Method and system for protecting health of user through intelligent self-adaptive keyboard

Abstract

The invention relates to the field of man-machine interaction, and discloses an intelligent self-adaptive keyboard user health protection method and system, and the method comprises the steps: obtaining a pressure distribution map of a target user during the input of a self-adaptive keyboard, analyzing the joint accumulated stress, constructing a joint stress accumulation curve, extracting a fatigue response parameter, calculating the keyboard layout adaptation degree, and obtaining the fatigue response parameter. The method comprises the following steps of: simulating a muscle-bone injury accumulated value in a long-term input scene according to a layout adaptation degree, constructing a key travel adjustment data cluster, then integrating the data cluster and a preset rhythm feedback mechanism, generating a rhythm optimization target, calculating a collaborative vector value, in addition, identifying a local fluctuation state of a key area based on a health loss index, extracting a key fluctuation factor, and finally obtaining a key travel adjustment result. And analyzing and correcting the priority item, and finally generating an adaptive keyboard health protection scheme exclusive to the target user. The health risk caused by long-term use of the keyboard can be reduced.

Description

Technical Field

[0001] The present invention relates to a method and system for an intelligent adaptive keyboard to protect user health, belonging to the field of human-computer interaction. Background Art

[0002] With the rapid development of computer technology, the keyboard, as the core input device for human-computer interaction, is widely used in multiple fields such as office work, entertainment, and programming. However, long-term use of the keyboard may cause health problems for users, such as wrist fatigue, finger joint injuries, and cervical discomfort. Research shows that incorrect typing postures, excessive key pressure, and long-term maintenance of static postures are the main causes of these health problems.

[0003] Currently, the designs of keyboards on the market mainly focus on mechanical performance, response speed, and appearance design, while relatively less consideration is given to health protection functions. Although some keyboard products attempt to relieve fatigue through ergonomic designs (such as split keyboards, wrist rests, etc.), traditional ergonomic keyboards adopt fixed structures and cannot be dynamically adjusted according to the user's hand shape, typing habits, or fatigue status. Moreover, most existing keyboards do not have functions such as real-time monitoring of the user's typing posture, key pressing force, or usage duration, and cannot provide targeted health reminders or interventions. Therefore, an intelligent adaptive keyboard protection method is needed that can real-time monitor the user's operation status, dynamically adjust keyboard parameters, and provide personalized health interventions to reduce the health risks brought by long-term use of the keyboard. Summary of the Invention

[0004] The present invention provides a method and system for an intelligent adaptive keyboard to protect user health, and its main purpose is to reduce the health risks brought by long-term use of the keyboard.

[0005] To achieve the above object, an intelligent adaptive keyboard protection method for user health provided by the present invention includes:

[0006] Obtain the pressure distribution map of the target user during input on the adaptive keyboard. Based on the pressure distribution map, analyze the joint cumulative stress corresponding to the target user's use of the adaptive keyboard. According to the joint cumulative stress, construct the joint stress accumulation curve corresponding to the target user;

[0007] Extract the fatigue response parameters from the joint stress accumulation curve, analyze the peak load threshold and continuous risk index in the fatigue response parameters. Based on the peak load threshold and the continuous risk index, calculate the layout adaptability corresponding to the adaptive keyboard;

[0008] Based on the layout adaptability, simulate the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario. According to the cumulative value of musculoskeletal injuries, construct the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard;

[0009] Integrate the key travel adjustment data clusters with a preset rhythm feedback mechanism to generate a rhythm optimization target corresponding to the adaptive keyboard, extract cooperative optimization parameters corresponding to the rhythm optimization target, and calculate a cooperative vector value corresponding to the cooperative optimization parameters;

[0010] Based on the health loss index, identify the local fluctuation state of the key area in the adaptive keyboard, extract the key fluctuation factors in the local fluctuation state, analyze the correction priority items corresponding to the key fluctuation factors, and generate a health protection plan for the target user for the adaptive keyboard based on the correction priority items.

