Intelligent foot health monitoring system based on micro-spectral technology and method thereof

Abstract

The application discloses an intelligent foot health monitoring system and method based on micro-spectrum technology, which collects humidity, microorganism and VOCs spectrum characteristic data by using micro-spectrum, selects effective wavelengths to train a linear regression model, predicts humidity, microorganism and VOCs content by using the linear regression model, and automatically judges a foot health state according to a prediction result, and sends early warning information to a user and provides corresponding nursing suggestions when humidity is too high, microorganisms breed and VOCs concentration exceeds a standard. The application realizes comprehensive and real-time monitoring of the foot health state, has the advantages of non-invasive, high precision, real-time feedback and the like, can effectively prevent the occurrence of foot diseases, improves the health awareness and life quality of the user, and has a wide market application prospect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent foot health monitoring, and in particular to an intelligent foot health monitoring system based on micro-spectral technology and a method thereof. BACKGROUND

[0002] With the improvement of living standards, people's demand for personal health management is growing. As an important load and movement part of the human body, the health status of the feet directly affects the overall health and quality of life of individuals. However, due to various factors such as environment and living habits, the feet often face problems such as excessive humidity, microbial breeding and odor, which, if not promptly addressed, can cause skin diseases such as athlete's foot and fungal infections. Currently, the only way to determine whether there are skin diseases such as athlete's foot and fungal infections is through medical diagnosis, so it is particularly important to develop a system that can monitor the health status of the feet in real time. The present application collects spectral feature data of humidity, microorganisms and VOCs by using a miniature spectrometer, trains a machine learning model using training data labeled with humidity, microorganism and VOC content, and determines the health status of the feet by measuring the humidity, microorganism and VOC content of the feet. The present application provides an intelligent foot health monitoring system based on micro-spectral technology and a method thereof. SUMMARY

[0003] The present application aims to provide an intelligent foot health monitoring system based on micro-spectral technology and a method thereof, which can monitor the humidity, microorganisms and VOC concentration inside the shoes in real time and accurately, and automatically determine the health status of the feet according to the preset standard, and send warning information to the user.

[0004] The present application discloses an intelligent foot health monitoring system based on micro-spectral technology, comprising a miniature spectral sensor module, a data processing and analysis unit, a wireless communication module and a power module, wherein:

[0005] The miniature spectral sensor module is integrated inside the shoes, and the miniature spectral coverage wavelength is 255nm-1650nm, which is used to collect the original spectral feature data of humidity, microorganisms and VOCs.

[0006] The data processing and analysis unit receives data from the sensor, performs real-time processing and analysis, and determines the health status of the feet.

[0007] The wireless communication module transmits data between the intelligent device (such as APP, applet, etc.) and the monitoring results and health suggestions are sent to the user.

[0008] The power module provides stable power supply for the entire system, and uses a rechargeable battery or a miniature energy collection device.

[0009] The application further discloses a method for intelligent foot health monitoring based on micro-spectrum technology.

[0010] Step 1: Since the micro-spectrometer is composed of various electronic components, the differences between the electronic components and the batch differences will cause differences between the final devices. Therefore, a solution is designed to solve the differences between the devices, and the specific processing process is as follows:

[0011] Device calibration: Before each micro-spectrometer is shipped, the original data of one calibration sample is collected, and the spectral intensity value is adjusted according to the original spectral value of the calibration sample, so that the spectral value error of all devices collecting the calibration sample is less than 1%.

[0012] Calibration sample: standard air (oxygen (O2) accounts for about 21% of the total volume, nitrogen (N2) accounts for about 79% of the total volume, total hydrocarbon content needs to be less than 2ppm (parts per million), and moisture content needs to be less than 5ppm.

[0013] Step 2: In order to quickly and accurately predict foot health, the original spectral feature data of humidity, microorganisms and VOCs is collected by the micro-spectrometer calibrated in step 1, and the specific collection method is as follows:

[0014] ① Place the micro-spectrometer in standard air and collect a spectral data as a blank.

