CN119366941A

CN119366941A - A method, device, equipment and medium for detecting waist muscle fatigue

Info

Publication number: CN119366941A
Application number: CN202411801963.1A
Authority: CN
Inventors: 王炳坤; 姚方来
Original assignee: De Rucci Healthy Sleep Co Ltd
Current assignee: De Rucci Healthy Sleep Co Ltd
Priority date: 2024-12-09
Filing date: 2024-12-09
Publication date: 2025-01-28

Abstract

The embodiment of the invention discloses a waist muscle fatigue detection method, device, equipment and medium. The method comprises the steps of obtaining electromyographic signals and temperature data of waist muscles of a target user based on waist detection equipment, obtaining a first fatigue degree grade of subjective evaluation of the target user, determining a second fatigue degree grade of the waist muscles according to the electromyographic signals and the temperature data, determining a target fatigue degree grade of the target user according to the first fatigue degree grade and the second fatigue degree grade, and adjusting a working mode of an intelligent mattress according to the target fatigue degree grade, wherein the waist detection equipment is associated with the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxing scheme is provided, and the sleeping quality of a target user is improved.

Description

Waist muscle fatigue detection method, device, equipment and medium

Technical Field

The invention relates to the technical field of data processing, in particular to a waist muscle fatigue detection method, device, equipment and medium.

Background

The modern life is rapid in rhythm, high in working pressure and increasingly serious in waist muscle fatigue. Prolonged sitting or incorrect posture can easily lead to fatigue of waist muscles, thereby affecting the quality of life and sleep of people.

Most of the existing waist fatigue evaluation methods are subjective evaluation, and lack objective and accurate detection means. In addition, the existing mattress adjusting technology is difficult to accurately adjust according to the muscle fatigue condition of an individual, so that the waist fatigue cannot be effectively relieved.

Disclosure of Invention

The invention provides a waist muscle fatigue detection method, a device, equipment and a medium, which can intelligently adjust the working mode of a mattress by monitoring the fatigue grade state of waist muscles in real time so as to provide a personalized waist muscle relaxation scheme and improve the sleeping quality of a target user.

According to an aspect of the present invention, there is provided a lumbar muscle fatigue detection method including:

Acquiring electromyographic signals and temperature data of lumbar muscles of a target user based on lumbar detection equipment, and acquiring a first fatigue degree level of subjective assessment of the target user;

Determining a second level of fatigue of the lumbar muscle based on the electromyographic signals and the temperature data;

Determining a target fatigue level of a target user according to the first fatigue level and the second fatigue level;

and adjusting the working mode of the intelligent mattress according to the target fatigue degree level, wherein the waist detection equipment is associated with the intelligent mattress.

According to another aspect of the present invention, there is provided a lumbar muscle fatigue detection device comprising:

The data acquisition module is used for acquiring electromyographic signals and temperature data of waist muscles of a target user based on the waist detection equipment and acquiring a first fatigue degree grade of subjective evaluation of the target user;

a second fatigue level determination module for determining a second fatigue level of the lumbar muscle based on the electromyographic signal and the temperature data;

The target fatigue degree grade determining module is used for determining the target fatigue degree grade of the target user according to the first fatigue degree grade and the second fatigue degree grade;

and the intelligent mattress adjusting module is used for adjusting the working mode of the intelligent mattress according to the target fatigue degree level, wherein the waist detecting equipment is associated with the intelligent mattress.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor, and

A memory communicatively coupled to the at least one processor, wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the lumbar muscle fatigue detection method according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for detecting lumbar muscle fatigue according to any of the embodiments of the present invention.

