Waist muscle fatigue detection method, device, equipment and medium

Abstract

The embodiment of the invention discloses a waist muscle fatigue detection method and device, equipment and a medium. The method comprises the following steps: acquiring electromyographic signals and temperature data of waist muscles of a target user based on waist detection equipment, and acquiring 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 a target user according to the first fatigue degree grade and the second fatigue degree grade; adjusting the working mode of the intelligent mattress according to the target fatigue degree grade; wherein the waist 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 relaxation scheme is provided, and the sleep quality of a target user is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a waist muscle fatigue detection method, device, equipment and medium. Background Art

[0002] With the fast pace of modern life and high work pressure, waist muscle fatigue is becoming more and more serious. Sitting for a long time or incorrect posture can easily lead to waist muscle fatigue, thus affecting people's quality of life and sleep.

[0003] Existing methods for assessing waist fatigue are mostly subjective and lack objective and accurate detection methods. In addition, existing mattress adjustment technology is difficult to accurately adjust according to individual muscle fatigue, thus failing to effectively relieve waist fatigue. Summary of the invention

[0004] The present invention provides a waist muscle fatigue detection method, device, equipment and medium, which can monitor the fatigue level of waist muscles in real time and intelligently adjust the working mode of the mattress to provide a personalized waist muscle relaxation plan and improve the sleep quality of the target user.

[0005] According to one aspect of the present invention, a waist muscle fatigue detection method is provided, comprising:

[0006] Acquire the electromyographic signal and temperature data of the waist muscles of the target user based on the waist detection device, and acquire the first fatigue level of the target user's subjective assessment;

[0007] determining a second fatigue level of the waist muscles according to the electromyographic signal and the temperature data;

[0008] determining a target fatigue level of a target user according to the first fatigue level and the second fatigue level;

[0009] The working mode of the smart mattress is adjusted according to the target fatigue level; wherein the waist detection device is associated with the smart mattress.

[0010] According to another aspect of the present invention, there is provided a waist muscle fatigue detection device, comprising:

[0011] A data acquisition module, used to acquire myoelectric signals and temperature data of waist muscles of a target user based on a waist detection device, and to acquire a first fatigue level of a subjective assessment of the target user;

[0012] A second fatigue level determination module, used for determining a second fatigue level of the waist muscle according to the electromyographic signal and the temperature data;

[0013] a target fatigue level determination module, configured to determine a target fatigue level of a target user according to the first fatigue level and the second fatigue level;

[0014] The smart mattress adjustment module is used to adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the waist muscle fatigue detection method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the waist muscle fatigue detection method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention is to obtain the electromyographic signal and temperature data of the waist muscle of the target user based on the waist detection device, and obtain the first fatigue level of the subjective evaluation of the target user; determine the second fatigue level of the waist muscle according to the electromyographic signal and the temperature data; determine the target fatigue level of the target user according to the first fatigue level and the second fatigue level; adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress. This technical solution can provide a personalized waist muscle relaxation solution and improve the sleep quality of the target user by monitoring the fatigue level status of the waist muscles in real time and intelligently adjusting the working mode of the mattress.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a waist muscle fatigue detection method provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is an exemplary diagram of a waist detection device provided according to Embodiment 1 of the present invention;

[0025] Figure 3 is a flow chart of a waist muscle fatigue detection method provided according to Embodiment 2 of the present invention;

[0026] Figure 4 is a structural schematic diagram of a waist muscle fatigue detection device provided according to Embodiment 3 of the present invention;

[0027] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second" and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a waist muscle fatigue detection method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of waist muscle fatigue detection. The method can be performed by a waist muscle fatigue detection device. The waist muscle fatigue detection device can be implemented in the form of hardware and / or software. The waist muscle fatigue detection device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0032] S110, obtaining the electromyographic signal and temperature data of the waist muscles of the target user based on the waist detection device, and obtaining the first fatigue level of the target user's subjective assessment.