[0015] ② Wear shoes for 8 hours on the first day, collect original spectral data at 0, 4 and 8 hours during exercise, then put them into a plastic bag with holes and culture for 12 hours, then collect original spectral data, wear shoes for 8 hours on the second day, collect spectral data at 0, 6 and 12 hours during exercise, then put them into a plastic bag with holes and culture for 24 hours, then collect original spectral data.

[0016] ③ Quantitative calibration of each label is performed on the original spectral feature data as training data.

[0017] The principle of detecting humidity, microorganisms and VOCs content to judge the foot health state by spectral method: different substances have corresponding absorption wavelengths, and the training data with humidity, microorganism and VOCs content labels are used to train the machine learning model, and the model is used to predict the humidity, microorganism and VOCs content to judge the foot health state.

[0018] The humidity, microorganism and VOCs original spectral feature data cover 255nm-1650nm wavelength, and the corresponding absorption wavelength of the corresponding index is selected for training.

[0019] The training wavelength humidity selects 1300nm wavelength, microorganism selects 365nm wavelength, and VOCs selects 1650nm wavelength.

[0020] Step 3: Train the linear regression model with the training data from Step 2 to generate a model that can predict the humidity, microorganisms, and VOCs content using the linear regression model. Then, based on the prediction results, determine the foot health status. The specific description is as follows:

[0021] The linear regression model selected in this application is the LASSO model, which uses regularization and ridge regression to estimate the L1 and L2 parameters in the model and the coefficient R 2 Evaluate the accuracy of the model.

[0022] Through the linear regression model, when the humidity exceeds 70%, the system issues a warning, prompting the user that the humidity is too high and there is a risk of rapid growth of microorganisms.

[0023] Through the linear regression model, when the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units as medium risk, and more than 1000 units as high risk, and the corresponding level of health warning is issued.

[0024] Through the linear regression model, when the VOCs concentration is less than 0.5mg / m 3 , it is judged as low risk, 0.5mg / m 3 -1mg / m 3 as medium risk, and more than 1mg / m 3 as high risk, and the system issues a warning and suggestion to the user accordingly.

[0025] The system sends health warning information to the user's mobile phone APP through the wireless communication module based on the monitoring results, including high humidity, microorganism breeding, and VOCs concentration exceeding the standard, etc., and provides corresponding care suggestions, such as replacing insoles, increasing ventilation, using antibacterial spray, etc.

[0026] The application has the following advantages:

[0027] 1. The miniature spectral technology is used to realize comprehensive and real-time monitoring of the foot health status, with the advantages of non-invasive, high precision, real-time feedback, etc.

[0028] 2. The system can effectively prevent the occurrence of foot diseases and improve the user's health awareness and quality of life, with broad market application prospects. BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0030] Figure 1 An intelligent foot health monitoring system based on miniature spectral technology and its method flowchart DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described and discussed clearly and completely in combination with the drawings of the present application. Apparently, only some examples of the present application are described here, and not all examples. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0032] In order to monitor the humidity, microorganism and VOCs concentration in the shoe in real time and accurately, and automatically judge the foot health condition according to the preset standard, and send the warning information to the user. In combination with Figure 1 , Table 1, Table 2, Table 3, the present application is described in detail, and the specific implementation steps are as follows:

[0033] Example 1:

[0034] The humidity, microorganism and VOCs content are determined.

[0035] The present application further discloses a method for intelligent foot health monitoring based on micro-spectral technology, which utilizes the above device for detection, including the following steps:

[0036] Step 1: Since the micro-spectrometer is composed of various electronic components, the differences between the electronic components and the batch differences will cause differences between the final devices. Therefore, the following solutions are designed to solve the differences between the devices, and the specific processing process is as follows:

[0037] Device calibration: Before each micro-spectrometer is shipped, the original data of one calibration substance is collected. Table 1 lists the original data of 365nm, 1300nm and 1650nm in 255nm-1650nm, and Table 2 lists the standard substance spectral data collected after adjusting the light intensity. According to the original spectral value of the calibration substance, the spectral light intensity value is adjusted, so that the spectral value error of all devices collecting the calibration substance is less than 1%.