[0011] Optionally, constructing the joint stress accumulation curve corresponding to the target user according to the joint cumulative stress includes:

[0012] Extract the stress accumulation data in the joint cumulative stress;

[0013] Generate a stress change sequence corresponding to the stress accumulation data;

[0014] Perform a segmentation process on the stress change sequence to obtain a stress fluctuation interval;

[0015] Identify the stress accumulation segments in the stress fluctuation interval;

[0016] Construct the joint stress accumulation curve corresponding to the target user based on the stress accumulation segments.

[0017] Optionally, extracting the fatigue response parameters in the joint stress accumulation curve includes:

[0018] Collect the curve load parameters in the joint stress accumulation curve;

[0019] Query the stress amplitude and the number of cycles in the curve load parameters;

[0020] Fit a fatigue evolution matrix corresponding to the stress amplitude and the number of cycles;

[0021] Calibrate the fatigue response points in the fatigue evolution matrix;

[0022] Extract the fatigue response parameters in the joint stress accumulation curve according to the fatigue response points.

[0023] Optionally, calculating the layout adaptability corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index includes:

[0024] Calculate the layout adaptability corresponding to the adaptive keyboard using the following formula:

[0025]

[0026] Among them, Bd represents the layout adaptation degree corresponding to the adaptive keyboard, t represents the time when the user uses the keyboard, n represents the total number of usage scenarios corresponding to the adaptive keyboard, i represents the number index corresponding to the usage scenario, and P i represents the peak load threshold under the i-th usage scenario, and R i represents the continuous risk index under the i-th usage scenario, and T i represents the cumulative duration of the i-th usage scenario within the time t, and S represents the normalization constant.

[0027] Optionally, simulating the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario based on the layout adaptation degree includes:

[0028] Analyzing the posture adaptation parameters corresponding to the layout adaptation degree;

[0029] Based on the posture adaptation parameters, analyzing the joint load intensity of the target user under different input actions;

[0030] Based on the joint load intensity, quantifying the joint fatigue index of the target user during continuous input;

[0031] Based on the joint fatigue index, constructing the musculoskeletal fatigue framework corresponding to the target user;

[0032] Based on the musculoskeletal fatigue framework, simulating the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario.

[0033] Optionally, constructing the key travel adjustment data cluster corresponding to the keys in the adaptive keyboard according to the cumulative value of musculoskeletal injuries includes:

[0034] Analyzing the key injury areas corresponding to the cumulative value of musculoskeletal injuries;

[0035] Extracting the touch pressure peak points in the key injury areas;

[0036] Based on the touch pressure peak points, calculating the key travel attenuation index corresponding to the keys in the adaptive keyboard;

[0037] Based on the key travel attenuation index, querying the key travel damping parameters corresponding to the adaptive keyboard;

[0038] Based on the key travel damping parameters, constructing the key travel adjustment data cluster corresponding to the keys in the adaptive keyboard.

[0039] Optionally, integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism to generate the rhythm optimization target corresponding to the adaptive keyboard includes:

[0040] Extract the pressing distribution parameters in the key travel adjustment data cluster;

[0041] Query the timing characteristics of the pressing distribution parameters during user input;

[0042] Match the timing characteristics with a preset rhythm feedback mechanism to obtain a phase matching threshold;

[0043] Fit the force feedback curve corresponding to the adaptive keyboard based on the phase matching threshold;

[0044] Generate a rhythm optimization target corresponding to the adaptive keyboard based on the force feedback curve.

[0045] Optionally, calculating the collaborative vector value corresponding to the collaborative optimization parameter includes:

[0046] Calculate the collaborative vector value corresponding to the collaborative optimization parameter using the following formula:

[0047]

[0048] where CV represents the collaborative vector value corresponding to the collaborative optimization parameter, M represents the number of parameters of the collaborative optimization parameter, j represents the parameter index of the collaborative optimization parameter, K i represents the parameter influence factor corresponding to the jth collaborative optimization parameter, D j represents the damping coefficient of the jth collaborative optimization parameter, F j represents the actual value of the parameter corresponding to the jth collaborative optimization parameter.