According to the technical scheme, the lumbar muscle of the target user is obtained through the electromyographic signals and the temperature data based on the lumbar detection equipment, the first fatigue degree grade of subjective evaluation of the target user is obtained, the second fatigue degree grade of the lumbar muscle is determined according to the electromyographic signals and the temperature data, the target fatigue degree grade of the target user is determined according to the first fatigue degree grade and the second fatigue degree grade, and the working mode of the intelligent mattress is adjusted according to the target fatigue degree grade, wherein the lumbar detection equipment is associated with the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxing scheme is provided, and the sleeping quality of a target user is improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for detecting fatigue of lumbar muscle according to a first embodiment of the present invention;

Fig. 2 is an exemplary view of a waist detecting apparatus provided according to a first embodiment of the present invention;

fig. 3 is a flowchart of a method for detecting fatigue of lumbar muscle according to a second embodiment of the present invention;

Fig. 4 is a schematic structural view of a lumbar muscle fatigue detection device according to a third embodiment of the present invention;

Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and "object" in the description of the present invention and the claims and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a lumbar muscle fatigue detection method according to an embodiment of the present invention, where the method may be performed by a lumbar muscle fatigue detection device, which may be implemented in hardware and/or software, and the lumbar muscle fatigue detection device may be configured in an electronic device with data processing capability. As shown in fig. 1, the method includes:

s110, acquiring electromyographic signals and temperature data of lumbar muscles of a target user based on a lumbar detection device, and acquiring a first fatigue degree level of subjective assessment of the target user.

Wherein the waist detection apparatus may be a device belt for fatigue detection of the user's waist muscles. The waist detecting apparatus in this embodiment may include a myoelectric sensor and a temperature sensor. The electromyographic signal can be a superposition of Motion Unit Action Potentials (MUAP) in a plurality of muscle fibers in time and space, and is a bioelectric signal generated along with muscle contraction actions. The surface electromyographic signal (sEMG) is an electrical signal accompanied by muscle contraction, is a combined effect of the electrical activity on the superficial muscle EMG and the nerve trunk on the skin surface, and can reflect the activity of the nerve muscle to a certain extent. The temperature data may be data obtained by detecting the temperature condition of the lumbar muscle. In this embodiment, the myoelectric signal of the lumbar muscle of the target user may be acquired by the myoelectric sensor in the lumbar detection device, and the temperature data of the lumbar muscle of the target user may be acquired by the temperature sensor in the lumbar detection device. The target user may refer to a user who is currently detecting lumbar muscles using the lumbar detection device.

An example of the lumbar detection apparatus in this embodiment is shown in fig. 2, which can also be understood as a lumbar muscle detection device band, the gray array may be a flexible electrode array for detecting sEMG signals, and the white frame may be a temperature sensor for acquiring temperature data. Myoelectric signals (EMG) and temperature data of lumbar muscles can be acquired in real time through a myoelectric sensor and a temperature sensor in the lumbar detection device in the embodiment. In the embodiment, when data is actually acquired, basic data acquisition can be performed, namely basic EMG and temperature data are acquired in a rest state, and initial MVC is recorded. It is also possible to design a task of longer duration and increasing strength, such as standing, sitting, bending over or carrying a weight for periodic measurements. And periodically measuring EMG, temperature and MVC in the task process, and simultaneously enabling a target user to perform subjective fatigue scoring. The arrangement of the sensors in this embodiment should be reasonable to ensure the accuracy and stability of signal acquisition.

The first fatigue level may be a fatigue level obtained by subjective evaluation by the target user according to an individual. In this embodiment, the target user performs subjective evaluation according to a subjective evaluation method according to an individual to obtain a first fatigue level of the target user.

The subjective assessment in this example can be recorded by the Borg fatigue scale (RPE scale) and questionnaire. The Borg fatigue scale allows subjects to score fatigue on their own feel, typically using a scoring system of 0 to 10. A higher score indicates a higher degree of fatigue. The questionnaire is to let the subject describe themselves their feeling of fatigue, muscular soreness, degree of fatigue, etc. Among them, the Borg fatigue Scale (Borg Scale) is a tool for assessing subjective fatigue perception in sports, also called subjective fatigue perception assessment Scale (The Borg Rating of Perceived Exertion, RPE). In this embodiment, the first fatigue level may include 4 types of fatigue levels, specifically, the fatigue level corresponding to 0-2 is no fatigue, the fatigue level corresponding to 3-5 is slight fatigue, the fatigue level corresponding to 6-8 is moderate fatigue, and the fatigue level corresponding to 9-10 is heavy fatigue, and the target user may determine the first fatigue level by marking subjective scores in scores of 0-10 according to his/her own actual experience.

