A smart mouse management method and integrated system with a heart rate detection function

Abstract

The application relates to the technical field of human-computer interaction, and discloses a management method and an integrated system of an intelligent mouse with a heart rate detection function, which comprises the following steps: emitting specific wavelength light to the skin contacted by the hand, and receiving reflected light of the specific wavelength light; converting the reflected light into light electric signals, analyzing ambient light interference noise and motion artifact noise in the light electric signals, denoising the light electric signals, and obtaining denoised light electric signals; calculating signal peak values of the denoised light electric signals, marking a sequence heart rate value of a target user, collecting user behavior data of the target user in a time period corresponding to the sequence heart rate value, analyzing mouse operation frequency and mouse operation types of the target user, and analyzing application scenarios of the target user; analyzing a scene state of the target user, and constructing a mouse management report of the heart rate detection mouse. The application can improve user experience of users using the intelligent mouse.

Description

TECHNICAL FIELD

[0001] The application relates to a smart mouse management method and integrated system integrating heart rate detection functions and belongs to the technical field of human-computer interaction. BACKGROUND

[0002] Smart mouse management refers to optimizing and personalizing settings of mouse use by using advanced technical means and intelligent algorithms to improve user work efficiency, comfort and overall use experience. The goal of smart mouse management is to upgrade mouse functions intelligently so that the mouse is not only an input device but also an intelligent tool that can improve work efficiency, focus on user health and provide personalized experience.

[0003] Traditional smart mouse management methods are usually limited to basic user settings such as adjusting DPI (dots per inch) sensitivity, scroll wheel speed and key functions. This method cannot automatically adjust settings according to user's actual needs and physiological state, ignores user behavior pattern analysis, and therefore cannot provide customized use recommendations according to different work or game scenarios, thus failing to achieve deeper optimization of user experience and improvement of work efficiency. SUMMARY

[0004] The application provides a smart mouse management method and integrated system integrating heart rate detection functions, which mainly aims to improve user experience of smart mouse users.

[0005] To achieve the above purpose, the application provides a smart mouse management method integrating heart rate detection functions, which comprises the following steps:

[0006] Determining the contact between the heart rate detection mouse and the target user's hand and the skin, emitting specific wavelength light from the PPG sensor of the heart rate detection mouse to the contact skin, and receiving the reflected light of the specific wavelength light by a pre-set photodiode;

[0007] Converting the reflected light into a light signal, analyzing the ambient light interference noise and motion artifact noise in the light signal, denoising the light signal based on the ambient light interference noise and motion artifact noise, and obtaining a denoised light signal;

[0008] Calculating the signal peak value of the denoised light signal, marking the sequence heart rate value of the target user according to the signal peak value, and collecting user behavior data of the target user in a time period corresponding to the sequence heart rate value, wherein the user behavior data includes mouse behavior data and computer screen operation data;

[0009] Based on the user behavior data, analyze the mouse operation frequency and mouse operation type of the target user, and based on the computer screen operation data, analyze the application scenario of the target user;

[0010] Based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario, use a preset state intelligent analysis algorithm to analyze the scene state of the target user, and based on the scene state, construct a mouse management report of the heart rate detection mouse.

[0011] Optionally, the heart rate detection mouse corresponding PPG sensor emits specific wavelength light to the hand contact skin, including:

[0012] Analyze the dirtiness index of the hand contact skin;

[0013] According to the dirtiness index, determine the coverage area of the hand contact skin on the heart rate detection mouse;

[0014] Analyze the coincidence coefficient of the coverage area and the PPG sensor corresponding transparent window;

[0015] When the coincidence coefficient meets the preset coincidence threshold, activate the PPG sensor corresponding LED to obtain an activated LED;

[0016] The activated LED emits specific wavelength light to the hand contact skin.

[0017] Optionally, the analysis of the dirtiness index of the hand contact skin includes:

[0018] Analyze the reflectivity coefficient and dirtiness type of the hand contact skin, wherein the dirtiness type includes oil, protein, and carbohydrate;

[0019] Determine the dirtiness concentration of the dirtiness type, wherein the dirtiness concentration includes oil concentration, protein concentration, and carbohydrate concentration;

[0020] Based on the reflectivity coefficient, the oil concentration, the protein concentration, and the carbohydrate concentration, use the following formula to calculate the dirtiness index of the hand contact skin:

[0021] (DI)=α*(RC)+β*(GC)+μ*(PC)+ρ*(CC)

[0022] Wherein, (DI) represents a dirt index of hand contact with skin, (RC) represents a reflectance coefficient, a represents a weight of the reflectance coefficient, (GC) represents a grease concentration, β represents a weight of the grease concentration, (PC) represents a protein concentration, μ represents a weight of the protein concentration, (CC) represents a carbohydrate concentration, and ρ represents a weight of the carbohydrate concentration.