[0049] Optionally, based on the health loss index, identifying the local fluctuation state of the key area in the adaptive keyboard includes:

[0050] Extract the dynamic decay characteristics in the health loss index;

[0051] Based on the dynamic decay characteristics, divide the abnormal key areas in the adaptive keyboard;

[0052] Detect the pressure change value within the abnormal key area;

[0053] Calculate the regional fluctuation entropy corresponding to the pressure change value;

[0054] Identify the local fluctuation state of the key area in the adaptive keyboard according to the regional fluctuation entropy.

[0055] To solve the above problems, the present invention also provides an intelligent adaptive keyboard health protection system, and the system includes:

[0056] The curve construction module obtains the pressure distribution map of the target user during adaptive keyboard input, analyzes the joint cumulative stress corresponding to the adaptive keyboard used by the target user based on the pressure distribution map, and constructs the joint stress accumulation curve corresponding to the target user according to the joint cumulative stress;

[0057] The adaptability calculation module is used to extract the fatigue response parameters in the joint stress accumulation curve, analyze the peak load threshold and the continuous risk index in the fatigue response parameters, and calculate the layout adaptability corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index;

[0058] The data cluster module is used to simulate the cumulative value of musculoskeletal injury of the target user in the long-term input scenario based on the layout adaptability, and construct the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injury;

[0059] The vector value calculation module is used to integrate the key travel adjustment data cluster and the preset rhythm feedback mechanism, generate the rhythm optimization target corresponding to the adaptive keyboard, extract the collaborative optimization parameters corresponding to the rhythm optimization target, and calculate the collaborative vector value corresponding to the collaborative optimization parameters;

[0060] The solution generation module is used to identify the local fluctuation state of the key area on the adaptive keyboard based on the health loss index, extract the key fluctuation factors in the local fluctuation state, analyze the correction priority items corresponding to the key fluctuation factors, and generate the health protection solution for the target user for the adaptive keyboard based on the correction priority items.

[0061] Compared with the problems described in the background art, the present invention can analyze the joint cumulative stress when the user uses the keyboard by obtaining the pressure distribution map during the adaptive keyboard input of the target user, can identify the local fluctuation state of the keyboard key area, formulate a targeted health protection plan, and effectively reduce the health risks brought by the user's long-term use of the keyboard. By extracting the fatigue response parameters in the joint stress accumulation curve, the present invention can accurately calculate the keyboard layout adaptability, evaluate the impact of the existing keyboard layout on the user's health, and can optimize the keyboard key travel and formulate a health protection plan accordingly to effectively prevent health problems caused by long-term use of the keyboard. Further, based on the layout adaptability, the present invention simulates the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario, can intuitively show the long-term impact of different keyboard layouts on the user's musculoskeletal system, and can accordingly optimize the keyboard design or provide reasonable usage suggestions for the user to effectively reduce the risk of musculoskeletal injuries caused by long-term use of the keyboard. Further, by integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism, the present invention generates a rhythm optimization target corresponding to the adaptive keyboard, can significantly improve the adaptive keyboard usage experience, and through key travel adjustment, makes the key operation conform to human mechanics and reduces the musculoskeletal burden. Finally, based on the health loss index, the present invention identifies the local fluctuation state of the key area in the adaptive keyboard, can timely discover which key areas cause excessive health loss to the user due to high-frequency use or unreasonable design, thereby significantly improving the comfort and sustainability of keyboard use. Therefore, a method and system for protecting the health of users by an intelligent adaptive keyboard provided by the embodiments of the present invention can reduce the health risks brought by long-term use of the keyboard. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. is a schematic flow chart of a method for protecting the health of users by an intelligent adaptive keyboard provided by an embodiment of the present invention;

[0063] Figure 2 FIG. is a schematic module diagram of a system for realizing the method for protecting the health of users by an intelligent adaptive keyboard provided by an embodiment of the present invention.

[0064] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] The embodiments of the present application provide a method for an intelligent adaptive keyboard to protect user health. The execution subject of the method for an intelligent adaptive keyboard to protect user health includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for an intelligent adaptive keyboard to protect user health can be executed by software or hardware installed on 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.

