Mental fatigue monitoring method and system based on nose wing skin temperature

Abstract

The invention discloses a mental fatigue monitoring method and system based on the nose wing skin temperature. The method comprises the steps that nose bridge skin temperature data and environment temperature data are obtained, the nose bridge skin temperature data are preprocessed, a low-temperature compensation model is constructed, dynamic calibration is conducted, and a nose bridge skin temperature threshold value is obtained; calculating a nose bridge skin temperature variation, extracting a time domain feature of the nose bridge skin temperature, inputting the nose bridge skin temperature variation and the time domain feature into a pre-trained evaluation model, and outputting a mental fatigue grade score; and comparing the mental fatigue grade score with a preset fatigue threshold value, and outputting a fatigue state judgment result according to a comparison result. The fatigue level can be accurately output, accurate and reliable judgment is guaranteed, and scientific and convenient mental fatigue monitoring is provided.

Description

Technical Field

[0001] This invention relates to the field of health monitoring technology, and in particular to a method and system for monitoring mental fatigue based on nasal skin temperature. Background Technology

[0002] Mental fatigue, a prevalent sub-health issue among contemporary working professionals, is particularly prominent in modern office settings where mental labor is predominant. Employees often work under conditions of multitasking, high-intensity decision-making, and information overload, which can easily lead to decreased attention span, emotional exhaustion, and reduced work efficiency. It may also increase health risks such as chronic cardiovascular and cerebrovascular diseases and anxiety. Therefore, developing a real-time mental fatigue monitoring system suitable for daily office environments is of great significance for providing accurate early warnings and scientific interventions for mental fatigue.

[0003] Mental fatigue is a neurophysiological phenomenon induced by prolonged cognitive load, mainly manifested as poor concentration, low mood, and reduced work efficiency, and is a common manifestation of sub-health.

[0004] Current technologies for detecting mental fatigue primarily rely on electroencephalography (EEG), electrocardiography (ECG, heart rate variability (HRV)), electrooculography (EOG), and facial micro-expression analysis to assess mental fatigue by monitoring relevant physiological signals or characteristics. However, research has revealed that mental fatigue detection systems relying on EEG and ECG technologies generally suffer from drawbacks such as discomfort when worn, limited usage scenarios, and susceptibility to environmental stimuli. These methods are largely confined to laboratory research. While commercially available EEG and facial micro-expression analysis technologies offer better reliability, they are still affected by environmental factors such as changes in lighting and facial occlusion, impacting the accuracy of monitoring. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring mental fatigue based on nasal skin temperature. This invention reflects the user's level of mental fatigue by real-time monitoring and analysis of the rate of change in nasal bridge skin temperature, providing the user with a real-time report on mental fatigue.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for monitoring mental fatigue based on nasal skin temperature, comprising: Acquire nasal bridge skin temperature data and ambient temperature data, and obtain the average initial nasal bridge skin temperature based on the nasal bridge skin temperature data; The nasal bridge skin temperature data is preprocessed, and a low temperature compensation model is constructed based on the ambient temperature data and a preset ambient temperature threshold. Dynamic calibration is then performed to obtain the nasal bridge skin temperature threshold. The change in nasal bridge skin temperature is calculated, the temporal features of nasal bridge skin temperature are extracted, and the change in nasal bridge skin temperature and temporal features are input into a pre-trained evaluation model to output a mental fatigue level score. The mental fatigue level score is compared with a preset fatigue threshold, and the fatigue status judgment result is output based on the comparison result.

[0007] As a further technical solution, when the ambient temperature data is lower than a preset ambient temperature threshold, a low-temperature compensation model is activated. The constructed low-temperature compensation model is expressed as follows: ;in, This indicates the temperature threshold of the skin on the bridge of the nose. This represents the average initial temperature of the skin on the bridge of the nose. Indicates the compensation coefficient. This represents ambient temperature data; If the ambient temperature data is greater than or equal to the ambient temperature threshold, the low temperature compensation model will not be activated.

[0008] As a further technical solution, based on the real-time acquired nasal bridge skin temperature data and nasal bridge skin temperature threshold, the change in nasal bridge skin temperature is calculated, specifically expressed as follows: ;in, This indicates the change in temperature of the skin on the bridge of the nose. This indicates the real-time temperature data of the skin on the bridge of the nose. This indicates the threshold temperature of the skin on the bridge of the nose.

[0009] As a further technical solution, the extracted temporal features of nasal bridge skin temperature include the rate of change and fluctuation amplitude of nasal bridge skin temperature. The rate of change is the rate of decrease in nasal bridge skin temperature per unit time, and the fluctuation amplitude is the standard deviation of nasal bridge skin temperature within a set time window.