S120, determining a second fatigue degree level of the waist muscle according to the electromyographic signals and the temperature data.

The second fatigue level may be an objective fatigue level obtained by evaluating electromyographic signals and temperature data. In this embodiment, the maximum voluntary contractive force MVC, the characteristic change of the muscle signal, and the characteristic change of the temperature in the electromyographic signal may be used to perform objective evaluation, so as to obtain the corresponding second fatigue level.

In this embodiment, MVC is a method for measuring the force generated by the lumbar muscle under the condition of maximum effort, and the MVC is reduced when the muscle is tired, and the root Mean square value (RMS) of the EMG signal may be reduced, the average Frequency (Mean Frequency) or the Median Frequency (Median Frequency) may be reduced when the muscle is tired, and the temperature of the lumbar muscle may be increased after long-time exercise or tired, so that the lumbar muscle may be objectively evaluated according to the electromyographic signals and the temperature data to obtain the second fatigue degree grade.

S130, determining a target fatigue level of the target user according to the first fatigue level and the second fatigue level.

The target fatigue level may be a final fatigue level determined by the target user. In this embodiment, different weights may be set respectively for the first fatigue level determined by the subjective evaluation method and the second fatigue level determined by the objective evaluation method, so as to determine the target fatigue level of the lumbar muscle of the target user.

In this embodiment, optionally, determining the target fatigue level of the target user according to the first fatigue level and the second fatigue level includes obtaining a first weight factor corresponding to the first fatigue level and a second weight factor corresponding to the second fatigue level, and determining the target fatigue level of the target user according to the first fatigue level, the first weight factor, the second fatigue level, and the second weight factor.

The first weight factor may be a weight factor corresponding to the first fatigue degree. The second weight factor may be a weight factor corresponding to a second degree of fatigue. In this embodiment, the first weight factor and the second weight factor may be preset, and may be set according to actual requirements. Illustratively, the first weight factor may be set to 0.6 and the second weight factor may be set to 0.4.

In this embodiment, a preset first weight factor and a preset second weight factor may be obtained, and then a weighted summation process is performed according to the first fatigue level and the first weight factor, and the second fatigue level and the second weight factor, so as to obtain a target fatigue level of the target user.

For example, in this embodiment, the first fatigue level and the second fatigue level are both classified into 4 types of fatigue level, i.e., no fatigue, slight fatigue, moderate fatigue and severe fatigue, and the result obtained by weighting and summing may be respectively 1-4 scores according to the 4 types of fatigue level, where the higher the fatigue level, the higher the score thereof. The first weight factor may be set to 0.6 and the second weight factor may be set to 0.4. In this embodiment, if the result score obtained by weighted summation of the first fatigue level, the first weight factor, the second fatigue level and the second weight factor is 3.7, the fatigue level corresponding to 4 points, namely, severe fatigue, may be classified, and if the result score obtained by weighted summation of the first fatigue level, the first weight factor, the second fatigue level and the second weight factor is 3.4 (namely, lower than 3.5 points), the fatigue level corresponding to 3 points, namely, moderate fatigue, may be classified.

Through the arrangement, the final target fatigue degree grade can be determined through the manner that different weight factors are set and then weighted summation is carried out on different fatigue degree grades obtained through a subjective evaluation manner and an objective evaluation manner, and the reliability of the fatigue degree grade division is improved. Compared with the traditional mode which is limited by subjective evaluation, the embodiment provides a more objective and reliable fatigue detection means.

And S140, adjusting the working mode of the intelligent mattress according to the target fatigue degree level.