[0067] Embodiment 1:

[0068] Referring to Figure 1 As shown, it is a schematic flowchart of a method for an intelligent adaptive keyboard to protect user health provided by an embodiment of the present invention. In this embodiment, the method for an intelligent adaptive keyboard to protect user health includes:

[0069] S1. Obtain the pressure distribution map of the target user during adaptive keyboard input. Based on the pressure distribution map, analyze the joint cumulative stress corresponding to the target user's use of the adaptive keyboard. According to the joint cumulative stress, construct the joint stress accumulation curve corresponding to the target user.

[0070] By obtaining the pressure distribution map of the target user during adaptive keyboard input, the present invention can analyze the joint cumulative stress of the user when using the keyboard, can identify the local fluctuation state of the keyboard key area, formulate a targeted health protection plan, and effectively reduce the health risks brought by the user's long-term use of the keyboard.

[0071] Among them, the target user refers to an individual who uses an adaptive keyboard for input operations, and the system monitors his operating status and formulates a personalized health intervention plan for him. Different users have different hand shapes, typing habits, and fatigue states. The system conducts targeted analysis around each specific user who uses the keyboard; the adaptive keyboard refers to a keyboard that is different from a traditional fixed structure keyboard. It can monitor the user's typing posture, key strength, usage time and other operating states in real time, and dynamically adjust keyboard parameters, such as key travel, according to the acquired data; it can also provide users with personalized health intervention through a preset rhythm feedback mechanism, and reduce the health risks caused by long-term use. The smart keyboard; the pressure distribution map refers to a map that records the pressure size and distribution of the target user's fingers on each key when typing on the adaptive keyboard. It is the basic data source for the system to subsequently analyze the accumulated stress of the user's joints, and then build a series of data models and generate health protection plans, which intuitively reflects the pressure characteristics of the user when typing. Optionally, the pressure distribution map of the target user when typing on the adaptive keyboard can be obtained through a multimodal sensing fusion method, such as: using a flexible PVDF piezoelectric film sensor (such as TE Connectivity's DT series), arranged in a 10×10 matrix under the keycaps, with a sampling rate of ≥200Hz, generating a three-dimensional pressure heat map by measuring dynamic pressure signals.

[0072] Furthermore, based on the pressure distribution map, the present invention analyzes the accumulated joint stress corresponding to the target user's use of the adaptive keyboard, can simulate the musculoskeletal injuries that are easily caused by long-term use of the keyboard, and can also lay a solid foundation for formulating personalized keyboard key travel adjustment strategies and health protection plans, effectively reducing the risk of health problems caused by long-term use of the keyboard for users.

[0073] Among them, the accumulated joint stress refers to the accumulated stress caused by the continuous pressure on the joints such as fingers and wrists when the user is typing on the adaptive keyboard. For example, when the keyboard is tapped vigorously for a long time, the force on the finger joints will accumulate on the original basis each time, which is like the leg muscles constantly bearing pressure and accumulating fatigue during long-distance running. Optionally, the analysis of the accumulated joint stress corresponding to the target user's use of the adaptive keyboard can be achieved through a machine learning algorithm, such as a neural network or other algorithm, which first trains a large number of pressure distribution maps and corresponding joint stress data, and then inputs the target user's pressure distribution map into the trained model to output the accumulated joint stress.

[0074] Furthermore, the present invention constructs a joint stress accumulation curve corresponding to the target user based on the joint accumulated stress, which can intuitively present the changing trend of the user's joint stress over time during the user's use of the keyboard, and can extract key fatigue response parameters such as peak load threshold and continuous risk index, thereby providing data support for calculating the adaptability of the keyboard layout and simulating the cumulative value of musculoskeletal injuries.

[0075] Among them, the joint stress accumulation curve refers to a curve plotted based on the stress accumulation section with time as the horizontal axis and the joint cumulative stress as the vertical axis. It intuitively shows the change of the joint cumulative stress over time. Through the curve trend, the process and degree of the joint force accumulation during the user's typing can be analyzed, providing a basis for health protection.

