smart watches and the like and scanning sensors, and the scoring data come from the daily self-evaluation of the participants; the physiological data is continuously tracked for 24 hours in a mode of acquiring 90 seconds of data every 10 minutes; participants were asked to assess their degree of excitement and emotional valence at least 3 times per day in terms of scoring data for the emotional arousal tag and the emotional valence tag.
Acquiring a group data model through the data acquisition scheme, wherein the group data model is used for defining the overall distribution and boundary of data; it is also possible to determine a rough correspondence between input and output to form a rough judgment.
In a preferred embodiment, in the emotion recognition module 2, on the basis of establishing the emotional arousal prediction model and the emotional valence prediction model, the physiological data of the participant collected by the RRI collection module is input into the two models, so that the corresponding emotional arousal and emotional valence can be obtained. In particular, the physiological data comprises heart beat intervals, which heart beat intervals RRI are first converted into sympathetic and parasympathetic outputs of a composite neural activity indicator:
using Laguerre function recursion to make the dependent variable be a nearest RRI and make the independent variable be 8 Laguerre recursion decomposition terms X, each decomposition term is composed of an unknown coefficient G, an inferable coefficient phi and an RRI value, and the overall estimation expression is as shown in the following formula (one):
where S represents the upper limit of j, the order of the laguerre polynomial, which determines how many RRIs were used in the past to fit an expression, the more the order, the more accurate the result, preferably 9 are used; j represents the order of the orthogonal laguerre discrete time function; g (j, t) represents a coefficient matrix obtained by combining j-order Laguerre polynomials and RRI interval time in t time range, wherein the coefficient in the coefficient matrix is the coefficient of each included RRI, so that a plurality of RRIs are merged into a recursion Laguerre polynomial, and the last RRI is fitted by the past RRIs to form a recursion relation; f (t) represents the inclusion of the calculated position count for a particular interval in the sequence of interval between adjacent heart beats; n represents the serial number of the RRI traced back from this RRI；RRF(t)-nRepresenting any RRI, obtained by laguerre polynomial recursion;
an orthogonal laguerre discrete time function representing the j order, obtained by the following formula (two);
alpha is a constant, and the value of alpha is 0.2;
and calculating the nearest RRI, taking 8 RRIs as the RRIs with the same or more in the reverse direction of time, and substituting the RRIs into the RRI combination to form the RRI ═ sigma (i belongs to 0-2) Xi + ∑ (i belongs to 3-8) Xi. 8 unknown coefficients G are solved by using Kalman autoregression. Substituting sigma (i belongs to 0-2) NiGi and sigma (i belongs to 3-8) NiGi respectively represent sympathetic and parasympathetic output values in the synthetic neural activity index. The matched coefficients N are constants 39, 10, -5, 28, -17, 6, 12, 6, -7, -6, -4 respectively.
And then the comprehensive nerve activity indexes are respectively brought into an emotion awakening prediction model and an emotion valence prediction model, and the following processing is respectively carried out in the two models:
respectively substituting the comprehensive nerve activity indexes into an emotional arousal prediction model for predicting emotional arousal degree and an emotional valence prediction model for predicting emotional valence; the emotion awakening prediction model receives the comprehensive neural activity index, obtains values output by the multilayer neural network model with the 3-4 layer structure, the C4.5 decision tree model and the XGboost calculation module model respectively, selects 2 relatively close values from the three output values, and calculates the average value of the two values to serve as the output result of the emotion awakening model. The emotion valence prediction model also comprises a multilayer neural network model with a 3-4 layer structure, a C4.5 decision tree model and an XGboost calculation module model, after receiving the comprehensive neural activity index, the emotion valence prediction model obtains values respectively output by the multilayer neural network model with the 3-4 layer structure, the C4.5 decision tree model and the XGboost calculation module model, 2 relatively close values are selected from the three output values, and the average value of the two values is obtained and used as the output result of the emotion valence prediction model.
And finally, obtaining the corresponding emotional arousal degree and emotional valence degree, namely the emotional condition of the user.
