Intelligent foot health monitoring system and method based on micro spectrum technology

Abstract

According to the intelligent foot health monitoring system based on the micro spectrum technology and the method thereof, micro spectrums are used for collecting humidity, microorganism and VOCs spectrum characteristic data, effective wavelengths are selected for training a linear regression model, the linear regression model is used for predicting the humidity, microorganism and VOCs content to be measured, and the humidity, microorganism and VOCs content to be measured is obtained. Then the system can automatically judge the health state of the feet according to the prediction result, early warning information is sent to a user when the humidity is too high, microorganisms breed and the concentration of VOCs exceeds the standard, and corresponding nursing suggestions are provided. The foot health state monitoring system realizes comprehensive and real-time monitoring of the foot health state, has the advantages of non-intrusive type, high precision, real-time feedback and the like, can effectively prevent foot diseases, improves the health awareness and life quality of a user, and has a wide market application prospect.

Description

Technical Field

[0001] The present invention relates to the field of intelligent foot health monitoring, and in particular to an intelligent foot health monitoring system and method based on micro-spectroscopy technology. Background Art

[0002] With the improvement of living standards, people's demand for personal health management is growing. As an important load-bearing and moving part of the human body, the health status of the foot directly affects the overall health and quality of life of the individual. However, due to various factors such as the environment and living habits, the feet often face problems such as excessive humidity, microbial growth and odor. If these problems are not handled in time, they may cause skin diseases such as athlete's foot and fungal infection. At present, whether there are skin diseases such as athlete's foot and fungal infection can only be determined through medical diagnosis. Therefore, it is particularly important to develop a system that can monitor the health status of the feet in real time. The present invention collects spectral feature data of humidity, microorganisms, and VOCs through a micro-spectrometer, and uses training data with humidity, microorganisms, and VOCs content labels to train a machine learning model to obtain the humidity, microorganisms, and VOCs content of the feet to be tested to determine the health status of the feet. The present invention provides an intelligent foot health monitoring system and method based on micro-spectral technology. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent foot health monitoring system and method based on micro-spectral technology. The system can monitor the humidity, microorganisms and VOCs concentration inside the shoes in real time and accurately, and automatically judge the health status of the feet according to preset standards, and send early warning information to the user.

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

[0005] The micro-spectral sensor module is integrated inside the shoe. The micro-spectral coverage wavelength is 255nm-1650nm and is used to collect original spectral characteristic 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 foot.

[0007] The wireless communication module transmits data with smart devices (such as APP, mini-programs, etc.) to send monitoring results and health advice to users.

[0008] The power module provides a stable power supply for the entire system, using a rechargeable battery or a micro energy harvesting device.

[0009] The present invention further discloses a method for intelligent foot health monitoring based on micro-spectroscopy technology, which uses the above device for detection, including the following steps:

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

[0011] Equipment calibration: Each micro-spectrometer collects the original data of a calibration object before leaving the factory, and adjusts the spectral intensity value according to the original spectral value of the calibration object, so that the error of the spectral value collected by all devices is less than 1%.

[0012] Calibration material: Standard air (oxygen (O2) accounts for about 21% of the total volume, nitrogen (N2) accounts for about 79% of the total volume, the total hydrocarbon content needs to be less than 2ppm (parts per million), and the 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 need to be collected through the micro-spectrometer calibrated in step 1. The specific collection method is as follows:

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

[0015] ② On the first day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the original spectral data of 0, 4, and 8 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 12 hours and the original spectral data was collected. On the second day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the spectral data of 0, 6, and 12 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 24 hours and the original spectral data was collected.

[0016] ③ Quantitatively calibrate the labels of the original spectral feature data as the data to be trained.

[0017] Spectroscopic method is used to detect humidity, microorganisms, and VOCs content to determine the health status of the feet. Principle: Different substances have corresponding absorption wavelengths. The machine learning model is trained using training data labeled with humidity, microorganisms, and VOCs content. The model is used to predict humidity, microorganisms, and VOCs content to determine the health status of the feet.

[0018] The original spectral characteristic data of humidity, microorganisms, and VOCs cover wavelengths of 255nm-1650nm, and corresponding absorption wavelengths of corresponding indicators are selected for training.

[0019] The training wavelength for humidity is 1300nm, the training wavelength for microorganisms is 365nm, and the training wavelength for VOCs is 1650nm.

[0020] Step 3: For the training data in step 2, use the training data to train a linear regression model to generate a model, use the linear regression model to predict the humidity, microorganisms, and VOCs content to be tested, and then judge the health status of the feet based on the prediction results. The specific description is as follows:

[0021] The linear regression model used in the present invention is the LASSO model, and regularization and ridge regression are used to estimate the L1 and L2 parameters in the model and the determination coefficient R 2 Evaluate model accuracy.