[0023] Optionally, the converting the reflected light into a light electric signal comprises:

[0024] Collecting photon energy generated by the reflected light being reflected into the photodiode corresponding to the reflected light;

[0025] According to the photon energy, constructing an electron-hole pair of a semiconductor material in the photodiode;

[0026] Separating the electron-hole pair by using a built-in electric field in the photodiode to obtain a separation current;

[0027] Converting the separation current into an initial light electric signal by using an external circuit of the photodiode;

[0028] Gaining the initial light electric signal to obtain the light electric signal.

[0029] Optionally, the constructing the multi-scale network of the printed 3D model according to the high stress area and the boundary area comprises:

[0030] Defining a regional network node of the high stress area and the boundary area;

[0031] Constructing a coarse network of the regional network node;

[0032] According to the coarse network, constructing a meso network of the high stress area and the boundary area;

[0033] Determining a key area of the high stress area and the boundary area;

[0034] Constructing a micro network of the key area;

[0035] Establishing a network bridge of the coarse network, the meso network and the micro network;

[0036] Based on the network bridge, the coarse network, the meso network and the micro network, constructing the multi-scale network of the printed 3D model.

[0037] Optionally, the analyzing the interference coefficient of the ambient light frequency component on the frequency domain light electric signal comprises:

[0038] Analyzing a frequency and a frequency bandwidth of the ambient light frequency component;

[0039] Based on the frequency and the frequency bandwidth, the interference coefficient of the ambient light frequency component on the frequency domain light signal is calculated by using the following formula:

[0040]

[0041] Wherein, I represents the interference coefficient of the ambient light frequency component f on the frequency domain light signal, X(f) represents the power of the frequency domain light signal at the ambient light frequency component f, f env represents the frequency of the ambient light frequency component f, B env represents the frequency bandwidth of the ambient light frequency component f, f max represents the maximum frequency of the frequency domain light signal, f mim represents the minimum frequency of the frequency domain light signal.

[0042] Optionally, the signal peak value of the denoised light signal is calculated, comprising:

[0043] Filtering the denoised light signal to obtain a filtered light signal;

[0044] Deriving the filtered light signal to obtain a derived light signal;

[0045] Marking the zero-crossing point of the derived light signal;

[0046] Determining the local maximum value of the zero-crossing point;

[0047] According to the local maximum value and the preset amplitude threshold, the signal peak value of the denoised light signal is determined.

[0048] Optionally, the application scenario of the target user is analyzed according to the computer screen operation data, comprising

[0049] Defining the potential scenario of the target user;

[0050] Extracting the screen operation elements of the computer screen operation data;

[0051] Analyzing the element correlation and the scene correlation of the screen operation elements;

[0052] Determining the application scenario in the potential scenario through the element correlation and the scene correlation.

[0053] Optionally, the scene state of the target user is analyzed based on the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scenario by using a preset state intelligent analysis algorithm, comprising:

[0054] extract the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene heart rate value feature, operation frequency feature, operation type feature and application scene feature respectively;

[0055] define a scene state group of the target user;

[0056] analyze the scene state score of the scene state group by the heart rate value feature, operation frequency feature, operation type feature and application scene feature by using the state intelligent analysis algorithm;

[0057] determine the scene state of the target user by the scene state score.

[0058] In order to solve the above problems, the application further provides a smart mouse management system integrated with a heart rate detection function, which comprises:

[0059] a reflected light acquisition module, configured to determine that the heart rate detection mouse and the hand of the target user contact the skin, emit specific wavelength light to the hand contact skin by using the PPG sensor of the heart rate detection mouse, and receive reflected light of the specific wavelength light by using a preset photodiode;

[0060] a light signal denoising module, configured to convert the reflected light into a light signal, analyze ambient light interference noise and motion artifact noise in the light signal, denoise the light signal based on the ambient light interference noise and the motion artifact noise, and obtain a denoised light signal;

[0061] a heart rate value analysis module, configured to calculate a signal peak value of the denoised light signal, mark a sequence heart rate value of the target user according to the signal peak value, and collect user behavior data of the target user in a time period corresponding to the sequence heart rate value, wherein the user behavior data comprises mouse behavior data and computer screen operation data;

[0062] an application scene determination module, configured to analyze a mouse operation frequency and a mouse operation type of the target user based on the user behavior data, and analyze an application scene of the target user according to the computer screen operation data;

[0063] a scene state analysis module, configured to analyze a scene state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene, and construct a mouse management report of the heart rate detection mouse according to the scene state.