In a preferred embodiment, the relaxation feedback module 3 comprises a processing model obtained by machine learning; the processing model adopts a linear model, such as support vector machine regression, elastic network regression or k-nearest neighbor classification algorithm, and preferably selects the support vector machine regression as a machine learning method; during the learning of the model, at least 5000 pieces of measurement data from different individuals are collected, including the real-time emotional condition of each individual during the massage, and the relaxation degree value of each individual during the massage. The emotional condition is obtained in real time through the emotion recognition module, and the relaxation degree value is selected from the relaxation degree label by each individual according to the actual condition in the massage process. 5-10 numerical gears are arranged in the relaxation degree label, the larger the numerical value is, the higher the relaxation degree is, and the smaller the numerical value is, the lower the relaxation degree is, for example, 10 numerical values of 1-10 can be set as 10 specific gears. Preferably, the relaxation degree value is recorded every 10-30 seconds in the massage process.
And setting the real-time emotion awakening degree and emotion valence of the tested person as input items and the relaxation degree value as a learning label in a learning program to generate a judgment model. In the using process, the emotional arousal degree value and the emotional effect value in a period of time are used as input items, and the corresponding relaxation degree value of the user can be obtained.
Preferably, in the relaxation feedback module 3, the degree of relaxation is divided into at least three levels according to specific values of the degree of relaxation, i.e. no relaxation, relaxation but fatigue and maximum relaxation, preferably, the specific value of relaxation may be set to 1-4 for no relaxation, 5-7 for relaxation but fatigue, and 8-10 for maximum relaxation.
In a preferred embodiment, in the control module 4, the working state of the massage chair is controlled according to the change rate of the relaxation degree of the user in a specific time period after the massage is started and the grade of the relaxation degree of the user at the specific time.
When the relaxation degree of the user is not relaxed and the change rate of the relaxation degree of the user in a specific time period after the massage is started is below a standard value, the massage force is increased through the control module 4 until the change rate of the relaxation degree of the user reaches the standard value, and then the massage force is adjusted to an initial state;
when the user does not relax and the massage process is finished, the massage time is prolonged by the control module 4, and the time can be prolonged by 2-3 minutes each time.
The standard value can be an average value used in machine learning of the relaxation feedback module 3, that is, the average value of the relaxation degrees of all individuals at the beginning of the massage and the average value of the relaxation degrees of all individuals at the 2-minute massage are calculated, and then the slope of the connection line of the two average values is obtained as the standard value.
The relaxation degree change rate is the slope of a line between the relaxation degree value of the user at the beginning of the massage and the relaxation degree value at the 2-minute massage.
The specific time is 2 to 3 minutes, preferably 2 minutes.
In a preferred embodiment, the intelligent massage chair further comprises a display device for displaying the relaxation degree value, the working state of the massage chair, the massage time and the massage mode in real time. The working state of the massage chair comprises information such as massage time, massage gears, massage manipulations and the like. Preferably, when the massage mode is selected to be the intelligent mode, the working state of the massage chair is controlled by the control module.
A control method of an intelligent massage chair comprises the following steps:
step one, the heart beat interval of the user is collected in real time through an RRI collection module 1,
step two, judging the emotional condition of the user in real time according to the heart beating interval of the user through an emotion recognition module 2;
thirdly, calculating the relaxation degree of the user in real time according to the emotional condition of the user through a relaxation feedback module 3;
and step four, controlling the working state of the massage chair in real time according to the relaxation degree of the user through the control module 4.
Preferably, in step 4, when the degree of relaxation of the user is not relaxed and the change rate of the degree of relaxation of the user in a specific time period after the start of the massage is below a standard value, the control module 4 increases the massage force until the change rate of the degree of relaxation of the user reaches the standard value, and then adjusts the massage force to an initial state;
when the user does not relax and the massage process is finished, the massage time is prolonged by the control module 4.
Examples
Selecting a user to experience the intelligent massage armchair for 8 minutes, acquiring the heart beating interval of the user in real time through an RRI acquisition module on the intelligent massage armchair,
the heart beating interval collected in real time is input into an emotion recognition module so as to obtain the emotion condition of the user,
the emotional condition of the user is transmitted to a relaxation feedback module in real time, and the relaxation degree of the user is further obtained in real time;
the working state of the massage chair is controlled according to the relaxation degree of the user.