[0033] Among them, the waist detection device can be a device belt for fatigue detection of the user's waist muscles. In this embodiment, the waist detection device can include an electromyographic sensor and a temperature sensor. The electromyographic signal can be the superposition of motor unit action potentials (MUAP) in many muscle fibers in time and space, and is a bioelectric signal generated by muscle contraction. The surface electromyographic signal (sEMG) is an electrical signal accompanying muscle contraction. It is the comprehensive effect of superficial muscle EMG and electrical activity on the nerve trunk on the surface of the skin, and can reflect the activity of nerve muscles to a certain extent. The temperature data can be data obtained by detecting the temperature of the waist muscles. In this embodiment, the electromyographic signal of the waist muscles of the target user can be collected by the electromyographic sensor in the waist detection device, and the temperature data of the waist muscles of the target user can be collected by the temperature sensor in the waist detection device. The target user can refer to the user who is currently using the waist detection device to detect the waist muscles.

[0034] An example diagram of the waist detection device in this embodiment is shown in FIG. Figure 2 As shown, it can also be understood as a waist muscle detection device belt, the gray array can be a flexible electrode array for detecting sEMG signals, and the white frame can be a temperature sensor for collecting temperature data. In this embodiment, the electromyographic signal (EMG) and temperature data of the waist muscles can be collected in real time by the electromyographic sensor and temperature sensor in the waist detection device. In this embodiment, when actually collecting data, basic data collection can be performed: basic EMG and temperature data are collected in a resting state, and the initial MVC is recorded. A task with a long duration and gradually increasing intensity can also be designed, such as standing for a long time, sitting for a long time, bending over or carrying heavy objects for periodic measurements. Periodic measurement: During the task, EMG, temperature and MVC are measured regularly, and the target user is asked to perform subjective fatigue scoring. The arrangement of sensors in this embodiment should be reasonable to ensure the accuracy and stability of signal acquisition.

[0035] The first fatigue level may be a fatigue level obtained by the target user through personal subjective evaluation. In this embodiment, the first fatigue level of the target user is obtained by subjectively evaluating the target user according to a subjective evaluation method.

[0036] In the present embodiment, the subjective evaluation method can be recorded by the Borg fatigue scale (RPE scale) and the questionnaire. The Borg fatigue scale allows the subject to score the fatigue level according to his or her own feeling, usually using a 0 to 10 scoring system. A higher score indicates a higher fatigue level. The questionnaire allows the subject to describe their fatigue feelings, muscle soreness, fatigue level, etc. Among them, the Borg fatigue scale (Borg Scale) is a tool for evaluating subjective fatigue perception in sports, also known as the subjective fatigue perception evaluation scale (The Borg Rating of Perceived Exertion, RPE). In the present embodiment, the first fatigue level can include 4 types of fatigue levels, specifically 0-2 corresponding fatigue levels are no fatigue, 3-5 corresponding fatigue levels are slight fatigue, 6-8 corresponding fatigue levels are moderate fatigue, and 9-10 corresponding fatigue levels are severe fatigue. The target user can score a subjective score in the 0-10 score according to his or her true feelings to determine his or her first fatigue level accordingly.

[0037] S120. Determine a second fatigue level of the waist muscles according to the electromyographic signal and the temperature data.

[0038] The second fatigue level can be an objective fatigue level obtained by evaluating the electromyographic signal and temperature data. In this embodiment, the maximum voluntary contraction force MVC in the electromyographic signal, the muscle signal characteristic change and the temperature characteristic change can be used for objective evaluation to obtain the corresponding second fatigue level.

[0039] In this embodiment, MVC measures the force generated by the waist muscles under maximum effort. Fatigue will cause the MVC to decrease; and the root mean square value (RMS) of the EMG signal may decrease, and the mean frequency (Mean Frequency) or median frequency (Median Frequency) may decrease when fatigued; and after long-term exercise or fatigue, the temperature of the waist muscles may rise. Therefore, the waist muscles can be objectively evaluated based on the electromyographic signals and temperature data to obtain a second fatigue level.

[0040] S130: Determine a target fatigue level of the target user according to the first fatigue level and the second fatigue level.

[0041] The target fatigue level may be the final fatigue level determined by the target user. In this embodiment, different weights may be set for the first fatigue level determined by subjective evaluation and the second fatigue level determined by objective evaluation to determine the target fatigue level of the waist muscles of the target user.

[0042] In this embodiment, optionally, determining the target fatigue level of the target user based on 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; determining the target fatigue level of the target user based on the first fatigue level, the first weight factor, the second fatigue level and the second weight factor.