[0022] The linear regression model predicts that when the humidity exceeds 70%, the system will issue an early warning to remind the user that the humidity is too high and there is a risk of rapid growth of microorganisms.

[0023] The linear regression model predicts that when the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units is medium risk, and when it is greater than 1000 units, it is judged as high risk, and a health warning of the corresponding level will be issued.

[0024] The linear regression model predicts that when the VOCs concentration is less than 0.5 mg / m 3 It is judged as low risk, 0.5mg / m 3 -1mg / m 3 Medium risk, greater than 1 mg / m 3 It is a high risk, and the system issues warnings and suggestions to users accordingly.

[0025] Based on 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 excessive humidity, microbial growth and excessive VOCs concentration, and provides corresponding care suggestions, such as replacing insoles, increasing ventilation, using antibacterial spray, etc.

[0026] The beneficial effects of the present invention are:

[0027] 1. Through micro-spectral technology, comprehensive and real-time monitoring of the health status of the feet is achieved, which has the advantages of non-invasive, high precision and real-time feedback.

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

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

[0030] Figure 1 An intelligent foot health monitoring system based on micro-spectral technology and its method flow chart DETAILED DESCRIPTION

[0031] The following will clearly and completely describe and discuss the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, what is described here is only a part of the examples of the present invention, not all the examples. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] In order to accurately monitor the humidity, microorganisms and VOCs concentration inside the shoes in real time, and automatically judge the health of the feet according to preset standards, and send warning information to the user. Figure 1 , Table 1, Table 2, Table 3 describe the present invention in detail, and the specific implementation steps are as follows:

[0033] Implementation example 1:

[0034] Realize the determination of humidity, microorganisms and VOCs content.

[0035] The present invention further discloses a method for intelligent foot health monitoring based on micro-spectroscopy technology, which uses 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 electronic components and batch differences will lead to differences between the final devices. Therefore, the following solution is designed to solve the differences between devices. The specific processing process is as follows:

[0037] Equipment calibration: Each micro-spectrometer collects the original data of one calibration object before leaving the factory. Table 1 lists some of the original data of 365nm, 1300nm, and 1650nm in the range of 255nm-1650nm, and Table 2 collects the spectral data of the standard object after adjusting the light intensity. The spectral intensity value is adjusted according to the original spectral value of the calibration object, so that the error of the spectral value collected by all devices of the calibration object is less than 1%.

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

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

[0040] Table 2 Spectral data of standard substances collected after adjusting the light intensity

[0041] wavelength 300nm 1300nm 1650nm Device 1 spectral value 685410 2338064 1987179 Device 2 spectral value 589125 2329227 1971084

[0042] Step 2: In order to quickly and accurately predict foot health, the original spectral feature data of humidity, microorganisms, and VOCs need to be collected through the micro-spectrometer calibrated in step 1. The specific collection method is as follows:

[0043] ① Place the micro-spectrometer in standard air and collect a piece of spectrum data as a blank.

[0044] ② On the first day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the original spectral data of 0, 4, and 8 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 12 hours and the original spectral data was collected. On the second day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the spectral data of 0, 6, and 12 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 24 hours and the original spectral data was collected.

[0045] ③ Quantitatively calibrate the labels of the original spectral feature data as the data to be trained.

[0046] Spectroscopic method is used to detect humidity, microorganisms, and VOCs content to determine the health status of the feet. Principle: Different substances have corresponding absorption wavelengths. The machine learning model is trained using training data labeled with humidity, microorganisms, and VOCs content. The model is used to predict humidity, microorganisms, and VOCs content to determine the health status of the feet.

[0047] The original spectral characteristic data of humidity, microorganisms, and VOCs cover wavelengths of 255nm-1650nm, and corresponding absorption wavelengths of corresponding indicators are selected for training.

[0048] The training wavelength for humidity is 1300nm, the training wavelength for microorganisms is 365nm, and the training wavelength for VOCs is 1650nm.

[0049] Step 3: For the training data in step 2, use the training data to train a linear regression model to generate a model, use the linear regression model to predict the humidity, microorganisms, and VOCs content to be tested, and then judge the health status of the foot based on the prediction results, as shown in Table 3. The model prediction effect is described in detail as follows:

[0050] The linear regression model used in the present invention is the LASSO model, and regularization and ridge regression are used to estimate the L1 and L2 parameters in the model and the determination coefficient R 2 Evaluate model accuracy.

[0051] The linear regression model predicts that when the humidity exceeds 70%, the system will issue an early warning to remind the user that the humidity is too high and there is a risk of rapid growth of microorganisms.

[0052] The linear regression model predicts that when the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units is medium risk, and when it is greater than 1000 units, it is judged as high risk, and a health warning of the corresponding level will be issued.