Wherein the lumbar detection device is associated with the intelligent mattress. The working mode of the intelligent mattress can refer to the hardness degree of the mattress and the vibration mode of the mattress. Illustratively, the intelligent mattress in this embodiment may include one or more air bag modules capable of adjusting hardness by inflating and deflating, and a vibration module capable of driving the mattress to vibrate, so as to adjust the hardness degree and vibration mode of the intelligent mattress. According to the embodiment, the waist detection equipment can be associated with the intelligent mattress, so that the target fatigue degree of the waist muscles is determined according to the state of the waist muscles monitored in real time, the vibration mode and the hardness degree of the mattress are intelligently adjusted according to the target fatigue degree, a personalized waist muscle relaxation scheme is provided, and the sleeping quality of a target user is improved.

Specifically, according to the estimated target fatigue degree level, the intelligent mattress system can automatically select a proper vibration or wave adjusting mode to provide a personalized lumbar muscle relaxation scheme. In this embodiment, different adjustment modes can be set according to different fatigue degrees, so as to ensure maximization of the relaxation effect. In addition, the intelligent mattress system in the embodiment not only can adjust the vibration mode, but also can automatically adjust the hardness of the mattress according to the fatigue condition of waist muscles. Such regulation can provide better support and comfort level, laminating waist curve, effectively alleviates waist pressure, promotes user's whole sleep and experiences.

According to the technical scheme, the lumbar muscle fatigue detection device comprises a lumbar muscle fatigue detection device, a lumbar muscle fatigue detection system and an intelligent mattress, wherein the lumbar muscle fatigue detection system is used for acquiring electromyographic signals and temperature data of lumbar muscles of a target user, acquiring a first fatigue degree grade of subjective evaluation of the target user, determining a second fatigue degree grade of the lumbar muscles according to the electromyographic signals and the temperature data, determining a target fatigue degree grade of the target user according to the first fatigue degree grade and the second fatigue degree grade, and adjusting a working mode of the intelligent mattress according to the target fatigue degree grade, wherein the lumbar muscle fatigue detection device is associated with the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxing scheme is provided, and the sleeping quality of a target user is improved.

Example two

Fig. 3 is a flowchart of a lumbar muscle fatigue detection method according to a second embodiment of the present invention, which is optimized based on the above-mentioned embodiment. The method comprises the steps of determining a second fatigue degree grade of the waist muscle according to electromyographic signals and temperature data, wherein the step of preprocessing the electromyographic signals and the temperature data to obtain preprocessed electromyographic signals and preprocessed temperature data, the step of extracting features of the preprocessed electromyographic signals and the preprocessed temperature data to obtain corresponding electromyographic features and temperature features, and the step of determining the second fatigue degree grade of the waist muscle according to the electromyographic features, the temperature features and a set evaluation model. As shown in fig. 3, the method includes:

s310, acquiring electromyographic signals and temperature data of lumbar muscles of a target user based on a lumbar detection device, and acquiring a first fatigue degree level of subjective assessment of the target user.

S320, preprocessing the electromyographic signals and the temperature data to obtain preprocessed electromyographic signals and preprocessed temperature data.

The preprocessing operation may include denoising, rectifying and smoothing, or normalizing. The preprocessed electromyographic signals can be electromechanical signal data obtained after denoising, rectifying, smoothing and other processing operations. The preprocessed temperature data may be temperature data obtained after denoising and normalization.

In the embodiment, the collected electromyographic signals can be subjected to pretreatment operations such as denoising, rectifying and smoothing to obtain the pretreated electromyographic signals, and the temperature data is subjected to denoising and normalization pretreatment to obtain the pretreated temperature data. In this embodiment, the electromyographic signals and the temperature data are preprocessed respectively, so that the preprocessed data are easier to extract and analyze the subsequent features.

And S330, respectively carrying out feature extraction on the preprocessed electromyographic signals and the preprocessed temperature data to obtain corresponding electromyographic features and temperature features.