[0038] Calibration substance: standard air (oxygen (O2) accounts for about 21% of the total volume, nitrogen (N2) accounts for about 79% of the total volume, total hydrocarbon content needs to be less than 2ppm (parts per million), and water content needs to be less than 5ppm. ) Table 1 micro-spectral collection standard substance data

[0039] Wavelength 300 nm 1300 nm 1650 nm Device 1 spectral values 685410 2338064 1987179 Device 2 spectral values 701023 2457349 2113253

[0040] Table 2 Adjustment of light intensity after collecting standard substance spectral data

[0041] Wavelength 300 nm 1300 nm 1650 nm Device 1 spectral values 685410 2338064 1987179 Device 2 spectral values 689125 2329227 1971084

[0042] Step 2: In order to quickly and accurately predict the health of the feet, the original spectral feature data of humidity, microorganisms and VOCs is collected by the microspectrometer calibrated in step 1, and the specific collection method is as follows:

[0043] ① Place the microspectrometer in standard air, and collect a spectrum data as a blank.

[0044] ② Wear shoes for 8 hours on the first day, during which the feet sweat due to exercise and the original spectral data at 0, 4 and 8 hours are collected, then put them into a plastic bag with holes and culture for 12 hours, then collect the original spectral data, wear shoes for 8 hours on the second day, during which the feet sweat due to exercise and the spectral data at 0, 6 and 12 hours are collected, then put them into a plastic bag with holes and culture for 24 hours, then collect the original spectral data.

[0045] ③ Quantitative calibration of each label is carried out on the original spectral feature data as training data.

[0046] The principle of detecting humidity, microorganism and VOCs content to judge the health status of the feet by spectral method: different substances have corresponding absorption wavelengths, the machine learning model is trained by using the training data with humidity, microorganism and VOCs content labels, and the model is used to predict the humidity, microorganism and VOCs content to judge the health status of the feet.

[0047] The humidity, microorganism and VOCs original spectral feature data covers 255nm-1650nm wavelength, and the corresponding absorption wavelength of the corresponding index is selected for training.

[0048] The training wavelength humidity selects 1300nm wavelength, microorganism selects 365nm wavelength, and VOCs selects 1650nm wavelength.

[0049] Step 3: For the training data of step 2, a linear regression model is trained by using the training data, a model is generated, the linear regression model is used to predict the humidity, microorganism and VOCs content, and then the health status of the feet is judged according to the prediction result, and the model prediction effect is shown in table 3, and the specific description is as follows:

[0050] The linear regression model selected in the application is LASSO model, and the L1 and L2 parameters in the model are estimated and the determination coefficient R is determined by using regularization and ridge regression 2 Evaluate the accuracy of the model.

[0051] When the humidity exceeds 70% through the linear regression model, the system issues a warning, prompting the user that the humidity is too high and there is a risk of rapid growth of microorganisms.

[0052] When the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units is medium risk, and more than 1000 units is judged as high risk, and the corresponding level of health warning is issued.

[0053] By linear regression model, when VOCs concentration is less than 0.5mg / m 3 , it is judged as low risk, 0.5mg / m 3 -1mg / m 3 , it is judged as medium risk, and greater than 1mg / m 3 , it is judged as high risk. The system issues a warning and suggestion to the user accordingly. According to the monitoring results, the system sends health warning information to the user's mobile phone APP through the wireless communication module, including specific conditions such as high humidity, microbial breeding and VOCs concentration exceeding the standard, and provides corresponding nursing suggestions, such as replacing insoles, increasing ventilation, using antibacterial spray, etc.

[0054] Table 3 Model prediction results table

[0055]

[0056] From Table 3, it can be concluded that the humidity model can accurately predict humidity, the microbial model can accurately predict microorganisms, and the VOCs model can accurately predict VOCs.