Specifically, the emotion recognition module obtains:
selecting 100 participants, continuously tracking all the participants for 50 days, wearing a smart watch capable of collecting heart beat intervals, acquiring continuous 90-second heart beat interval data every 10 minutes by the smart watch, converting the heart beat interval data into activity indexes of sympathetic nerves and activity indexes of parasympathetic nerves, recording the emotion excitement degree in an emotion awakening label and the emotion valence in an emotion valence label 3 times a day, wherein the label comprises 10 numerical gears, recording the average emotion excitement degree and the emotion valence of the participants in the morning of the day, recording the average emotion excitement degree and the emotion valence of the participants in the afternoon of the day, and recording the average emotion excitement degree and the emotion valence of the participants in the evening of the day.
718650 pieces of RRI data are obtained in total, each 250 values are averaged, then the RRI data are converted into an activity index of sympathetic nerves and an activity index of parasympathetic nerves, 15000 records containing emotional arousal labels and emotional valence labels are obtained by collecting the data, and one emotional label data and a plurality of comprehensive nerve activity index data corresponding to the emotional label data are combined into one basic data, and 15000 basic data are formed in a conformal mode.
Randomly dividing all 15000 pieces of basic data into 12 parts, using one part as a test group and the other parts as learning groups, flushing the model through the learning groups, verifying the model by using data in the test group to obtain a verification result of each test group data, using data in the other parts as the test groups, repeating the steps for 10 times, ensuring that each data is distributed to the test groups once, namely each data obtains a corresponding verification result, solving that the total passing rate is 82% and is higher than 80%, and carrying out next processing.
And eliminating abnormal data in the basic data to obtain usable data, specifically,
calculating the average passing rate, dividing all basic data into 12 parts again, wherein one part is used as a test group, the other parts are used as learning groups, flushing the model through the learning groups, and verifying the model by using the data in the test groups to obtain the verification result of each data; then, the checking group and the learning group are redistributed, and the process is repeated for at least 120 times, so that each basic data is divided into the checking group for at least 10 times, namely each basic data obtains 10 corresponding verification results, and further the average passing rate of each basic data is obtained;
finding and hiding 1 piece of basic data with the lowest average passing rate, utilizing the rest 14999 pieces of basic data to execute the process of obtaining the average passing rate and the total passing rate again, observing whether the total passing rate is improved compared with the total passing rate before hiding the data, and deleting the hidden basic data with the unified format if the total passing rate is improved; if the total passing rate is not improved, recovering the hidden data, selecting and hiding the basic data with the second lowest average passing rate, and repeating the process of obtaining the total passing rate until the total passing rate is improved;
and deleting the hidden data after the hit rate is increased, continuously executing the process of obtaining the average passing rate on the basis of the rest basic data, calculating the average passing rate corresponding to each basic data, searching and hiding the data with the lowest average passing rate, obtaining the total passing rate on the basis of the data with the lowest average passing rate, and continuously repeating the removing process.
And after the hit rate is increased, deleting the hidden data, and continuously repeating the process on the basis of the rest basic data. The remaining data when the total throughput reaches 95% is called usable data.
An emotional arousal prediction model and an emotional valence prediction model are obtained from the available data, and in particular,
the method comprises the steps that a DNN neural network model is obtained by using available data to flush a DNN neural network with a 4-layer structure, a C4.5 decision tree model is obtained by using available data to flush a C4.5 decision tree, an XGboost calculation module model is obtained by using available data to flush an XGboost calculation module, and the three models are combined to form a prediction model; when the prediction model receives a new comprehensive neural activity index, copying the received information into 3 parts, and respectively transmitting the 3 parts to a DNN neural network model, a C4.5 decision tree model and an XGboost calculation module model with a 4-layer structure, wherein the output value of the prediction model is the average value of 2 closer values in the output of 3 models given by the three models, so that an emotional arousal prediction model and an emotional valence prediction model are obtained.
The relaxation feedback module adopts the same data processing process as the emotion recognition module, collects the measurement data of 5000 users, namely the real-time emotion conditions in the massage process, and also collects the relaxation degree values of all the users in the massage process, wherein the specific relaxation degree value can be selected from numbers of 0-3, the collection time of each user is 8 minutes, and the relaxation degree value is recorded every 14 seconds; and taking the real-time emotion awakening degree and emotion valence of the user as input items, and taking the relaxation degree value as a learning label to obtain a relaxation feedback module.