The myoelectricity characteristics can comprise time domain characteristics and frequency domain characteristics, and the temperature characteristics can comprise temperature average values and temperature variation characteristics. In this embodiment, feature extraction may be performed on the preprocessed electromyographic signals to obtain time domain features and frequency domain features, and feature extraction may be performed on the preprocessed temperature data to obtain corresponding temperature average and temperature variation features.

In the embodiment, the myoelectricity characteristics comprise time domain characteristics and frequency domain characteristics, the temperature characteristics comprise temperature average values and temperature variation characteristics, and the corresponding myoelectricity characteristics and temperature characteristics are obtained by respectively carrying out characteristic extraction on the preprocessed myoelectricity signals and the preprocessed temperature data.

The time domain features may refer to features extracted directly from the time-series signal of the electromyographic signal. The time domain features in this embodiment may include Root Mean Square (RMS), mean Absolute Value (MAV), zero Crossing Rate (ZCR), and Waveform Length (WL) feature data. The Frequency domain features may include Power Spectral Density (PSD), median Frequency (MF), average Frequency (Mean Frequency), and the like feature data. The temperature average value may be average temperature data obtained by extracting temperature data. The temperature change amount characteristic amount may be a temperature change rate characteristic obtained by extracting temperature data.

In this embodiment, feature extraction may be performed on the preprocessed electromyographic signals to obtain time domain features and Frequency domain features, which are feature data such as root Mean square value (RMS), average absolute value (MAV), zero Crossing Rate (ZCR), waveform Length (WL), power Spectrum Density (PSD), median Frequency (MF), and average Frequency (Mean Frequency), respectively.

By the arrangement, the pre-processed electromyographic signals and the pre-processed temperature data can be subjected to feature extraction respectively to obtain the required feature data, so that the processing based on the feature data can be performed later.

In addition, in this embodiment, normalization processing may be performed uniformly according to a time point for each index data in the preprocessed electromyographic signal and the preprocessed temperature data. For example, the detection is performed at an hour, and the corresponding feature values are detected at one hour, or at two hours. The method can unify one data value at time points in time sequence, and if two points have one characteristic data value and three points have one characteristic data value, a matrix vector is formed, and each data value is unified to be subjected to corresponding normalization processing.

S340, determining a second fatigue degree level of the waist muscle according to the myoelectric characteristics, the temperature characteristics and the set evaluation model.

The set evaluation model can be a trained neural network model for evaluating the fatigue degree level of the waist muscle. In this embodiment, the extracted myoelectricity feature and the temperature feature may be subjected to feature fusion to form a fusion feature vector, and the second fatigue level of the waist muscle is obtained according to the fusion feature vector and the trained evaluation model.

Furthermore, in this embodiment, the extracted myoelectricity and temperature characteristics may be fused to form a comprehensive feature vector. Based on the comprehensive feature vectors, training is performed by using a machine learning model (such as a neural network) according to a preset fatigue degree grade dividing standard so as to obtain a set evaluation model for evaluating the fatigue degree grade of the waist muscle. Fatigue level grades can be divided into four grades of no fatigue, mild fatigue, moderate fatigue and severe fatigue.

In this embodiment, optionally, the second fatigue level of the lumbar muscle is determined according to the myoelectric characteristic, the temperature characteristic and the set evaluation model, and the method includes performing fusion processing on the myoelectric characteristic and the temperature characteristic to obtain a fusion characteristic vector, and inputting the fusion characteristic vector into the set evaluation model to obtain the second fatigue level of the lumbar muscle.

The fusion process may be understood as a process of a feature stitching operation. The fusion feature vector may be a feature vector obtained by performing feature stitching operation on myoelectricity features and temperature features to fuse them together. In this embodiment, feature stitching operation may be performed on myoelectric features and temperature features to obtain a fused feature vector, and then the fused feature vector is input into a trained set evaluation model, where the set evaluation model determines each feature data in the fused feature vector according to a preset fatigue level classification standard, and determines a second fatigue level of the waist muscle according to a determination result.