[0076] As an embodiment of the present invention, constructing the joint stress accumulation curve corresponding to the target user according to the joint cumulative stress includes: extracting the stress accumulation data in the joint cumulative stress; generating a stress change sequence corresponding to the stress accumulation data; performing a segmentation process on the stress change sequence to obtain a stress fluctuation interval; identifying the stress accumulation section in the stress fluctuation interval; and constructing the joint stress accumulation curve corresponding to the target user based on the stress accumulation section.

[0077] Among them, the stress accumulation data refers to the specific numerical set of the joint cumulative stress within a specific time period during the use of the adaptive keyboard. For example, during a 10-minute typing process, the finger joint cumulative stress is recorded every 10 seconds, and these data constitute the stress accumulation data, which can reflect the force accumulation of the joint at different moments; the stress change sequence refers to arranging the stress accumulation data in chronological order to form a sequence reflecting the change of stress over time. For example, the joint cumulative stress data at 5 moments are recorded: 10MPa, 12MPa, 11MPa, 13MPa, 14MPa. This set of data arranged in time is the stress change sequence, clearly showing the dynamic change of stress; the stress fluctuation interval refers to the range interval of the stress rising and falling obtained by performing a segmentation process on the stress change sequence. For example, within a specific time, the stress fluctuates between 10 - 15MPa, and this 10 - 15MPa is a stress fluctuation interval, helping to analyze the law and amplitude of the stress change; the stress accumulation section refers to the part where the stress shows a continuous increase or remains at a relatively high level in the stress fluctuation interval. For example, in the 10 - 15MPa fluctuation interval, there are 5 data points in sequence: 12MPa, 13MPa, 14MPa, 14.5MPa, 15MPa, and this part of the data constitutes the stress accumulation section, reflecting the cumulative trend of the joint force.

[0078] Further, the stress accumulation data in the joint cumulative stress can be obtained through a sensor data processing algorithm. For example, the original stress sensor data is denoised and aggregated through Kalman filtering or moving average method to finally obtain the stress accumulation data. The stress change sequence corresponding to the stress accumulation data can be generated through a time series analysis tool. For example, using the time series data processing function of Pandas or the array transformation method of NumPy, the cumulative data is sorted by timestamp to generate the stress change sequence. The segmentation of the stress change sequence can be achieved through a change point detection algorithm. For example, based on Bayesian Change Point Detection or sliding window variance analysis method, the sequence is divided into stress fluctuation intervals with different statistical characteristics. The identification of the stress accumulation segments in the stress fluctuation intervals can be realized through a pattern recognition algorithm. For example, applying Dynamic Time Warping (DTW) to match a preset accumulation pattern, or screening the intervals with a continuously positive slope through a threshold method to finally obtain the stress accumulation segments. The construction of the joint stress accumulation curve corresponding to the target user can be achieved through a data visualization tool. For example, using the plotting function of Matplotlib or the interactive chart tool of Plotly, the cumulative segment data is fitted into a continuous curve to finally obtain the joint stress accumulation curve.

[0079] S2. Extract the fatigue response parameters from the joint stress accumulation curve, analyze the peak load threshold and continuous risk index in the fatigue response parameters, and calculate the layout adaptability of the adaptive keyboard based on the peak load threshold and the continuous risk index.

[0080] By extracting the fatigue response parameters from the joint stress accumulation curve, the present invention can accurately calculate the keyboard layout adaptability, evaluate the impact of the existing keyboard layout on user health, optimize the keyboard key travel and formulate a health protection plan accordingly, effectively preventing health problems caused by long-term use of the keyboard.

[0081] Among them, the fatigue response parameters refer to the relevant data indicators extracted from the joint stress accumulation curve that can reflect the joint fatigue state of the user during keyboard use. For example, it may include the fatigue damage degree value corresponding to a specific fatigue response point, which is calculated according to the relationship between the stress amplitude and the number of cycles in the fatigue evolution matrix to measure the fatigue damage suffered by the joint under the current stress conditions.

[0082] As an embodiment of the present invention, extracting the fatigue response parameters from the joint stress accumulation curve includes: collecting the curve load parameters in the joint stress accumulation curve; querying the stress amplitude and the number of cycles in the curve load parameters; fitting the fatigue evolution matrix corresponding to the stress amplitude and the number of cycles; calibrating the fatigue response points in the fatigue evolution matrix; and extracting the fatigue response parameters from the joint stress accumulation curve according to the fatigue response points.