In an embodiment, the cardiac beat intervals acquired by the RRI acquisition module within 8 minutes of the user are as shown in fig. 2; in the figure, the abscissa represents seconds, and the ordinate represents the interval of heart beats in milliseconds;
inputting the collected data into an emotion recognition module to obtain the emotional condition of the user within 8 minutes as shown in figure 3, wherein the abscissa unit is second, the ordinate represents the emotional arousal degree value output by the emotion recognition module, the range is 0-200, and the higher the numerical value is, the higher the emotional arousal degree is;
inputting the emotional state into a relaxation feedback module to obtain the change of the relaxation degree of the user within 8 minutes as shown in fig. 4, wherein the abscissa unit is second, the ordinate represents the relaxation degree, 0-0.5 of the relaxation degree represents the state of not being relaxed, 0.5-1.5 represents the state of being relaxed but having fatigue, and 1.5-2.5 represents the state of being greatly relaxed;
then, the above-mentioned change information of the degree of relaxation is inputted into the control module, and the obtained working state of the massage chair is as shown in fig. 5, in the figure, the abscissa unit is second, and the ordinate represents the massage force of the massage chair, wherein 3.5 is the highest gear, i.e. the maximum massage force, and 0.5 is the lowest gear, i.e. the softest massage force.
According to the embodiment, the relaxation degree of the user is gradually improved through the massage of the massage chair, the massage strength of the massage chair is gradually relieved, and more proper massage experience is provided for the user.
After the user finishes the 8-minute experience of the intelligent massage armchair, an evaluation table is filled, and whether the adjustment effect of the massage intensity of the massage armchair for automatically adjusting the massage intensity is satisfied is evaluated in the second half of the experience.
Further inviting 99 users to experience the intelligent massage armchair, and counting whether the massage armchair is satisfied or not to automatically adjust the adjustment effect of the massage intensity, so that 93 parts of satisfied evaluation, 3 parts of unsatisfied evaluation and 4 parts of so-called evaluation are obtained, and therefore, the control method of the intelligent massage armchair can provide targeted and appropriate massage intensity for the users, and has extremely high judgment accuracy which can reach 93%.
The present invention has been described above in connection with preferred embodiments, but these embodiments are merely exemplary and merely illustrative. On the basis of the above, the invention can be subjected to various substitutions and modifications, and the substitutions and the modifications are all within the protection scope of the invention.
Claims (10)
1. An intelligent massage chair is characterized by comprising an RRI acquisition module (1), an emotion recognition module (2), a relaxation feedback module (3) and a control module (4);
the RRI acquisition module (1) is used for acquiring the heart beat interval of a user in real time,
the emotion recognition module (2) is used for judging the emotion condition of the user in real time according to the heart beating interval of the user;
the relaxation feedback module (3) is used for measuring and calculating the relaxation degree of the user in real time according to the emotional condition of the user;
the control module (4) is used for controlling the working state of the massage chair according to the relaxation degree of the user in real time.
2. The intelligent massage chair of claim 1,
the RRI acquisition module (1) keeps synchronous with the action of the massage chair in the data acquisition process, and only uses the data measured in a non-extrusion state.
3. The intelligent massage chair of claim 1,
the emotion recognition module (2) is obtained by learning through the following steps:
step 1, collecting physiological data through a collection device, wherein the physiological data comprises heart beating intervals and converting the physiological data into activity indexes of sympathetic nerves and parasympathetic nerves;
step 2, setting an emotion awakening tag and an emotion valence tag, recording specific emotion arousing degree in the emotion awakening tag, recording specific emotion valence in the emotion valence tag, and combining the comprehensive neural activity index data and the emotion tag into basic data;
step 3, adjusting the format of the basic data to obtain basic data with a uniform format, and judging whether the basic data with the uniform format meets the requirements or not;
step 4, selecting available data from the basic data in the unified format meeting the requirements;
and 5, acquiring an emotion recognition module according to the available data in the step 4.