[0064] Compared with the problems described in the background art, first, by ensuring the stable connection of the heart rate detection mouse with the user's hand contacting the skin, and combining the PPG sensor to emit light of a specific wavelength, the scheme can accurately capture the user's heart rate data, not only improving the convenience of measurement, but also ensuring the comfort of the user, second, by denoising the reflected light signal, the influence of environmental light interference noise and motion artifact noise is effectively reduced, the accuracy and reliability of the heart rate data are improved, this step is crucial for maintaining data quality in a variable use environment, third, by calculating the signal peak value of the denoised light signal to mark the sequence heart rate value, and combining the user behavior data, the scheme can monitor the user's physiological state in real time when operating the mouse, and provide personalized feedback to the user, in addition, analyzing the mouse operation frequency and type, and judging the application scene according to the computer picture operation data, can help to deeply understand the user's work mode and behavior habit, so as to provide more thoughtful service for the user, finally, by using the preset state intelligent analysis algorithm, the scheme can comprehensively analyze the user's scene state according to the multi-dimensional data such as heart rate, mouse operation frequency, operation type and application scene, and construct a mouse management report accordingly, which can not only help the user to manage and improve work efficiency, but also provide data support for health management, and promote the improvement of the user's life quality. Therefore, the present application can improve the user experience of the user using the intelligent mouse. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 A flowchart of an intelligent mouse management method with heart rate detection function provided by an embodiment of the present application is shown.

[0066] Figure 2 A module diagram of the intelligent mouse management method with heart rate detection function provided by an embodiment of the present application is shown.

[0067] The purpose of the present application, the functional characteristics and the advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0069] The embodiment of the present application provides an intelligent mouse management method with heart rate detection function. The execution subject of the intelligent mouse management method with heart rate detection function includes but is not limited to at least one of the electronic devices which can be configured to execute the method provided by the embodiment of the present application, such as a server, a terminal and the like. In other words, the intelligent mouse management method with heart rate detection function can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0070] Embodiment 1

[0071] Referring to Figure 1 Fig. 1 shows a flowchart of a method for managing a smart mouse with a heart rate detection function according to an embodiment of the present application. In this embodiment, the method for managing a smart mouse with a heart rate detection function includes:

[0072] S1, determining that a heart rate detection mouse and a hand of a target user are in contact with skin, emitting light of a specific wavelength from a PPG sensor corresponding to the heart rate detection mouse to the skin in contact with the hand, and receiving reflected light of the specific wavelength by a preset photodiode.

[0073] It should be noted that the heart rate detection mouse refers to a computer mouse integrated with heart rate monitoring technology, the target user refers to the main consumer group of the heart rate detection mouse, such as programmers, designers, and game players, and the hand in contact with the skin refers to the skin surface in contact with the heart rate detection mouse when the user uses the heart rate detection mouse.

[0074] The present application can ensure the accuracy of heart rate measurement by emitting light of a specific wavelength from a PPG sensor corresponding to the heart rate detection mouse to the skin in contact with the hand.

[0075] In detail, the step of emitting light of a specific wavelength from a PPG sensor corresponding to the heart rate detection mouse to the skin in contact with the hand includes:

[0076] analyzing a dirtiness index of the skin in contact with the hand;

[0077] determining a coverage area of the heart rate detection mouse on the skin in contact with the hand according to the dirtiness index;

[0078] analyzing a coincidence coefficient of the coverage area and a transparent window corresponding to the PPG sensor;

[0079] when the coincidence coefficient meets a preset coincidence threshold, activating an LED corresponding to the PPG sensor to obtain an activated LED;

[0080] emitting light of a specific wavelength from the activated LED to the skin in contact with the hand.

[0081] The dirtiness index refers to a quantitative index for describing the degree of contamination of the skin surface contacted by the hand. The coverage area refers to the area of the skin surface contacted by the heart rate detection mouse when the hand contacts the skin. The PPG sensor refers to a sensor for monitoring heart rate by emitting light of a specific wavelength and detecting changes in the light after penetrating the skin. The transparent window refers to a transparent area on the heart rate detection mouse for placing the PPG sensor. The coincidence coefficient refers to a numerical value describing the degree of coincidence between the coverage area and the transparent window of the PPG sensor. The coincidence threshold refers to a standard for determining whether the coincidence coefficient is high enough. The activated LED refers to a specific wavelength LED in the PPG sensor that is started or turned on. The specific wavelength light refers to the light emitted by the PPG sensor for heart rate measurement, which is usually red light (about 660 nanometers).

[0082] Further, the analysis of the dirtiness index of the hand contacting the skin includes:

[0083] Analyzing the reflectivity coefficient and the dirtiness type of the hand contacting the skin, wherein the dirtiness type includes grease, protein, and carbohydrate;

[0084] Determining the dirtiness concentration of the dirtiness type, wherein the dirtiness concentration includes grease concentration, protein concentration, and carbohydrate concentration;

[0085] Based on the reflectivity coefficient, the grease concentration, the protein concentration, and the carbohydrate concentration, the dirtiness index of the hand contacting the skin is calculated using the following formula:

[0086] (DI) = a * (RC) + β * (GC) + μ * (PC) + ρ * (CC)

[0087] Where (DI) represents the dirtiness index of the hand contacting the skin, (RC) represents the reflectivity coefficient, a represents the weight of the reflectivity coefficient, (GC) represents the grease concentration, β represents the weight of the grease concentration, (PC) represents the protein concentration, μ represents the weight of the protein concentration, (CC) represents the carbohydrate concentration, and ρ represents the weight of the carbohydrate concentration.