[0083] Among them, the curve load parameter refers to the data used to describe the stress change characteristics on the joint stress accumulation curve, including information such as stress magnitude and change time. For example, the stress data and time stamps of the curve at different time periods can help locate the details of the stress change over time and provide a basis for analyzing the joint force condition; the stress amplitude refers to half of the difference between the maximum stress and the minimum stress during the periodic change of the joint stress. Taking the common periodic keyboard tapping as an example, if the joint stress rises from 5 MPa to 15 MPa during one tap, the stress amplitude is (15 - 5) ÷ 2 = 5 MPa, which reflects the severity of the stress fluctuation; the number of cycles refers to the number of times the joint stress completes a periodic change within a certain time. For example, within 1 minute, when the user continuously types, the finger joint stress changes repeatedly according to a certain rule, and the complete number of changes is the number of cycles, which reflects the frequency of keyboard use; the fatigue evolution matrix refers to a mathematical model obtained by fitting the stress amplitude and the number of cycles, used to describe the fatigue development process of materials under different stress conditions. For example, with the stress amplitude as the horizontal axis and the number of cycles as the vertical axis, fitting multiple sets of corresponding data to form a matrix can intuitively present the influence of the stress amplitude and the number of cycles on fatigue development; the fatigue response point refers to the data point calibrated in the fatigue evolution matrix, representing a specific fatigue state. These points are determined based on the stress amplitude and the number of cycles. Once extracted, the corresponding fatigue response parameters can be determined accordingly, helping to evaluate the fatigue degree of the user during keyboard use.

[0084] Furthermore, the curve load parameters in the collected joint stress accumulation curve can be realized through signal processing algorithms. For example, the Fourier transform (FFT) is used to extract frequency domain features, or the peak detection algorithm (such as the local extreme value method) is adopted to identify the load fluctuation extreme values, and finally the curve load parameters are obtained. The stress amplitude and cycle number in the query of the curve load parameters can be realized through statistical analysis tools. For example, the rain flow counting is carried out by using the SciPy library of Python or the window statistical method based on Pandas, and finally the stress amplitude and cycle number are obtained. The fatigue evolution matrix corresponding to the fitting of the stress amplitude and cycle number can be realized through machine learning algorithms. For example, the stress-life (S-N) relationship model is established by using linear regression or support vector machine (SVM), and finally the fatigue evolution matrix is obtained. The fatigue response points in the calibration of the fatigue evolution matrix can be realized through optimization algorithms. For example, the least squares method is used to fit the experimental data, or the genetic algorithm (GA) is used to optimize the key parameters, and finally the fatigue response points are obtained. The fatigue response parameters in the extraction of the joint stress accumulation curve can be realized through feature engineering methods. For example, the principal component analysis (PCA) is used for dimensionality reduction to extract key features, or the wavelet transform is used to decompose the stress signal, and finally the fatigue response parameters are obtained.

[0085] By analyzing the peak load threshold and the continuous risk index in the fatigue response parameters, the present invention can accurately insight into the potential health risks of users when using the keyboard, which can help to judge whether the maximum pressure borne by the joint instantaneously exceeds the standard, and the continuous risk index reflects the pressure risk accumulated over a long time.

[0086] Among them, the peak load threshold refers to the maximum stress value that the joint can bear during the use of the keyboard without immediately causing damage. For example, through a large number of experiments and data statistics, it is obtained that when typing frequently, the maximum stress that the finger joint can bear is 50N, and this 50N is the peak load threshold. When the stress borne by the joint during typing reaches or exceeds this value, there is an immediate damage risk, and analyzing it can effectively prevent acute joint injuries. The continuous risk index refers to that, optionally, the analysis of the peak load threshold and the continuous risk index in the fatigue response parameters can be realized through statistical modeling and risk assessment algorithms. For example, the extreme value theory is used to calculate the peak load threshold, and the fatigue life data is fitted based on the Weibull distribution to evaluate the continuous risk index.