[0010] As a further technical solution, the pre-trained evaluation model is represented as follows: ;in, This represents the age correction factor. This indicates the change in temperature of the skin on the bridge of the nose. Indicates time interval, This indicates the rate of change in skin temperature on the bridge of the nose. Indicates the fluctuation range. This represents the gender correction factor.

[0011] As a further technical solution, the fatigue threshold is divided into a first fatigue threshold, a second fatigue threshold, and a third fatigue threshold; when the mental fatigue level score is less than the first fatigue threshold, it is determined to be a non-mental fatigue state; when the mental fatigue level score is greater than or equal to the first fatigue threshold and less than the second fatigue threshold, it is determined to be a mild mental fatigue state; when the mental fatigue level score is greater than or equal to the second fatigue threshold and less than the third fatigue threshold, it is determined to be a moderate mental fatigue state; when the mental fatigue level score is greater than the third fatigue threshold, it is determined to be a severe mental fatigue state.

[0012] As a further technical solution, after outputting the fatigue state judgment result, the change in nasal skin temperature, time-domain characteristics, and fatigue state judgment result are transmitted to a mobile terminal via Bluetooth for display.

[0013] Secondly, the present invention provides a mental fatigue monitoring system based on nasal skin temperature, comprising the following modules: The temperature acquisition module is configured to acquire nose bridge skin temperature data and ambient temperature data, and obtain the initial average nose bridge skin temperature based on the nose bridge skin temperature data. The data processing and calibration module is configured to: preprocess the nasal bridge skin temperature data, construct a low temperature compensation model based on the ambient temperature data and a preset ambient temperature threshold, perform dynamic calibration, and obtain the nasal bridge skin temperature threshold. The mental fatigue level score output module is configured to: calculate the change in nasal bridge skin temperature, extract the time-domain features of nasal bridge skin temperature, and input the change in nasal bridge skin temperature and time-domain features into a pre-trained evaluation model to output a mental fatigue level score. The fatigue state determination module is configured to compare the mental fatigue level score with a preset fatigue threshold and output the fatigue state determination result based on the comparison result.

[0014] One or more technical solutions of the present invention have the following beneficial effects: This invention collects nasal bridge skin temperature data, eliminating interference from light levels and head / face obstruction, making it less affected by environmental factors compared to techniques like electrooculography and facial micro-expression analysis. Furthermore, it combines this data with dynamic calibration using an ambient temperature sensor (error ±0.1℃) to extract temporal features such as nasal temperature change rate and fluctuation amplitude. These features are then input into a specific evaluation model to accurately output fatigue levels, ensuring accurate and reliable assessments.

[0015] The mental fatigue monitoring system provided by this invention can be directly integrated into eyeglasses, an everyday item. It is comfortable to obtain data on the skin temperature of the bridge of the nose and the ambient temperature. It is suitable for various scenarios such as daily office work and solves the problems of discomfort and limited use scenarios of technologies such as EEG and heart rate variability.

[0016] This invention transmits data wirelessly to mobile phones and other terminals, displaying a real-time nasal temperature change curve and fatigue level (color-coded). When moderate to severe fatigue occurs, vibration and pop-up reminders are triggered, helping users to take timely intervention measures, improve mental efficiency, and reduce the incidence of chronic fatigue and related health risks. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is a flowchart of a method for monitoring mental fatigue based on nasal skin temperature according to the present invention. Detailed Implementation

[0019] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example 1 A 2021 paper published in Volume 145 of the *International Journal of Human-Computer Studies*, titled "Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments," indicates that nasal temperature drops rapidly by approximately 0.25°C (p<0.05) during high-intensity mental activity compared to low-intensity mental activity. The paper "The mental nose and the Pinocchio effect: thermography, planning, anxiety, and lies" even found that nasal temperature decreases by approximately 2.4°C when facing real high mental workload and stress, compared to an average decrease of 0.9°C (p<0.05) when facing simulated mental work. The paper "Facial temperature as a measure of mental workload" found that nasal temperature decreases by up to 1.5°C under high mental workload. The mainstream explanation for this phenomenon is that when the brain's energy consumption increases, blood is preferentially supplied to the central nervous system, leading to vasoconstriction in the superficial capillaries of the head and a decrease in nasal temperature. Therefore, this embodiment provides a method for monitoring mental fatigue based on nasal wing skin temperature. By monitoring and analyzing the rate of change in nasal bridge skin temperature in real time, it reflects the user's level of mental fatigue, providing scientific assurance for the user's mental efficiency. Figure 1 As shown, the specific steps are as follows: S1: Obtain the skin temperature data of the bridge of the nose and the ambient temperature data, and obtain the average initial skin temperature of the bridge of the nose based on the skin temperature data of the bridge of the nose.