[0043] Among them, the first weight factor may be a weight factor corresponding to the first fatigue level. The second weight factor may be a weight factor corresponding to the second fatigue level. In this embodiment, the first weight factor and the second weight factor may be pre-set and may be set according to actual needs. For example, the first weight factor may be set to 0.6 and the second weight factor may be set to 0.4.

[0044] In this embodiment, a preset first weight factor and a second weight factor may be obtained, and then a weighted sum process is performed according to the first fatigue level and the first weight factor, and the second fatigue level and the second weight factor to obtain the target fatigue level of the target user.

[0045] Exemplarily, in this embodiment, both the first fatigue level and the second fatigue level are divided into four fatigue levels: no fatigue, slight fatigue, moderate fatigue and severe fatigue. The result obtained by weighted summation can be respectively corresponded to scores of 1-4 according to these four fatigue levels. The higher the fatigue level, the higher the score. The first weight factor can be set to 0.6, and the second weight factor can be set to 0.4. In this embodiment, if the 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, it can be divided into the fatigue level corresponding to 4 points, that is, severe fatigue; if the 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 (that is, less than 3.5 points), it can be divided into the fatigue level corresponding to 3 points, that is, moderate fatigue.

[0046] In this embodiment, by such a setting, different weight factors can be set for different fatigue levels obtained by subjective evaluation and objective evaluation, and then the final target fatigue level can be determined by weighted summation, thereby improving the reliability of fatigue level classification. Compared with the traditional method limited to subjective evaluation, this embodiment provides a more objective and reliable fatigue detection method.

[0047] S140: adjusting the working mode of the smart mattress according to the target fatigue level.

[0048] Among them, the waist detection device is associated with the smart mattress. The working mode of the smart mattress may refer to the hardness of the mattress and the vibration mode of the mattress. Exemplarily, in this embodiment, the smart mattress may include one or more airbag modules that can adjust the hardness by inflating and deflating air and a vibration module that can drive the mattress to vibrate, so as to adjust the hardness and vibration mode of the smart mattress. In this embodiment, the waist detection device can be associated with the smart mattress, so as to determine the target fatigue degree of the waist muscles based on real-time monitoring of the waist muscle state, and intelligently adjust the vibration mode and hardness of the mattress according to the target fatigue degree, so as to provide a personalized waist muscle relaxation plan and improve the sleep quality of the target user.

[0049] Specifically, in this embodiment, the smart mattress system can automatically select the appropriate vibration or wave adjustment mode according to the assessed target fatigue level to provide a personalized waist muscle relaxation solution. In this embodiment, different adjustment modes can be set for different fatigue levels to ensure the maximum relaxation effect. Moreover, in this embodiment, the smart mattress system can not only adjust the vibration mode, but also automatically adjust the softness and hardness of the mattress according to the fatigue of the waist muscles. Such adjustment can provide better support and comfort, fit the waist curve, effectively relieve waist pressure, and improve the user's overall sleeping experience.

[0050] The technical solution of the embodiment of the present invention is to obtain the electromyographic signal and temperature data of the waist muscle of the target user based on the waist detection device, and obtain the first fatigue level of the subjective evaluation of the target user; determine the second fatigue level of the waist muscle according to the electromyographic signal and the temperature data; determine the target fatigue level of the target user according to the first fatigue level and the second fatigue level; adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress. This technical solution can provide a personalized waist muscle relaxation solution and improve the sleep quality of the target user by monitoring the fatigue level state of the waist muscle in real time and intelligently adjusting the working mode of the mattress.

[0051] Embodiment 2

[0052] Figure 3This is a flow chart of a method for detecting lumbar muscle fatigue provided in accordance with the second embodiment of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is as follows: determining the second fatigue level of the lumbar muscles according to the electromyographic signal and the temperature data, including: performing preprocessing operations on the electromyographic signal and the temperature data to obtain preprocessed electromyographic signals and preprocessed temperature data; performing feature extraction on the preprocessed electromyographic signal and the preprocessed temperature data to obtain corresponding electromyographic features and temperature features; determining the second fatigue level of the lumbar muscles according to the electromyographic features, the temperature features and the set evaluation model. Figure 3 As shown, the method includes:

[0053] S310: Acquire the electromyographic signal and temperature data of the waist muscles of the target user based on the waist detection device, and acquire the first fatigue level of the target user's subjective assessment.