[0087] Based on the peak load threshold and the continuous risk index, the present invention calculates the layout adaptability corresponding to the adaptive keyboard, can clearly understand whether the current keyboard layout will cause the joint to bear too high a load or cause long-term cumulative pressure, optimizes and adjusts the keyboard layout, and effectively reduces the health risks caused by unreasonable keyboard layout.

[0088] Among them, the layout adaptability is a quantitative index used to measure the degree of fit between the layout of the adaptive keyboard and the actual usage needs and physical function characteristics of the user. It comprehensively considers factors such as the peak load threshold and the continuous risk index, and reflects the friendliness of the keyboard layout to the user's joint pressure, fatigue degree, etc. during use.

[0089] As an embodiment of the present invention, calculating the layout adaptability corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index includes:

[0090] Calculating the layout adaptability corresponding to the adaptive keyboard using the following formula:

[0091]

[0092] Among them, Bd represents the layout adaptability corresponding to the adaptive keyboard, t represents the time when the user uses the keyboard, n represents the total number of usage scenarios corresponding to the adaptive keyboard, i represents the number index corresponding to the usage scenario, P i represents the peak load threshold in the i-th usage scenario, R i represents the continuous risk index in the i-th usage scenario, T i represents the cumulative duration of the i-th usage scenario within the time t, and S represents the normalization constant.

[0093] Specifically, the usage scenario refers to the specific situation or operation type when the user uses the keyboard. For example, in the daily text input scenario, it may be writing a document, editing an email, etc. At this time, the user mainly performs continuous input operations of letters, numbers, and symbols; in the game operation scenario, the user frequently uses specific key combinations to control the actions of the game character, such as arrow keys, skill shortcut keys, etc.; there is also the programming scenario, where the user inputs a large amount of code, involving the input of various special characters and code structures; the cumulative duration refers to the total duration of the user continuously using the keyboard for related operations within a period of time in a specific usage scenario. For example, during a day's working hours, the user is in the daily text input scenario for document writing, from 9 am to 11 am and from 2 pm to 5 pm. Then the cumulative duration in this usage scenario on that day is 6 hours; the normalization constant is a fixed value used for data normalization processing. By introducing the normalization constant, the calculated layout adaptability value is adjusted to a relatively fixed and unified value range, such as 0 - 100 or 0 - 1.

[0094] Furthermore, This part first performs a summation operation on different usage scenarios, where P i is the peak load threshold in the i-th usage scenario, representing the maximum stress that the joint can withstand in this scenario, Ri is the continuous risk index in the i-th usage scenario, which combines stress and time factors and is divided by T i because of the duration T i The longer it is, the risk will increase non-linearly. Taking the square root is a non-linear adjustment method for the influence of duration, making the increase in risk relatively mild when the duration is longer. Combining these three operations is to comprehensively measure the pressure and risk situation borne by the joints in each usage scenario; S is a standardization constant used to normalize the value obtained from the numerator calculation. Since the numerical range of the numerator calculation result can vary greatly due to different keyboards, different user usage habits, etc., it is not convenient to directly compare and judge the adaptability level. By dividing by the standardization constant S, the final layout adaptability Bd is adjusted to a relatively fixed, easy-to-understand and comparable numerical range, which is convenient for evaluating the keyboard layout adaptability degree.

[0095] S3. Based on the layout adaptability, simulate the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario, and construct a key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injuries.

[0096] Based on the layout adaptability, the present invention simulates the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario, which can intuitively show the long-term impact of different keyboard layouts on the user's musculoskeletal system. Accordingly, the keyboard design can be optimized specifically, or reasonable usage suggestions can be provided for the user, effectively reducing the risk of musculoskeletal injuries caused by long-term use of the keyboard.

[0097] Among them, the cumulative value of musculoskeletal injuries refers to a value obtained by simulating based on the musculoskeletal fatigue framework, which quantifies the cumulative damage degree of muscles and bones of the target user in the long-term input scenario. It considers the fatigue and pressure borne by the musculoskeletal system when the user repeatedly performs keyboard input actions for a long time, and is a key indicator comprehensively reflecting the long-term health risk.