[0021] In step S1, a high-precision temperature sensor (accuracy 0.1℃ or higher) embedded inside the nose pads of the glasses, with a sampling frequency of 10Hz (collecting data once every 100 milliseconds), can quickly generate a continuous data sequence as the temperature changes. The high-precision temperature sensor collects real-time temperature data of the bridge of the nose. (Subsequent real-time acquisition of nasal bridge skin temperature data is represented as follows) At the same time, the ambient temperature is collected by the ambient temperature sensor at the head of the mirror frame pile. Ambient temperature Used to compensate for environmental disturbances.

[0022] After the user wears the glasses, a high-precision temperature sensor continuously collects the skin temperature data of the bridge of the nose in a static state five times, and takes the average value to obtain the initial average temperature of the nostrils. The ambient temperature sensor collects real-time ambient temperature data. As the initial ambient temperature , and As a benchmark value.

[0023] S2: The nose bridge skin temperature data is preprocessed, and a low temperature compensation model is constructed based on the ambient temperature data and the preset ambient temperature threshold. Dynamic calibration is performed to obtain the nose bridge skin temperature threshold.

[0024] In step S2, the nose bridge skin temperature data is preprocessed by using a low-power chip to denoise and filter the nose bridge skin temperature data.

[0025] Based on ambient temperature data A low-temperature compensation model is constructed using a preset ambient temperature threshold and dynamically calibrated to obtain a nose bridge skin temperature threshold for subsequent feature extraction. Specifically, in this embodiment, the preset ambient temperature threshold is 22°C. If the ambient temperature is below the preset threshold (22℃), the low-temperature compensation model is activated, automatically increasing the skin temperature threshold on the bridge of the nose. The constructed low-temperature compensation model is expressed as follows: ;in, This indicates the temperature threshold of the skin on the bridge of the nose. This represents the average initial temperature of the skin on the bridge of the nose. Indicates the compensation coefficient. This represents ambient temperature data; When ambient temperature data If the ambient temperature is greater than or equal to the threshold temperature (22℃), the low-temperature compensation model will not be activated, and the average initial temperature of the nose wing will be used directly. Used for subsequent feature extraction.

[0026] S3: Calculate the change in nasal bridge skin temperature, extract the temporal features of nasal bridge skin temperature, and input the change in nasal bridge skin temperature and temporal features into a pre-trained evaluation model to output a mental fatigue level score.

[0027] In step S3, the nose bridge skin temperature data obtained in real time in the above steps is first... and the skin temperature threshold of the bridge of the nose The change in skin temperature on the bridge of the nose was calculated and expressed as follows: ;in, This indicates the change in temperature of the skin on the bridge of the nose. This indicates the real-time temperature data of the skin on the bridge of the nose. This indicates the threshold temperature of the skin on the bridge of the nose.

[0028] Next, the temporal features of the nasal bridge skin temperature are extracted. These features include the rate of change and fluctuation amplitude of the nasal bridge skin temperature. The rate of change (°C / min) is the rate at which the nasal bridge skin temperature decreases per unit time, expressed as: The fluctuation range is the standard deviation of the nasal bridge skin temperature within the set time window. It is used to reflect the stability of the skin temperature on the bridge of the nose.

[0029] The change in skin temperature on the bridge of the nose ( ) and temporal characteristics ( , The input is fed into a pre-trained evaluation model, which is represented as follows: ;in, This represents the age correction factor, in this embodiment. Take a value of 0.9 to 1.1. This indicates the change in temperature of the skin on the bridge of the nose. Indicates time interval, This indicates the rate of change in skin temperature on the bridge of the nose. Indicates the fluctuation range. This represents the gender correction factor for females. Value is 1.92, male The value is set to 0.92. The evaluation model outputs a score indicating the level of mental fatigue. .

[0030] S4: Compare the mental fatigue level score with the preset fatigue threshold, and output the fatigue status judgment result based on the comparison result.

[0031] In step S4, the fatigue threshold is divided into a first fatigue threshold. Second fatigue threshold and the third fatigue threshold When the mental fatigue level score Less than the first fatigue threshold When the level is low, it is determined to be a non-mental fatigue state; when the level of mental fatigue is high... The score is greater than or equal to the first fatigue threshold. And less than the second fatigue threshold When the mental fatigue level score is [score missing], it is determined to be a state of mild mental fatigue; when the mental fatigue level score is [score missing], it is determined to be a state of mild mental fatigue. Greater than or equal to the second fatigue threshold And less than the third fatigue threshold When the mental fatigue level score is [score missing], it is determined to be a state of moderate mental fatigue; when the mental fatigue level score is [score missing], it is determined to be a state of moderate mental fatigue. Greater than the third fatigue threshold At that time, it was determined to be a state of severe mental fatigue.