[0088] The reflectivity coefficient refers to the proportion of light reflected back from the skin surface of the hand. The grease refers to the grease-like substance on the skin surface of the hand. The protein refers to the protein-like substance on the skin surface of the hand. The carbohydrate refers to the carbohydrate-like substance on the skin surface of the hand. The grease concentration refers to the concentration of grease on the skin surface. The protein concentration refers to the concentration of protein on the skin surface. The carbohydrate concentration refers to the concentration of carbohydrate on the skin surface. The weight refers to the relative importance of the dirtiness concentration in calculating the dirtiness index.

[0089] It should be noted that in the present application, the dirt index calculated by the above formula can analyze whether the heart rate signal can be effectively monitored through hand contact with the skin, thereby improving the accuracy of later heart rate analysis. By multiplying the dirt concentration by its corresponding weight and adding the results, a linear combination can be obtained, which reflects the combined effect of each dirt concentration.

[0090] The present application can effectively receive the reflected light of specific wavelength light by using the preset photodiode to receive the reflected light of specific wavelength light, and convert it into useful physiological information. Among them, the photodiode refers to the photoelectric sensor integrated in the heart rate detection mouse, which is used to detect light of a specific wavelength, and the reflected light refers to the light reflected back from the target user's hand skin.

[0091] S2, convert the reflected light into a light signal, analyze the ambient light interference noise and motion artifact noise in the light signal, denoise the light signal based on the ambient light interference noise and motion artifact noise, and obtain a denoised light signal.

[0092] The present application converts the reflected light into a light signal as the data basis for heart rate analysis.

[0093] In detail, the conversion of the reflected light into a light signal includes:

[0094] Collecting the photon energy generated when the reflected light is reflected into the photodiode corresponding to the reflected light;

[0095] According to the photon energy, the electron-hole pairs of the semiconductor material in the photodiode are constructed;

[0096] Separate the electron-hole pairs by using the built-in electric field in the photodiode to obtain a separation current;

[0097] Convert the separation current into an initial light signal by using the external circuit of the photodiode;

[0098] Gain the initial light signal to obtain the light signal.

[0099] The photon energy refers to the energy transferred to an electron when a reflected light photon hits a semiconductor material, the semiconductor material refers to a key component in a photodiode, and is usually silicon (Si) or gallium arsenide (GaAs), etc., the electron-hole pair refers to when the photon energy is absorbed by the semiconductor material, an electron jumps from the valence band to the conduction band, leaving an equal amount of positive hole, the built-in electric field refers to an electric field generated in the semiconductor material due to the doping of different types of impurities (n-type and p-type), the separated current refers to the movement of electrons and holes to form a current when the built-in electric field separates the electron-hole pair, the external circuit refers to a circuit connected to the photodiode, which includes resistors, capacitors and other electronic components, for converting the separated current into a usable electrical signal, the initial light electrical signal refers to the electrical signal initially converted by the external circuit of the photodiode, and the light electrical signal refers to an electrical signal realized through an amplifier in the external circuit to improve the strength of the signal.

[0100] The analysis of the ambient light interference noise and the motion artifact noise in the light electrical signal can improve the quality of the signal and improve the accuracy of the later heart rate analysis.

[0101] In detail, the analysis of the ambient light interference noise and the motion artifact noise in the light electrical signal comprises:

[0102] Removing the baseline drift in the light electrical signal to obtain a drift-removed light electrical signal;

[0103] Performing fast Fourier transform on the drift-removed light electrical signal to obtain a frequency domain light electrical signal;

[0104] Identifying an ambient light frequency component of the frequency domain light electrical signal;

[0105] Analyzing an interference coefficient of the ambient light frequency component on the frequency domain light electrical signal;

[0106] According to the interference coefficient, determining an ambient light interference noise in the ambient light frequency component;

[0107] Analyzing signal changes and time domain characteristics of the light electrical signal;

[0108] Analyzing a motion artifact noise of the light electrical signal through the signal changes and the time domain characteristics.