[0054] S320, preprocessing the electromyographic signal and the temperature data to obtain preprocessed electromyographic signal and preprocessed temperature data.

[0055] The preprocessing operation may include denoising, rectification and smoothing or normalization. The preprocessed electromyographic signal may be electromechanical signal data obtained after denoising, rectification and smoothing. The preprocessed temperature data may be temperature data obtained after denoising and normalization.

[0056] In this embodiment, the collected electromyographic signal can be subjected to preprocessing operations such as denoising, rectification and smoothing to obtain the preprocessed electromyographic signal; the temperature data can be subjected to preprocessing operations such as denoising and normalization to obtain the preprocessed temperature data. In this embodiment, the electromyographic signal and the temperature data are preprocessed separately, so that the preprocessed data are easier to extract and analyze the subsequent features.

[0057] S330, performing feature extraction on the preprocessed electromyographic signal and the preprocessed temperature data respectively to obtain corresponding electromyographic features and temperature features.

[0058] Among them, the electromyographic features may include time domain features and frequency domain features; the temperature features may include temperature mean and temperature variation features. In this embodiment, feature extraction may be performed from 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 mean and temperature variation features.

[0059] In this embodiment, optionally, the electromyographic features include time domain features and frequency domain features; the temperature features include temperature mean and temperature variation features; accordingly, feature extraction is performed on the preprocessed electromyographic signal and the preprocessed temperature data, respectively, to obtain corresponding electromyographic features and temperature features, including: feature extraction on the preprocessed electromyographic signal to obtain time domain features and frequency domain features; feature extraction on the preprocessed temperature data to obtain temperature mean and temperature variation features.

[0060] Among them, the time domain feature may refer to a feature extracted directly from the time series signal of the electromyographic signal. In this embodiment, the time domain feature may include feature data such as root mean square value (RMS), mean absolute value (MAV), zero crossing rate (ZCR) and waveform length (WL). The frequency domain feature may include feature data such as power spectral density (PSD), median frequency (MF) and mean frequency (MeanFrequency). The temperature mean may be the average temperature data obtained by extracting the temperature data. The temperature change feature amount may be the temperature change rate feature obtained by extracting the temperature data.

[0061] In this embodiment, the preprocessed electromyographic signal can be feature extracted to obtain time domain features and frequency domain features, which are root mean square value (RMS), mean absolute value (MAV), zero crossing rate (ZCR), waveform length (WL), power spectral density (PSD), median frequency (MF) and mean frequency (Mean Frequency) and other feature data; in this embodiment, the preprocessed temperature data can be feature extracted to obtain average temperature and temperature change rate feature data.

[0062] In this embodiment, through such a setting, feature extraction can be performed on the preprocessed electromyographic signal and the preprocessed temperature data to obtain the required feature data, so as to facilitate subsequent processing based on the feature data.

[0063] In addition, in this embodiment, each index data in the preprocessed electromyographic signal and the preprocessed temperature data can be uniformly normalized according to the time point. For example, the detection is performed according to the hour point, and the corresponding characteristic values ​​are obtained at one hour; or the corresponding characteristic values ​​are obtained at two hours. It is possible to unify a data value by time point in chronological order; for example, there is one characteristic data value at two points and one characteristic data value at three points, forming a matrix vector, and uniformly normalizing each data value.

[0064] S340, determining a second fatigue level of the waist muscles according to the electromyographic characteristics, the temperature characteristics and the set evaluation model.

[0065] The set evaluation model can be a trained neural network model for evaluating the fatigue level of the waist muscles. In this embodiment, the extracted electromyographic features and temperature features can be fused to form a fused feature vector, and the second fatigue level of the waist muscles can be obtained based on the fused feature vector and the trained evaluation model.

[0066] Furthermore, in this embodiment, the extracted electromyographic and temperature features can be fused to form a comprehensive feature vector. Based on these comprehensive feature vectors, a machine learning model (such as a neural network) is used to train according to a preset fatigue level classification standard to obtain a set evaluation model to evaluate the fatigue level of the waist muscles. The fatigue level can be divided into four levels: no fatigue, slight fatigue, moderate fatigue, and severe fatigue.