[0057] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses or adaptive changes of this disclosure that follow, in general, the principles of the disclosure and include known equivalents or technical possibilities within the scope of the disclosure. The specification and examples are only considered as exemplary, and the true scope and spirit of the disclosure are indicated by the appended claims.

[0058] It should be understood that the present disclosure is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure should be limited by the appended claims.

Claims

1. An intelligent foot health monitoring system based on micro-spectroscopy technology and a method thereof, characterized by: The intelligent foot health monitoring system based on micro-spectral technology comprises a micro-spectral sensor module, a data processing and analysis unit, a wireless communication module and a power module, wherein: The micro-spectral sensor module is integrated in the shoe, and the micro-spectral coverage wavelength is 255nm-1650nm, which is used to collect the original spectral feature data of humidity, microorganisms and VOCs; The data processing and analysis unit receives data from the micro-spectral sensor module, performs real-time processing and analysis, and judges the foot health status; The wireless communication module transmits data between the intelligent device, and sends the monitoring results and health suggestions to the user; The power module provides stable power supply for the whole system, and uses a rechargeable battery or a micro energy collection device; The method for intelligent foot health monitoring based on micro-spectral technology uses the system for detection, comprising the following steps: Step 1: Device calibration scheme: Before each micro-spectrometer is shipped, the original data of the standard air containing about 21% oxygen, about 79% nitrogen, total hydrocarbon content <2ppm and water content <5ppm are collected, and the spectral light intensity value is adjusted according to the original spectral value of the calibration substance, so that the spectral value error of all devices collecting the calibration substance is <1%; Step 2: Dynamic sampling method: wear shoes for 8 hours on the first day, collect original spectral data at 0, 4 and 8 hours during exercise, then put them into a plastic bag with holes and culture for 12 hours, then collect original spectral data; wear shoes for 8 hours on the second day, collect spectral data at 0, 6 and 12 hours during exercise, then put them into a plastic bag with holes and culture for 24 hours, then collect original spectral data; the original spectral feature data is quantitatively calibrated with various labels as training data; Step 3: Select the spectral characteristics corresponding to 1300 nm wavelength for humidity, 365 nm wavelength for microorganisms, and 1650 nm wavelength for VOCs for the training data of step 2, train the LASSO model, and estimate the L1 and L2 parameters through regularization and ridge regression to determine the coefficient R 2 Evaluate the accuracy, predict the humidity, microorganisms, and VOCs content to be tested, and judge the foot health status according to the prediction results.

2. The intelligent foot health monitoring system based on micro-spectral technology and its method according to claim 1, the spectral method detects humidity, microorganisms and VOCs content to judge the foot health status: different substances have corresponding absorption wavelengths, the training data with humidity, microorganism and VOCs content labels are used to train the machine learning model, and the model is used to predict the humidity, microorganism and VOCs content to judge the foot health status; The humidity, microorganism and VOCs original spectral feature data cover 255nm-1650nm wavelength, and the corresponding absorption wavelength of the corresponding index is selected for training; The training wavelength humidity selects 1300nm wavelength, microorganism selects 365nm wavelength, and VOCs selects 1650nm wavelength.

3. The intelligent foot health monitoring system based on micro-spectral technology and its method according to claim 1, which is described as follows: When the humidity exceeds 70%, the system issues a warning and prompts the user that the humidity is too high and there is a risk of rapid growth of microorganisms through the linear regression model; When the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units as medium risk, and more than 1000 units as high risk, and the corresponding level of health warning is issued. By linear regression model, VOCs concentration less than 0.5 mg / m 3 is judged as low risk, 0.5 mg / m 3 -1 mg / m 3 is judged as medium risk, and greater than 1 mg / m 3 is judged as high risk, and the system issues a warning and suggestion to the user accordingly; The system sends health warning information to the smart device through the wireless communication module according to the monitoring result, including the conditions of too high humidity, microorganism breeding and VOCs concentration exceeding the standard, and provides corresponding nursing suggestions, such as replacing the insole, increasing ventilation and using antibacterial spray.

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