[0109] The de-drifted light electrical signal refers to a PPG signal with baseline drift removed through a certain signal processing method (such as high-pass filtering, wavelet transform or polynomial fitting, etc.), the frequency domain light electrical signal refers to a signal obtained by converting the de-drifted time domain PPG signal to the frequency domain through fast Fourier transform (FFT), the ambient light frequency component refers to a specific frequency related to ambient light interference identified in the frequency domain light electrical signal, the interference coefficient refers to a parameter quantifying the degree of interference of the ambient light frequency component on the PPG signal, the ambient light interference noise refers to a noise part in the PPG signal caused by the ambient light frequency component, the signal change refers to fluctuations in the PPG signal in the time domain, including sudden changes in amplitude, spikes, mutations, etc., the time domain feature refers to a feature extracted from the time domain waveform of the PPG signal, such as the mean, standard deviation, variance, heart rate variability (HRV) index and other features of the signal, and the motion artifact noise refers to noise in the PPG signal caused by the motion of the target user.

[0110] Further, the analysis of the interference coefficient of the ambient light frequency component on the frequency domain light electrical signal comprises:

[0111] analyzing the frequency and band width of the ambient light frequency component;

[0112] based on the frequency and band width, calculating the interference coefficient of the ambient light frequency component on the frequency domain light electrical signal using the following formula:

[0113]

[0114] wherein I represents the interference coefficient of the ambient light frequency component f on the frequency domain light electrical signal, X(f) represents the power of the frequency domain light electrical signal at the ambient light frequency component f, f env represents the frequency of the ambient light frequency component f, B env represents the band width of the ambient light frequency component f, f max represents the maximum frequency of the frequency domain light electrical signal, f min represents the minimum frequency of the frequency domain light electrical signal.

[0115] wherein the frequency refers to the center frequency of ambient light interference in the frequency domain, the band width refers to the frequency band range occupied by ambient light interference in the frequency domain, the maximum frequency refers to the highest frequency considered in the analysis of the frequency domain light electrical signal, and the minimum frequency refers to the lowest frequency considered in the analysis of the frequency domain light electrical signal.

[0116] It should be noted that in the present application, the interference coefficient calculated by the above formula can analyze the ambient light interference on the light signal, thereby further improving the data reliability of the light signal. The influence of ambient light frequency components on the signal is measured by calculating the proportion of the power of the ambient light frequency components in a certain frequency band to the total power.

[0117] The present application removes the ambient light interference noise and motion artifact noise from the light signal to obtain a denoised light signal and improve the signal quality.

[0118] S3, calculate the signal peak value of the denoised light signal, mark the sequence heart rate value of the target user according to the signal peak value, and collect user behavior data of the target user in a time period corresponding to the sequence heart rate value, wherein the user behavior data includes mouse behavior data and computer screen operation data.

[0119] The present application calculates the signal peak value of the denoised light signal as the data basis for later analysis of heart rate values.

[0120] In detail, the calculation of the signal peak value of the denoised light signal includes:

[0121] Filtering the denoised light signal to obtain a filtered light signal;

[0122] Deriving the filtered light signal to obtain a derived light signal;

[0123] Marking the zero-crossing point of the derived light signal;

[0124] Determining the local maximum value of the zero-crossing point;

[0125] According to the local maximum value and a preset amplitude threshold, determining the signal peak value of the denoised light signal.

[0126] The filtered light signal refers to the light signal after filtering, the derived light signal refers to the signal obtained by deriving the filtered light signal, the zero-crossing point refers to the point where the derivative changes from positive to negative or from negative to positive in the derived light signal, the local maximum value refers to the maximum value in the denoised light signal near the zero-crossing point, the amplitude threshold refers to a preset amplitude limit for determining which local maximum values are considered as valid signal peak values, and the signal peak value refers to the local maximum value in the denoised light signal that exceeds the preset amplitude threshold.

[0127] Optionally, the derivation of the filtered light ray electrical signal to obtain a derivative light ray electrical signal can be through a central difference processing.

[0128] It should be explained that the sequence of heart rate values refers to a sequence of signal peak values measured continuously in a certain time period, the mouse behavior data refers to a record of all interactive behaviors of the user when using the computer mouse, and the computer screen operation data refers to a record of various operations performed by the user on the computer screen.

[0129] S4, based on the user behavior data, analyzing the mouse operation frequency and the mouse operation type of the target user, and according to the computer screen operation data, analyzing the application scenario of the target user.

[0130] It should be explained that the mouse operation frequency refers to the number of mouse operations performed by the user per unit time, which can be used to measure the activity level of the user interacting with the computer, and the mouse operation type refers to different operation categories that can be performed by the user when using the mouse, such as click operation, double-click operation, drag operation, and move operation.

[0131] According to the computer screen operation data, the application scenario of the target user can be analyzed in depth to understand the behavior of the user.

[0132] In detail, the analysis of the application scenario of the target user according to the computer screen operation data comprises

[0133] defining the potential scenario of the target user;

[0134] extracting the screen operation elements of the computer screen operation data;

[0135] analyzing the element correlation and the scenario correlation of the screen operation elements;

[0136] determining the application scenario in the potential scenario through the element correlation and the scenario correlation.