[0067] In this embodiment, optionally, the second fatigue level of the waist muscles is determined based on the electromyographic characteristics, temperature characteristics and a set evaluation model, including: fusing the electromyographic characteristics and temperature characteristics to obtain a fused feature vector; inputting the fused feature vector into the set evaluation model to obtain the second fatigue level of the waist muscles.

[0068] Among them, the fusion processing can be understood as the processing of feature splicing operation. The fused feature vector can be a feature vector obtained by performing feature splicing operation on the electromyographic feature and the temperature feature to fuse them together. In this embodiment, the electromyographic feature and the temperature feature can be subjected to feature splicing operation to obtain a fused feature vector, and then the fused feature vector is input into the trained setting evaluation model, so that the setting evaluation model judges each feature data in the fused feature vector according to the preset fatigue level classification standard, and determines the second fatigue level of the waist muscle according to the judgment result.

[0069] Through such a setting in this embodiment, the second fatigue level corresponding to the waist muscles can be determined by fusing the feature data and setting the evaluation model, so as to obtain the fatigue level under the objective state, thereby improving the objectivity and reliability of the fatigue level.

[0070] In this embodiment, optionally, setting the evaluation model includes preset fatigue level classification standards; inputting the fused feature vector into the set evaluation model to obtain the second fatigue level of the waist muscles, including: inputting the fused feature vector into the set evaluation model, and determining the second fatigue level of the waist muscles based on the preset fatigue level classification standards and the fused feature vector.

[0071] The preset fatigue level classification standard may be a pre-set classification standard for each fatigue level based on feature data. In this embodiment, different classification standards may be set based on different feature data in the fused feature vector as a reference, and may be set according to actual needs, which is not limited in this embodiment. In this embodiment, data may be marked as different fatigue levels according to the preset fatigue classification standard.

[0072] Exemplarily, in this embodiment, the MVC, electromyographic EMG features and temperature change features contained in the fusion feature vector are used as examples to pre-set fatigue level classification standards. Specifically, the standard corresponding to no fatigue can be: the MVC decrease is less than 10%, the RMS (root mean square value) in the electromyographic feature EMG has no significant change, and the temperature change is less than 1°C. The standard corresponding to mild fatigue can be: the MVC decrease range is between 10%-20%, the RMS in the electromyographic feature EMG decreases slightly, and the temperature change is between the data range of 1°C-2°C. The standard corresponding to moderate fatigue can be: the MVC decrease range is between 20%-40%, the RMS in the electromyographic feature EMG decreases significantly, and the temperature change is between the data range of 2°C-3°C. The standard corresponding to severe fatigue can be: the MVC decrease is greater than 40%, the RMS in the electromyographic feature EMG decreases significantly, the frequency component changes significantly, and the temperature change is greater than 3°C in the data range. In this embodiment, the data range corresponding to different feature data can also be set according to actual needs as a preset fatigue level classification standard.

[0073] In this embodiment, the fused feature vector can be input into a pre-trained set evaluation model, so that the fused feature vector can be compared based on the preset fatigue level classification standard in the set evaluation model. According to the comparison result, the corresponding fatigue level can be obtained, that is, the second fatigue level of the waist muscles.

[0074] Through such a setting in this embodiment, by combining the lumbar electromyography signal (EMG) and temperature data and utilizing advanced machine learning algorithms, the fatigue level of the user's lumbar muscles can be evaluated in real time and accurately to obtain a more accurate fatigue level classification.

[0075] In this embodiment, optionally, a second fatigue level of the waist muscles is determined based on a preset fatigue level classification standard and a fused feature vector, including: comparing the fused feature vector according to the preset fatigue level classification standard to obtain a comparison result; and determining the second fatigue level of the waist muscles based on the comparison result.

[0076] The comparison result may be a result obtained by comparing each feature data in the fused feature vector with a pre-set classification standard corresponding to each fatigue level of the feature data. In this embodiment, each feature data in the fused feature vector may be compared with the data range or size corresponding to each feature data in the preset fatigue level classification standard to obtain each classification standard satisfied by the feature data, and the corresponding fatigue level is determined according to the classification standard, thereby obtaining the second fatigue level of the waist muscle.