Through the arrangement, the second fatigue degree grade corresponding to the waist muscle can be determined through fusion of the characteristic data and the set evaluation model, so that the fatigue degree grade in an objective state is obtained, and the objectivity and reliability of the fatigue degree grade are improved.

In this embodiment, the setting the evaluation model includes optionally setting a fatigue level classification criterion, and the inputting the fusion feature vector into the setting evaluation model to obtain a second fatigue level of the lumbar muscle includes inputting the fusion feature vector into the setting evaluation model, and determining the second fatigue level of the lumbar muscle based on the fatigue level classification criterion and the fusion feature vector.

The preset fatigue level classification standard may be a preset classification standard for each fatigue level according to the feature data. In this embodiment, different division standards may be set according to different feature data in the fusion feature vector as a reference, and may be set according to actual requirements, which is not limited in this embodiment. In this embodiment, the data may be marked as different fatigue levels according to a preset fatigue classification standard.

In this embodiment, the preset fatigue grade classification standard is exemplified by MVC, myoelectric EMG characteristics and temperature variation characteristics contained in the fusion characteristic vector, and specifically may be a standard corresponding to no fatigue, that is, MVC drop amplitude is less than 10%, RMS (root mean square value) in the myoelectric EMG characteristics is not significantly changed, and temperature variation is less than 1 ℃. The criteria for mild fatigue may be a range of MVC decline amplitude between 10% -20%, a slight decline in RMS in electromyographic characteristics EMG, and a temperature change between the data range of 1 ℃ and 2 ℃. The criteria corresponding to moderate fatigue may be a range of MVC decline in magnitude between 20% -40%, a significant decline in RMS in electromyographic characteristics EMG, and a temperature change in the data range between 2 ℃ and 3 ℃. The standard corresponding to severe fatigue can be that the MVC drop amplitude is more than 40%, the RMS in the electromyographic characteristic EMG is significantly reduced, the frequency component change is obvious, and the temperature change amount is more than 3 ℃ in the data range. In this embodiment, the data ranges corresponding to different feature data may be set according to actual requirements, and the data ranges may be used as preset fatigue level classification criteria.

In this embodiment, the fusion feature vector may be input into a pre-trained set evaluation model, so that the data comparison is performed on the fusion feature vector based on a preset fatigue level classification standard in the set evaluation model, and the fatigue level of the waist muscle that is correspondingly classified, that is, the second fatigue level of the waist muscle, may be obtained according to the comparison result.

By such arrangement in the present embodiment, by combining the lumbar electromyographic signal (EMG) and the temperature data, the fatigue degree of the lumbar muscle of the user can be accurately estimated in real time by using an advanced machine learning algorithm, resulting in more accurate fatigue degree classification.

In the embodiment, optionally, the second fatigue degree grade of the waist muscle is determined based on a preset fatigue degree grade dividing standard and the fusion feature vector, and the method comprises the steps of comparing the fusion feature vector according to the preset fatigue degree grade dividing standard to obtain a comparison result, and determining the second fatigue degree grade of the waist muscle according to the comparison result.

The comparison result may be a result obtained by comparing each feature data in the fusion feature vector with a preset division standard corresponding to each fatigue level of the feature data. In this embodiment, each feature data in the fusion feature vector may be compared with a data range or size condition corresponding to each feature data in the preset fatigue level classification standard, so as to obtain each classification standard satisfied by the feature data, and determine the corresponding fatigue level according to the classification standard, so as to obtain the second fatigue level of the waist muscle.

By the arrangement, the fatigue degree grade of the objective evaluation mode can be obtained, so that the final fatigue degree grade can be determined by combining the fatigue degree grade of the objective evaluation mode with the fatigue degree grade of the subjective evaluation mode.

S350, determining the target fatigue degree grade of the target user according to the first fatigue degree grade and the second fatigue degree grade.

S360, adjusting the working mode of the intelligent mattress according to the target fatigue degree level.

Wherein the lumbar detection device is associated with the intelligent mattress.