[0137] The potential scene refers to various environments and activity backgrounds that the target user can be in, including a work scene, a learning scene, an entertainment scene and the like, the picture operation element refers to specific operations and visual elements extracted from computer picture operation data, such as opened application programs and documents, mouse and keyboard operations (such as clicking, dragging and typing), the element relevance refers to the mutual relationship between different picture operation elements, the scene relevance refers to the connection between the picture operation element and the potential scene, for example, the frequency of a specific operation element appearing in a certain scene, the matching degree of an operation element combination and a specific scene and the like, and the application scene refers to the specific scene in which the user actually stays finally determined by analyzing the element relevance and the scene relevance of the picture operation element, for example, the user is currently editing a report (a work scene), and the user is playing a leisure game during a rest time (an entertainment scene).

[0138] S5, based on the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene, analyzing the scene state of the target user by using a preset state intelligent analysis algorithm, and constructing a mouse management report of the heart rate detection mouse according to the scene state.

[0139] The application analyzes the scene state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene, intelligently analyzes the scene state of the user, and provides a data basis for later mouse management.

[0140] In detail, the analysis of the scene state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene comprises:

[0141] Respectively extracting heart rate value features, operation frequency features, operation type features and application scene features of the sequence heart rate value, the mouse operation frequency, the mouse operation type and the application scene;

[0142] Defining a scene state group of the target user;

[0143] Analyzing the scene state score of the scene state group by using the state intelligent analysis algorithm through the heart rate value features, the operation frequency features, the operation type features and the application scene features;

[0144] Determining the scene state of the target user through the scene state score.

[0145] The heart rate value feature refers to a quantitative index extracted from a sequence of heart rate values, used to describe the dynamic changes and patterns of heart rate, such as average heart rate, heart rate variability, etc. The operation frequency feature refers to an index describing the frequency of mouse operation, such as the number of clicks per unit time. The operation type feature refers to an index describing the type of mouse operation, such as the proportion of each type of operation (click, move, scroll, long press). The application scenario feature is extracted from computer screen operation data, used to describe the characteristic attributes of the application environment in which the user is located. The scene state group refers to a group of predefined user states, such as the focused state: high heart rate variability, high frequency mouse operation, office scenario, relaxed state: low heart rate variability, low frequency mouse operation, web browsing scenario, nervous state: high average heart rate, high frequency click operation, game scenario, etc. The scene state score refers to a parameter indicating the likelihood of the user being in a specific scene state. The scene state refers to the current psychological or physiological state of the user determined based on the analysis results, such as focus, fatigue, etc. The state intelligent analysis algorithm refers to an algorithm for analyzing the likelihood of the target user's scene state. In detail, the state intelligent analysis algorithm can be constructed by a decision tree.

[0146] Finally, according to the scene state, the mouse management report of the heart rate detection mouse can better understand one's own behavior habits and physiological state, thereby improving work efficiency and health level. The mouse management report refers to a report containing various data and statistical analysis results of the user using the heart rate detection mouse. The mouse management report includes heart rate data analysis, mouse operation statistics, scene state analysis, work efficiency evaluation, health suggestions, etc.

[0147] Compared with the problems described in the background art, first, by ensuring the stable connection of the heart rate detection mouse with the user's hand contacting the skin, and combining the PPG sensor to emit light of a specific wavelength, the scheme can accurately capture the user's heart rate data, not only improving the convenience of measurement, but also ensuring the comfort of the user, second, by denoising the reflected light signal, the influence of environmental light interference noise and motion artifact noise is effectively reduced, the accuracy and reliability of the heart rate data are improved, this step is crucial for maintaining data quality in a variable use environment, third, by calculating the signal peak value of the denoised light signal to mark the sequence heart rate value, and combining user behavior data, the scheme can monitor the physiological state of the user when operating the mouse in real time, and provide personalized feedback to the user, in addition, analyzing the mouse operation frequency and type, and determining the application scenario according to the computer picture operation data, helps to deeply understand the user's work mode and behavior habits, so as to provide more thoughtful service for the user, finally, by using the preset state intelligent analysis algorithm, the scheme can comprehensively analyze the user's scene state according to the multi-dimensional data such as heart rate, mouse operation frequency, operation type and application scenario, and construct a mouse management report accordingly, this report can not only help the user to manage,

[0148] improve work efficiency, but also provide data support for health management, and promote the improvement of the user's life quality. Therefore, the present application can improve the user experience of the user using the intelligent mouse.

[0149] Embodiment 2:

[0150] As Figure 2 shown, it is a functional module diagram of an intelligent mouse management system integrating heart rate detection function.