[0053] The linear regression model predicts that when the VOCs concentration is less than 0.5 mg / m 3 It is judged as low risk, 0.5mg / m 3 -1mg / m 3 Medium risk, greater than 1 mg / m 3 The system sends warnings and suggestions to users based on the monitoring results. Based on 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 excessive humidity, microbial growth, and excessive VOCs concentration, and provides corresponding care suggestions, such as changing insoles, increasing ventilation, and using antibacterial spray.

[0054] Table 3 Model prediction results

[0055]

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

[0057] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0058] It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may 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 and method based on micro-spectroscopy technology, the present invention relates to the field of intelligent foot health monitoring, and its characteristics include the following steps: The present invention discloses an intelligent foot health monitoring system based on micro-spectral technology, including a micro-spectral sensor module, a data processing and analysis unit, a wireless communication module, and a power supply module, wherein: The micro-spectral sensor module is integrated inside the shoe. The micro-spectral coverage wavelength is 255nm-1650nm and is used to collect original spectral characteristic data of humidity, microorganisms, and VOCs. The data processing and analysis unit receives data from the sensor, performs real-time processing and analysis, and determines the health status of the foot. The wireless communication module transmits data with smart devices (such as APP, mini-programs, etc.) to send monitoring results and health advice to users. The power module provides a stable power supply for the entire system, using a rechargeable battery or a micro energy harvesting device. The present invention further discloses a method for intelligent foot health monitoring based on micro-spectroscopy technology, which uses the above device for detection, including the following steps: Step 1: Since the micro-spectrometer is composed of various electronic components, the differences between electronic components and batch differences will lead to differences between the final devices, so a solution is designed to address the differences between devices. Step 2: In order to quickly and accurately predict foot health, the original spectral feature data of humidity, microorganisms, and VOCs must be collected through the micro-spectrometer calibrated in step 1. Step 3: For the training data in step 2, use the training data to train a linear regression model to generate a model, use the linear regression model to predict the humidity, microorganisms, and VOCs content to be tested, and then judge the health status of the feet based on the prediction results.

2. According to the intelligent foot health monitoring system and method based on micro-spectroscopy technology of claim 1, the specific processing process is as follows: Equipment calibration: Each micro-spectrometer collects the original data of a calibration object before leaving the factory, and adjusts the spectral intensity value according to the original spectral value of the calibration object, so that the error of the spectral value collected by all devices is less than 1%. Calibration material: standard air (oxygen (O2) accounts for about 21% of the total volume, nitrogen (N2) accounts for about 79% of the total volume, the total hydrocarbon content needs to be less than 2ppm (parts per million), and the moisture content needs to be less than 5ppm.

3. According to the intelligent foot health monitoring system and method based on micro-spectroscopy technology of claim 1, the specific collection method is as follows: ① Place the micro-spectrometer in standard air and collect a piece of spectrum data as a blank. ② On the first day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the original spectral data of 0, 4, and 8 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 12 hours and the original spectral data was collected. On the second day, shoes were worn for 8 hours, during which time the feet were sweated by exercise and the spectral data of 0, 6, and 12 hours were collected. Then the feet were placed in a plastic bag with holes and cultured for 24 hours and the original spectral data was collected. ③ Quantitatively calibrate the labels of the original spectral feature data as the data to be trained. Spectroscopic method is used to detect humidity, microorganisms, and VOCs content to determine the health status of the feet. Principle: Different substances have corresponding absorption wavelengths. The machine learning model is trained using training data labeled with humidity, microorganisms, and VOCs content. The model is used to predict humidity, microorganisms, and VOCs content to determine the health status of the feet. The original spectral characteristic data of humidity, microorganisms, and VOCs cover wavelengths of 255nm-1650nm, and corresponding absorption wavelengths of corresponding indicators are selected for training. The training wavelength for humidity is 1300nm, the training wavelength for microorganisms is 365nm, and the training wavelength for VOCs is 1650nm.

4. According to claim 1, a smart foot health monitoring system and method based on micro-spectroscopy technology are specifically described as follows: The linear regression model used in the present invention is the LASSO model, and regularization and ridge regression are used to estimate the L1 and L2 parameters in the model and the determination coefficient R 2 Evaluate model accuracy. The linear regression model predicts that when the humidity exceeds 70%, the system will issue an early warning to remind the user that the humidity is too high and there is a risk of rapid growth of microorganisms. The linear regression model predicts that when the number of microorganisms is less than 100 units, it is judged as low risk, 100-1000 units is medium risk, and when it is greater than 1000 units, it is judged as high risk, and a health warning of the corresponding level will be issued. The linear regression model predicts that when the VOCs concentration is less than 0.5 mg / m 3 It is judged as low risk, 0.5mg / m 3 -1mg / m 3 Medium risk, greater than 1 mg / m 3 The system sends warnings and suggestions to users based on the monitoring results. Based on 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 excessive humidity, microbial growth, and excessive VOCs concentration, and provides corresponding care suggestions, such as changing insoles, increasing ventilation, and using antibacterial spray.

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