[0077] In this embodiment, through such a setting, the fatigue level of the objective evaluation method can be obtained, so that the fatigue level of the objective evaluation method and the fatigue level of the subjective evaluation method can be combined to determine the final fatigue level.

[0078] S350: Determine a target fatigue level of the target user according to the first fatigue level and the second fatigue level.

[0079] S360, adjusting the working mode of the smart mattress according to the target fatigue level.

[0080] Among them, the waist detection device is associated with the smart mattress.

[0081] In this embodiment, the waist detection device can be associated with the smart mattress, so that the smart mattress can be adjusted according to the target fatigue level obtained by the objective evaluation and the subjective evaluation, so that the smart mattress automatically selects the appropriate vibration or wave adjustment mode to relax the waist muscles. In addition, the smart mattress system can adjust the hardness of the mattress according to the fatigue level, fit the waist muscle condition, and provide the best support and comfort.

[0082] In this embodiment, real-time feedback and optimization can also be performed. The specific system can provide real-time feedback to the user on the fatigue state of the waist muscles and the adjustment of the mattress through a mobile application or other user interface. The vibration mode and the hardness adjustment strategy are continuously optimized based on user feedback and usage.

[0083] The technical solution of the embodiment of the present invention is to obtain the electromyographic signal and temperature data of the waist muscle of the target user based on the waist detection device, and obtain the first fatigue level of the target user's subjective evaluation; preprocess the electromyographic signal and temperature data to obtain the preprocessed electromyographic signal and preprocessed temperature data; extract features of the preprocessed electromyographic signal and preprocessed temperature data respectively to obtain corresponding electromyographic features and temperature features; determine the second fatigue level of the waist muscle according to the electromyographic features, temperature features and set evaluation model; determine the target fatigue level of the target user according to the first fatigue level and the second fatigue level; adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress. This technical solution can monitor the fatigue level status of the waist muscles in real time and intelligently adjust the working mode of the mattress to provide a personalized waist muscle relaxation plan and improve the sleep quality of the target user.

[0084] Embodiment 3

[0085] Figure 4 Schematic diagram of the structure of a waist muscle fatigue detection device provided according to Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0086] The data acquisition module 410 is used to acquire the electromyographic signal and temperature data of the waist muscles of the target user based on the waist detection device, and acquire the first fatigue level of the target user's subjective assessment;

[0087] A second fatigue level determination module 420, for determining a second fatigue level of the waist muscles according to the electromyographic signal and the temperature data;

[0088] A target fatigue level determination module 430, configured to determine a target fatigue level of a target user according to the first fatigue level and the second fatigue level;

[0089] The smart mattress adjustment module 440 is used to adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress.

[0090] Optionally, the second fatigue level determination module 420 includes:

[0091] A data preprocessing unit, used for performing preprocessing operations on the electromyographic signal and the temperature data to obtain preprocessed electromyographic signal and preprocessed temperature data;

[0092] A feature extraction unit is used to extract features from the preprocessed electromyographic signal and the preprocessed temperature data to obtain corresponding electromyographic features and temperature features;

[0093] The fatigue level evaluation unit is used to determine the second fatigue level of the waist muscles according to the myoelectric characteristics, temperature characteristics and a set evaluation model.

[0094] Optionally, the electromyographic features include time domain features and frequency domain features; the temperature features include temperature mean and temperature variation features;

[0095] The feature extraction unit is specifically used to extract features from the preprocessed electromyographic signal to obtain time domain features and frequency domain features; and to extract features from the preprocessed temperature data to obtain temperature mean and temperature variation features.

[0096] Optional, fatigue level assessment unit, including:

[0097] The feature fusion subunit is used to fuse the electromyographic feature and the temperature feature to obtain a fused feature vector;

[0098] The model evaluation subunit is used to input the fused feature vector into the set evaluation model to obtain the second fatigue level of the waist muscles.

[0099] Optionally, setting the assessment model includes presetting fatigue level classification standards;

[0100] The model evaluation subunit is specifically used to input the fused feature vector into the set evaluation model, and determine the second fatigue level of the waist muscles based on the preset fatigue level classification standard and the fused feature vector.