4. The intelligent massage chair of claim 3,
the step 3 of judging whether the basic data in the unified format meets the requirements comprises the following substeps:
substep 1, dividing all basic data with uniform format into a learning group and a checking group according to a preset proportion at random;
the substep 2, using the data in the learning group to flush the model, verifying the model one by using each data in the inspection group, and respectively recording the verification result of each data in the inspection group;
substep 3, repeating substep 1 and substep 2, wherein the basic data in the unified format once distributed to the test group is not distributed to the test group any more, and each basic data in the unified format is ensured to verify the model flushed by the data in the learned group in the test group until the verification results corresponding to all the basic data in the unified format are obtained;
and substep 4, calculating the total passing rate of all basic data verification results in the unified format, wherein when the total passing rate is greater than 80%, the basic data in the unified format meets the requirement, otherwise, deleting the basic data in the unified format, and repeating the step 1 and the step 2.
5. The intelligent massage chair as claimed in claim 3, wherein the step 4 of obtaining available data comprises the following sub-steps:
substep a, repeating substeps 1-3 for a plurality of times, and obtaining a test group consisting of different basic data with uniform formats when substep 1 is repeated each time; enabling the basic data in each uniform format to correspond to a plurality of verification results, and then respectively calculating the average passing rate corresponding to the basic data in each uniform format;
substep b, finding and hiding 1 piece of basic data with the lowest average passing rate and in the unified format, executing substeps 1-4 again by using the remaining basic data in the unified format, observing whether the total passing rate is increased compared with that before hiding the data, if the total passing rate is increased, deleting the hidden basic data in the unified format, and executing substep c; if the total passing rate is not improved, recovering the hidden data, selecting and hiding basic data in a uniform format with the second lowest average passing rate, and repeating the above processes until the total passing rate is improved;
and c, after the total passing rate is improved, repeating the substep a and the substep b on the basis of the residual basic data in the unified format, and after the total passing rate is improved, continuously repeating the substep a and the substep b on the basis of the current residual basic data in the unified format until the total passing rate reaches over 90 percent or the deleted basic data in the unified format reaches 20 percent of the basic data in the unified format, wherein the residual basic data in the unified format is the available data.
6. The intelligent massage chair of claim 1,
in the relaxation feedback module (3), the degree of relaxation is divided into at least three grades according to the specific numerical value of the degree of relaxation, namely, the degree of relaxation is not obtained, the degree of relaxation is obtained, but the fatigue feeling is still obtained, and the maximum degree of relaxation is obtained.
7. The intelligent massage chair of claim 1,
in the control module (4), the working state of the massage chair is controlled according to the change rate of the relaxation degree of the user in a specific time period after the massage is started and the level of the relaxation degree of the user in the specific time period.
8. The intelligent massage chair of claim 7,
when the relaxation degree of the user is not relaxed and the change rate of the relaxation degree of the user in a specific time period after the massage is started is below a standard value, the massage force is increased through the control module (4) until the change rate of the relaxation degree of the user reaches the standard value, and then the massage force is adjusted to an initial state;
when the user does not relax and the massage process is finished, the massage time is prolonged through the control module (4).
9. The intelligent massage chair of claim 7,
the specific time is 2 to 3 minutes, preferably 2 minutes.
10. A control method of an intelligent massage chair, which is characterized in that,
the method comprises the following steps:
step one, the heart beat interval of a user is collected in real time through an RRI collection module (1),
step two, judging the emotional condition of the user in real time according to the heart beating interval of the user through an emotion recognition module (2);
thirdly, calculating the relaxation degree of the user in real time according to the emotional condition of the user through a relaxation feedback module (3);
and step four, controlling the working state of the massage chair in real time according to the relaxation degree of the user through the control module (4).
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| CN113908004A (en) * | 2021-09-14 | 2022-01-11 | 哈工天愈（中山）机器人有限公司 | Control method of novel sleep disorder rehabilitation physiotherapy robot and robot |
| CN113908004B (en) * | 2021-09-14 | 2024-05-14 | 哈工天愈（中山）机器人有限公司 | Novel control method of sleep disorder rehabilitation physiotherapy robot and robot |
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