In this embodiment, the waist detection device may be associated with the intelligent mattress, so that the intelligent mattress is adjusted according to the target fatigue level obtained by the objective evaluation and subjective evaluation, so that the intelligent mattress automatically selects a suitable vibration or wave adjustment mode, and the waist muscles are relaxed. In addition, the intelligent mattress system can adjust the hardness of the mattress according to the fatigue degree, is fit with the condition of waist muscles, and provides optimal support and comfort.

Real-time feedback and optimization can be performed in the embodiment, and the specific system can feed back the fatigue state of the waist muscles and the mattress adjusting condition to the user in real time through mobile application or other user interfaces. And continuously optimizing a vibration mode and a hardness adjustment strategy according to user feedback and use conditions.

According to the technical scheme, the myoelectric signal and the temperature data of the lumbar muscle of the target user are obtained through the lumbar detection device, the first fatigue degree grade of subjective evaluation of the target user is obtained, preprocessing operation is conducted on the myoelectric signal and the temperature data to obtain preprocessed myoelectric signals and preprocessed temperature data, feature extraction is conducted on the preprocessed myoelectric signals and the preprocessed temperature data to obtain corresponding myoelectric features and temperature features respectively, the second fatigue degree grade of the lumbar muscle is determined according to the myoelectric features, the temperature features and the set evaluation model, the target fatigue degree grade of the target user is determined according to the first fatigue degree grade and the second fatigue degree grade, and the working mode of the intelligent mattress is adjusted according to the target fatigue degree grade, wherein the lumbar detection device is related to the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxing scheme is provided, and the sleeping quality of a target user is improved.

Example III

Fig. 4 is a schematic structural view of a lumbar muscle fatigue detection device according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes:

a data acquisition module 410, configured to acquire myoelectric signals and temperature data of lumbar muscles of a target user based on a lumbar detection device, and acquire a first fatigue level of subjective assessment of the target user;

A second fatigue level determination module 420 for determining a second fatigue level of the lumbar muscle based on the electromyographic signals and the temperature data;

a target fatigue level determining module 430 for determining a target fatigue level of the target user according to the first fatigue level and the second fatigue level;

the intelligent mattress adjustment module 440 is configured to adjust an operational mode of the intelligent mattress in accordance with the target fatigue level, wherein the lumbar detection device is associated with the intelligent mattress.

Optionally, the second fatigue level determination module 420 includes:

The data preprocessing unit is used for preprocessing the electromyographic signals and the temperature data to obtain preprocessed electromyographic signals and preprocessed temperature data;

The characteristic extraction unit is used for respectively carrying out characteristic extraction on the preprocessed electromyographic signals and the preprocessed temperature data to obtain corresponding electromyographic characteristics and temperature characteristics;

and the fatigue degree grade evaluation unit is used for determining a second fatigue degree grade of the waist muscle according to the myoelectricity characteristics, the temperature characteristics and the set evaluation model.

Optionally, the myoelectric characteristics comprise time domain characteristics and frequency domain characteristics, and the temperature characteristics comprise temperature average values and temperature variation characteristics;

The device comprises a feature extraction unit, a temperature average value and a temperature variation feature, wherein the feature extraction unit is specifically used for carrying out feature extraction on the preprocessed electromyographic signals to obtain time domain features and frequency domain features, and carrying out feature extraction on the preprocessed temperature data to obtain the temperature average value and the temperature variation feature.

Optionally, the fatigue level evaluation unit includes:

the characteristic fusion subunit is used for carrying out fusion treatment on myoelectricity characteristics and temperature characteristics to obtain fusion characteristic vectors;

And the model evaluation subunit is used for inputting the fusion characteristic vector into a set evaluation model to obtain a second fatigue degree grade of the waist muscle.

Optionally, setting the evaluation model includes presetting a fatigue level grading standard;

The model evaluation subunit is specifically configured to input the fusion feature vector into a set evaluation model, and determine a second fatigue level of the waist muscle based on a preset fatigue level classification standard and the fusion feature vector.