[0151] The intelligent mouse management system 200 integrating heart rate detection function can be installed in an electronic device. According to the functions realized, the intelligent mouse management system integrating heart rate detection function can include a reflected light acquisition module 201, a light signal denoising module 202, a heart rate value analysis module 203, an application scenario determination module 204 and a scene state analysis module 205. The modules of the present application can also be called units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0152] In the embodiments of the present application, the functions of each module / unit are as follows:

[0153] The reflected light acquisition module 201 is used to determine the contact of the heart rate detection mouse and the hand of the target user with the skin, emit light of a specific wavelength from the PPG sensor corresponding to the heart rate detection mouse to the hand contacting the skin, and receive the reflected light of the specific wavelength by using a preset photodiode.

[0154] The light ray electrical signal denoising module 202 is configured to convert the reflected light into a light ray electrical signal, analyze ambient light interference noise and motion artifact noise in the light ray electrical signal, denoise the light ray electrical signal based on the ambient light interference noise and the motion artifact noise, and obtain a denoised light ray electrical signal.

[0155] The heart rate value analysis module 203 is configured to calculate a signal peak value of the denoised light ray electrical signal, mark a sequence heart rate value of the target user according to the signal peak value, and collect user behavior data of the target user in a time period corresponding to the sequence heart rate value, wherein the user behavior data includes mouse behavior data and computer screen operation data.

[0156] The application scenario determination module 204 is configured to analyze a mouse operation frequency and a mouse operation type of the target user based on the user behavior data, and analyze an application scenario of the target user according to the computer screen operation data.

[0157] The scene state analysis module 205 is configured to analyze a scene state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario, and construct a mouse management report of the heart rate detection mouse according to the scene state.

[0158] In detail, the modules in the intelligent mouse management system 200 with the integrated heart rate detection function in the embodiments of the present application use the same technical means as the intelligent mouse management method with the integrated heart rate detection function in the above-mentioned Figure 1 , and can produce the same technical effects, which will not be described here again.

[0159] It is apparent for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application.

Claims

1. A smart mouse management method integrating heart rate detection function, characterized in that, The method includes: The heart rate detection mouse and the target user's hand are in contact with the skin. The heart rate detection mouse's corresponding PPG sensor emits light of a specific wavelength towards the hand in contact with the skin, and a preset photodiode receives the reflected light of the specific wavelength. The reflected light is converted into a photoelectric signal, and the ambient light interference noise and motion artifact noise in the photoelectric signal are analyzed. Based on the ambient light interference noise and motion artifact noise, the photoelectric signal is denoised to obtain a denoised photoelectric signal. Calculate the peak value of the denoised optical signal, mark the sequential heart rate value of the target user based on the peak value, and collect user behavior data of the target user for the time period corresponding to the sequential heart rate value, wherein the user behavior data includes mouse behavior data and computer screen operation data; Based on the user behavior data, the frequency and type of mouse operations of the target user are analyzed, and the application scenarios of the target user are analyzed based on the computer screen operation data. Based on the heart rate sequence, mouse operation frequency, mouse operation type, and application scenario, a preset intelligent state analysis algorithm is used to analyze the scenario state of the target user, and a mouse management report for the heart rate detection mouse is constructed according to the scenario state.

2. The intelligent mouse management method integrating heart rate detection function as described in claim 1, characterized in that, The heart rate detection mouse uses its corresponding PPG sensor to emit light of a specific wavelength towards the skin in contact with the hand, including: Analyze the dirt index of the hands in contact with the skin; Based on the dirt index, determine the area of ​​skin contacted by the hand within the heart rate detection mouse's coverage area; Analyze the overlap coefficient between the coverage area and the corresponding transparent window of the PPG sensor; When the overlap coefficient meets the preset overlap threshold, the LED corresponding to the PPG sensor is activated, and the activated LED is obtained. The activated LED emits light of a specific wavelength toward the skin of the hand.

3. The intelligent mouse management method integrating heart rate detection function as described in claim 2, characterized in that, The analysis of the dirt index of the hands in contact with the skin includes: The reflectivity coefficient and dirt type of the hand in contact with the skin were analyzed, wherein the dirt type included oil, protein and carbohydrate; Determine the dirt concentration of the type of dirt, wherein the dirt concentration includes grease concentration, protein concentration, and carbohydrate concentration; Based on the reflectivity, oil concentration, protein concentration, and carbohydrate concentration, the dirt index of the hand in contact with the skin is calculated using the following formula: in, This indicates the level of dirtiness of the hands in contact with the skin. Represents the reflectivity coefficient. The weights representing the reflectivity coefficient, Indicates the concentration of oil and fat. The weight representing the concentration of fats and oils. Indicates protein concentration. Weights representing protein concentration Indicates carbohydrate concentration. The weighting of carbohydrate concentration.