[0032] After outputting the fatigue state assessment result, the change in nasal bridge skin temperature will be... Temporal characteristics ( , The fatigue status assessment results are transmitted via Bluetooth to a mobile terminal (such as a mobile phone or a smart band app) for display. The display includes: a curve showing changes in nasal temperature; fatigue level (color markings: green = non-fatigue, yellow = mild, orange = moderate, red = severe) and warning prompts (vibration and pop-up reminders are triggered when fatigue is moderate or above).

[0033] In this embodiment, taking a woman as an example, she wears glasses with an embedded temperature sensor (a high-precision miniature temperature sensor integrated in the nose pad, with an accuracy of 0.1℃), and her mental fatigue state is determined through the following process: During user operation, the temperature sensor built into the nose pad collects real-time data on the skin temperature of the bridge of the nose. The average baseline value obtained from five consecutive measurements in the initial static state is... 33.5℃, ambient temperature 25℃ (If the temperature is above 22℃, use the average initial nasal skin temperature directly).

[0034] During the work, the real-time temperature data of the bridge of the nose was measured sequentially. The temperatures were 33.2℃, 32.8℃, 32.5℃, and 32.2℃. Then, the feature data were extracted and calculated to obtain the change in skin temperature on the bridge of the nose. The temperatures are -0.3℃, -0.7℃, -1.0℃, and -1.3℃, respectively; the set time interval is... Calculate the rate of change in skin temperature on the bridge of the nose in 1 minute. The temperature ranges were -0.3℃ / min, -0.7℃ / min, -1.0℃ / min, and -1.3℃ / min, respectively; the standard deviation of temperature within the time window was set to σ = 0.1, 0.2, 0.3, and 0.4 (to reflect the steady decrease in the temperature of the skin on the bridge of the nose).

[0035] The change in skin temperature on the bridge of the nose ( ) and temporal characteristics ( , The input is fed into the pre-trained evaluation model. In this embodiment, α=1.0 and β=1.92. (fatigue) represents the calculated level of mental fatigue score. The mental fatigue score is calculated based on this: when... At 33.2℃, =1.0×[-200×(-0.3)+100×(-0.3)]+500×0.1×1.92=1.0×(60 - 30)+96 = 126. Similarly, the fatigue scores at 32.8°C, 32.5°C, and 32.2°C are respectively f 2 = 262, f 3 = 388, f 4 = 514. Then, fatigue level determination is carried out: Set the thresholds F1, F2, and F3 to be 100 (mild fatigue), 300 (moderate fatigue), and 500 (severe fatigue) respectively. According to the obtained mental fatigue level scores above, the fatigue levels are respectively: =126 > F1 - mild mental fatigue state; F1 = 100 ≤ =262 < F2 = 300 - mild mental fatigue state; F2 = 300 ≤ =388 < F3 = 500 - moderate mental fatigue state (remind to rest); =514 > F3 = 500 - severe fatigue state (forcefully remind to stop working).

[0036] Finally, the change amount of the nasal bridge skin temperature , time domain features ( , ) and the obtained fatigue state judgment results are transmitted to the mobile terminal via Bluetooth, and the change curve of the nasal bridge skin temperature (from 33.5°C to 32.2°C) and the fatigue level color markings (green: non - mental fatigue; yellow: mild mental fatigue; orange: moderate mental fatigue; red: severe mental fatigue) are displayed in real - time; at the same time, different warning prompts are issued according to the fatigue level. There is no warning for non - mental fatigue and mild mental fatigue. When it is moderate fatigue, a vibration pop - up window ("It is recommended to rest") is triggered, and when it is severe mental fatigue, a pop - up window shows "Stop working immediately".

[0037] Embodiment 2 In this embodiment, a mental fatigue monitoring system based on the nasal wing skin temperature is provided, including the following modules: Temperature acquisition module, configured to: acquire the nasal bridge skin temperature data and environmental temperature data, and obtain the average value of the initial nasal bridge skin temperature according to the nasal bridge skin temperature data; Data processing and calibration module, configured to: pre - process the nasal bridge skin temperature data, and construct a low - temperature compensation model based on the environmental temperature data and a preset environmental temperature threshold for dynamic calibration to obtain the nasal bridge skin temperature threshold; Mental fatigue level score output module, configured to: calculate the change amount of the nasal bridge skin temperature, extract the time domain features of the nasal bridge skin temperature, and input the change amount of the nasal bridge skin temperature and the time domain features into a pre - trained evaluation model to output the mental fatigue level score; The fatigue state determination module is configured to compare the mental fatigue level score with a preset fatigue threshold and output the fatigue state determination result based on the comparison result.