[0101] Optionally, the model evaluation subunit is specifically used to compare the fused feature vector according to a preset fatigue level classification standard to obtain a comparison result; and determine the second fatigue level of the waist muscles according to the comparison result.

[0102] Optionally, the target fatigue level determination module 430 is specifically used 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 based on the first fatigue level, the first weight factor, the second fatigue level and the second weight factor.

[0103] A waist muscle fatigue detection device provided by an embodiment of the present invention can execute a waist muscle fatigue detection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0104] Embodiment 4

[0105] Figure 51 is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0106] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can 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 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a waist muscle fatigue detection method.

[0109] In some embodiments, the waist muscle fatigue detection method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on 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 waist muscle fatigue detection method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the waist muscle fatigue detection method in any other appropriate manner (for example, by means of firmware).

[0110] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0112] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

[0114] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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.

[0115] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0116] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0117] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting waist muscle fatigue, characterized in that: include: Acquire the electromyographic signal and temperature data of the waist muscles of the target user based on the waist detection device, and acquire the first fatigue level of the target user's subjective assessment; determining a second fatigue level of the waist muscles according to the electromyographic signal and the temperature data; determining a target fatigue level of a target user according to the first fatigue level and the second fatigue level; The working mode of the smart mattress is adjusted according to the target fatigue level; wherein the waist detection device is associated with the smart mattress.

2. The method according to claim 1, characterized in that Determining a second fatigue level of the waist muscles according to the electromyographic signal and the temperature data includes: Performing a preprocessing operation on the electromyographic signal and the temperature data to obtain a preprocessed electromyographic signal and preprocessed temperature data; Performing feature extraction on the preprocessed electromyographic signal and the preprocessed temperature data respectively to obtain corresponding electromyographic features and temperature features; A second fatigue level of the waist muscles is determined according to the myoelectric characteristics, the temperature characteristics and a set evaluation model.

3. The method according to claim 2, characterized in that The electromyographic features include time domain features and frequency domain features; The temperature characteristics include temperature mean and temperature variation characteristics; Correspondingly, feature extraction is performed on the preprocessed electromyographic signal and the preprocessed temperature data to obtain corresponding electromyographic features and temperature features, including: Extracting features from the preprocessed electromyographic signal to obtain time domain features and frequency domain features; The preprocessed temperature data is subjected to feature extraction to obtain the temperature mean and temperature variation features.

4. The method according to claim 2, characterized in that: Determining the second fatigue level of the waist muscles according to the myoelectric characteristics, the temperature characteristics and the set evaluation model includes: Fusing the electromyographic feature and the temperature feature to obtain a fused feature vector; The fused feature vector is input into the set evaluation model to obtain a second fatigue level of the waist muscles.

5. The method according to claim 4, characterized in that The set evaluation model includes preset fatigue level classification standards; Inputting the fused feature vector into the set evaluation model to obtain a second fatigue level of the waist muscles includes: The fused feature vector is input into the set evaluation model, and a second fatigue level of the waist muscles is determined based on the preset fatigue level classification standard and the fused feature vector.

6. The method according to claim 5, characterized in that Determining a second fatigue level of the waist muscles based on the preset fatigue level classification standard and the fused feature vector includes: Comparing the fused feature vectors according to the preset fatigue level classification standard to obtain a comparison result; A second fatigue level of the waist muscles is determined according to the comparison result.

7. The method according to claim 1, characterized in that Determining a target fatigue level of a 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; A target fatigue level of the target user is determined according to the first fatigue level, the first weight factor, the second fatigue level and the second weight factor.

8. A waist muscle fatigue detection device, characterized in that: include: A data acquisition module, used to acquire myoelectric signals and temperature data of waist muscles of a target user based on a waist detection device, and to acquire a first fatigue level of a subjective assessment of the target user; A second fatigue level determination module, used for determining a second fatigue level of the waist muscle according to the electromyographic signal and the temperature data; a target fatigue level determination module, configured to determine a target fatigue level of a target user according to the first fatigue level and the second fatigue level; The smart mattress adjustment module is used to adjust the working mode of the smart mattress according to the target fatigue level; wherein the waist detection device is associated with the smart mattress.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the waist muscle fatigue detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the waist muscle fatigue detection method according to any one of claims 1 to 7 when executed.

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