Optionally, the model evaluation subunit is specifically configured to compare the fusion feature vectors according to a preset fatigue level classification standard to obtain a comparison result, and determine a second fatigue level of the waist muscle according to the comparison result.

Optionally, the target fatigue level determining module 430 is specifically configured to obtain a first weight factor corresponding to the first fatigue level and a second weight factor corresponding to the second fatigue level, and determine the target fatigue level of the target user according to the first fatigue level, the first weight factor, the second fatigue level, and the second weight factor.

The waist muscle fatigue detection device provided by the embodiment of the invention can execute the waist muscle fatigue detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including an input unit 16, such as a keyboard, mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, optical disk, etc., and a communication unit 19, such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the lumbar muscle fatigue detection method.

In some embodiments, the lumbar muscle fatigue detection method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described lumbar muscle fatigue detection method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the lumbar muscle fatigue detection method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), a blockchain network, and the Internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method for detecting fatigue of a waist muscle, comprising:

2. The method of claim 1, wherein determining a second level of fatigue of the lumbar muscle based on the electromyographic signals and the temperature data comprises:

Preprocessing the electromyographic signals and the temperature data to obtain preprocessed electromyographic signals and preprocessed temperature data;

Respectively extracting characteristics of the preprocessed electromyographic signals and the preprocessed temperature data to obtain corresponding electromyographic characteristics and temperature characteristics;

And determining a second fatigue degree level of the waist muscle according to the myoelectric characteristics, the temperature characteristics and a set evaluation model.

3. The method of claim 2, wherein the myoelectrical characteristics include time domain characteristics and frequency domain characteristics, and wherein the temperature characteristics include a temperature mean value and a temperature variation characteristic;

Correspondingly, the feature extraction is performed on the preprocessed electromyographic signals and the preprocessed temperature data respectively to obtain corresponding electromyographic features and temperature features, and the method comprises the following steps:

Extracting the characteristics of the preprocessed electromyographic signals to obtain time domain characteristics and frequency domain characteristics;

and extracting the characteristics of the preprocessed temperature data to obtain the characteristics of the temperature average value and the temperature variation.

4. The method of claim 2, wherein determining a second fatigue level of the lumbar muscle based on the myoelectrical characteristics, the temperature characteristics, and a set assessment model comprises:

Carrying out fusion processing on the myoelectricity characteristic and the temperature characteristic to obtain a fusion characteristic vector;

and inputting the fusion feature vector into the set evaluation model to obtain a second fatigue degree grade of the waist muscle.

5. The method of claim 4, wherein the setting the assessment model comprises presetting a fatigue level grading criterion;

Inputting the fusion feature vector into the set evaluation model to obtain a second fatigue degree grade of the waist muscle, wherein the method comprises the following steps:

And inputting the fusion feature vector into the set evaluation model, and determining a second fatigue degree grade of the waist muscle based on the preset fatigue degree grade dividing standard and the fusion feature vector.

6. The method of claim 5, wherein determining a second fatigue level of the lumbar muscle based on the preset fatigue level classification criteria and the fusion feature vector comprises:

comparing the fusion feature vectors according to the preset fatigue degree grade division standard to obtain a comparison result;

and determining a second fatigue degree grade of the waist muscle according to the comparison result.

7. The method of claim 1, wherein determining a target fatigue level for a target user based on the first fatigue level and the second fatigue level comprises:

Acquiring a first weight factor corresponding to the first fatigue degree level and a second weight factor corresponding to the second fatigue degree level;

And determining the target fatigue degree grade of the target user according to the first fatigue degree grade, the first weight factor, the second fatigue degree grade and the second weight factor.

8. A lumbar muscle fatigue detection device, comprising:

9. An electronic device, the electronic device comprising:

at least one processor, and

A memory communicatively coupled to the at least one processor, wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the lumbar muscle fatigue detection method of any of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the lumbar muscle fatigue detection method according to any of claims 1-7 when executed.