[0038] Various modifications and variations of this invention will be apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for monitoring mental fatigue based on nasal skin temperature, characterized in that, include: Acquire nasal bridge skin temperature data and ambient temperature data, and obtain the average initial nasal bridge skin temperature based on the nasal bridge skin temperature data; The nasal bridge skin temperature data is preprocessed, and a low temperature compensation model is constructed based on the ambient temperature data and a preset ambient temperature threshold. Dynamic calibration is then performed to obtain the nasal bridge skin temperature threshold. The change in nasal bridge skin temperature is calculated, the temporal features of nasal bridge skin temperature are extracted, and the change in nasal bridge skin temperature and temporal features are input into a pre-trained evaluation model to output a mental fatigue level score. The mental fatigue level score is compared with a preset fatigue threshold, and the fatigue status judgment result is output based on the comparison result.

2. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, When the ambient temperature data is lower than the preset ambient temperature threshold, the low temperature compensation model is activated. The constructed low temperature compensation model is expressed as follows: ;in, This indicates the temperature threshold of the skin on the bridge of the nose. This represents the average initial temperature of the skin on the bridge of the nose. Indicates the compensation coefficient. This represents ambient temperature data; If the ambient temperature data is greater than or equal to the ambient temperature threshold, the low temperature compensation model will not be activated.

3. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, Based on real-time acquired nasal bridge skin temperature data and nasal bridge skin temperature threshold, the change in nasal bridge skin temperature is calculated, specifically expressed as follows: ;in, This indicates the change in temperature of the skin on the bridge of the nose. This indicates the real-time temperature data of the skin on the bridge of the nose. This indicates the threshold temperature of the skin on the bridge of the nose.

4. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, The extracted temporal features of nasal skin temperature include the rate of change and the amplitude of fluctuation of nasal skin temperature. The rate of change is the rate of decrease of nasal skin temperature per unit time, and the amplitude of fluctuation is the standard deviation of nasal skin temperature within a set time window.

5. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, The pre-trained evaluation model is represented as follows: ;in, This represents the age correction factor. This indicates the change in temperature of the skin on the bridge of the nose. Indicates time interval, This indicates the rate of change in skin temperature on the bridge of the nose. Indicates the fluctuation range. This represents the gender correction factor.

6. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, The fatigue threshold is divided into a first fatigue threshold, a second fatigue threshold, and a third fatigue threshold; when the mental fatigue level score is less than the first fatigue threshold, it is determined to be a non-mental fatigue state. When the mental fatigue level score is greater than or equal to the first fatigue threshold and less than the second fatigue threshold, it is judged as a mild state of mental fatigue. When the mental fatigue level score is greater than or equal to the second fatigue threshold and less than the third fatigue threshold, it is judged as a moderate state of mental fatigue. When the mental fatigue level score is greater than the third fatigue threshold, it is judged as a state of severe mental fatigue.

7. The method for monitoring mental fatigue based on nasal skin temperature as described in claim 1, characterized in that, After outputting the fatigue state judgment result, the change in nasal bridge skin temperature, time domain characteristics, and fatigue state judgment result are transmitted to the mobile terminal via Bluetooth for display.

8. A mental fatigue monitoring system based on nasal ala skin temperature, characterized in that, Includes the following modules: The temperature acquisition module is configured to acquire nose bridge skin temperature data and ambient temperature data, and obtain the initial average nose bridge skin temperature based on the nose bridge skin temperature data. The data processing and calibration module is configured to: preprocess the nasal bridge skin temperature data, construct a low temperature compensation model based on the ambient temperature data and a preset ambient temperature threshold, perform dynamic calibration, and obtain the nasal bridge skin temperature threshold. The mental fatigue level score output module is configured to: calculate the change in nasal bridge skin temperature, extract the time-domain features of nasal bridge skin temperature, and input the change in nasal bridge skin temperature and time-domain features into a pre-trained evaluation model to output a mental fatigue level score. The fatigue state determination module is configured to compare the mental fatigue level score with a preset fatigue threshold and output the fatigue state determination result based on the comparison result.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by a processor, the program implements the steps of a method for monitoring mental fatigue based on nasal skin temperature as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for monitoring mental fatigue based on nasal skin temperature as described in any one of claims 1-7.

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