4. The intelligent mouse management method integrating heart rate detection function as described in claim 3, characterized in that, Converting the reflected light into an electrical signal includes: Collect the photon energy generated when the reflected light is reflected into the photodiode corresponding to the reflected light; Based on the photon energy, electron-hole pairs are constructed in the semiconductor material of the photodiode; The electron-hole pairs are separated by the built-in electric field in the photodiode to obtain a separation current; The external circuitry of the photodiode converts the separated current into an initial photoelectric signal. The initial optical signal is amplified to obtain the optical signal.

5. The intelligent mouse management method integrating heart rate detection function as described in claim 4, characterized in that, The analysis of ambient light interference noise and motion artifact noise in the optical signal includes: Baseline drift is removed from the optical signal to obtain a drift-free optical signal; The drift-de-slip optical signal is subjected to a fast Fourier transform to obtain a frequency domain optical signal. Identify the ambient light frequency components of the frequency domain photoelectric signal; Analyze the interference coefficient of the ambient light frequency components on the frequency domain photoelectric signal; Based on the interference coefficient, determine the ambient light interference noise in the ambient light frequency components; Analyze the signal changes and time-domain characteristics of the optical signal; The motion artifact noise of the photoelectric signal is analyzed by considering the signal changes and time-domain characteristics.

6. The intelligent mouse management method integrating heart rate detection function as described in claim 5, characterized in that, The analysis of the interference coefficient of the ambient light frequency components on the frequency domain photoelectric signal includes: Analyze the frequency and bandwidth of the ambient light frequency components; Based on the frequency and bandwidth, the interference coefficient of the ambient light frequency component on the frequency domain photoelectric signal is calculated using the following formula: in, Indicates the frequency components of ambient light The interference coefficient of the frequency domain photoelectric signal, This represents the frequency components of the frequency domain photoelectric signal in ambient light. Output power Indicates the frequency components of ambient light frequency, Indicates the frequency components of ambient light bandwidth, This represents the maximum frequency of the optical signal in the frequency domain. This represents the minimum frequency of a frequency domain optical signal.

7. The intelligent mouse management method integrating heart rate detection function as described in claim 6, characterized in that, Calculating the signal peak value of the denoised photoelectric signal includes: The denoised photoelectric signal is filtered to obtain the filtered photoelectric signal; Differentiate the filtered optical signal to obtain the differentiated optical signal; Mark the zero-crossing points of the derivative ray electrical signal; Determine the local maximum value of the zero-crossing point; The signal peak value of the denoised photoelectric signal is determined based on the local maximum value and the preset amplitude threshold.

8. The intelligent mouse management method integrating heart rate detection function as described in claim 7, characterized in that, Based on the computer screen operation data, analyze the target user's application scenario, including... Define the potential scenarios for the target user; Extract the screen operation elements from the computer screen operation data; Analyze the element-relatedness and scene-relatedness of the aforementioned screen operation elements; By using the element correlation and scene correlation, the application scenarios in the potential scenarios are determined.

9. The intelligent mouse management method integrating heart rate detection function as described in claim 8, characterized in that, Based on the heart rate sequence, mouse operation frequency, mouse operation type, and application scenario, a preset intelligent state analysis algorithm is used to analyze the scenario state of the target user, including: Extract the heart rate value of the sequence, the mouse operation frequency, the mouse operation type, and the heart rate value feature, operation frequency feature, operation type feature, and application scenario feature of the application scenario, respectively. Define the scenario state group for the target user; The scene state score of the scene state group is analyzed using the state intelligent analysis algorithm based on the heart rate value characteristics, operation frequency characteristics, operation type characteristics, and application scenario characteristics. The scene state of the target user is determined by the scene state score.

10. An intelligent mouse management system integrating heart rate detection function, characterized in that, The system includes: The reflected light acquisition module is used to determine the heart rate detection mouse and the target user's hand contacting the skin. The heart rate detection mouse uses its corresponding PPG sensor to emit light of a specific wavelength towards the hand contacting the skin, and a preset photodiode receives the reflected light of the specific wavelength light. The optical fiber signal denoising module is used to convert the reflected light into an optical fiber signal, analyze the ambient light interference noise and motion artifact noise in the optical fiber signal, and denoise the optical fiber signal based on the ambient light interference noise and motion artifact noise to obtain a denoised optical fiber signal. The heart rate analysis module is used to calculate the signal peak value of the denoised photoelectric signal, mark the sequential heart rate value of the target user based on the signal peak value, and collect user behavior data of the target user in the time period corresponding to the sequential heart rate value. The user behavior data includes mouse behavior data and computer screen operation data. The application scenario determination module is used to analyze the mouse operation frequency and mouse operation type of the target user based on the user behavior data, and to analyze the application scenario of the target user based on the computer screen operation data. The scenario state analysis module is used to analyze the scenario state of the target user based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario using a preset intelligent state analysis algorithm, and to construct a mouse management report for the heart rate detection mouse based on the scenario state.

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