[0098] As an embodiment of the present invention, the simulating the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario based on the layout adaptability includes: analyzing the posture adaptability parameters corresponding to the layout adaptability; based on the posture adaptability parameters, analyzing the joint load intensity of the target user in different input actions; based on the joint load intensity, quantifying the joint fatigue index of the target user during continuous input; based on the joint fatigue index, constructing the musculoskeletal fatigue framework corresponding to the target user; based on the musculoskeletal fatigue framework, simulating the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario.

[0099] Among them, the posture adaptation parameters refer to a set of quantitative indicators used to measure the degree of match between the user's body posture and the keyboard layout when using the keyboard. For example, when the arm is naturally hanging, the elbow angle is about 90°, and the wrist is in a naturally stretched state, the corresponding data are the ideal values of the posture adaptation parameters; the joint load intensity refers to the pressure on the user's fingers, wrists, elbows and other joints during various input actions. Taking the finger joints as an example, during continuous high-speed input, the frequency and strength of force on the finger joints per unit time constitute the key data of joint load intensity; the joint fatigue index refers to The comprehensive calculation of joint load intensity is a quantitative measure of joint fatigue during continuous input. For example, if a user continuously inputs at a high-intensity load for 1 hour, the joint fatigue index of the former will be higher than that of a user who inputs at the same intensity for only 10 minutes. The musculoskeletal fatigue framework refers to a systematic architecture built based on the joint fatigue index. It is used to comprehensively describe the fatigue status of the muscles and bones related to typing actions of the target user throughout the body during the process of using the keyboard. It integrates the fatigue data of different joints, comprehensively analyzes the fatigue status of fingers, wrists, arms and other parts, and draws an overall picture of musculoskeletal fatigue.

[0100] Furthermore, the analysis of posture adaptation parameters corresponding to the layout adaptability can be implemented through a motion capture analysis system, such as: extracting human posture angle data through Kinect SDK, and finally obtaining posture adaptation parameters; the analysis of the joint load intensity of the target user under different input actions can be implemented through biomechanical simulation software, such as: using AnyBody Modeling System to perform inverse dynamics calculations, and finally obtaining joint load intensity; the quantification of the joint fatigue index of the target user during continuous input can be implemented through a fatigue accumulation algorithm, such as: calculating the fatigue coefficient based on the muscle activation degree of the electromyographic signal (EMG), and finally obtaining the joint fatigue index; the construction of the musculoskeletal fatigue framework corresponding to the target user can be implemented through a multimodal data fusion method, such as: combining kinematic data, surface electromyography and pressure distribution information, using TensorFlow to build a deep learning prediction model, and finally obtaining a musculoskeletal fatigue framework; the simulation of the target user's musculoskeletal injury cumulative value in a long-term input scenario can be implemented through an injury accumulation model, such as: simulating the soft tissue stress concentration effect through finite element analysis (FEA), and finally obtaining the musculoskeletal injury cumulative value.

[0101] The present invention constructs a key travel adjustment data cluster corresponding to the keys in the adaptive keyboard according to the accumulated value of musculoskeletal injury, which can effectively reduce the damage suffered by the user in the process of using the keyboard. By analyzing the accumulated value, the influence of different keys on musculoskeletal injury when used is clarified, and the key travel is adjusted in a targeted manner, so that the typing action is easier and more natural, and the joint load is reduced.

[0102] Among them, the key travel adjustment data cluster refers to the data set constructed by comprehensively considering factors such as key travel damping parameters. It contains the specific data of the key travel that needs to be adjusted for each key in the adaptive keyboard, which clarifies the key travel length that each key should be adjusted to, in order to reduce the musculoskeletal injuries of users and improve the usage experience. It is the core data for realizing the adaptive adjustment of the keyboard key travel.

[0103] As an embodiment of the present invention, constructing the key travel adjustment data cluster corresponding to the keys in the adaptive keyboard according to the cumulative value of musculoskeletal injuries includes: analyzing the key injury areas corresponding to the cumulative value of musculoskeletal injuries; extracting the touch pressure peak points in the key injury areas; calculating the key travel attenuation index corresponding to the keys in the adaptive keyboard based on the touch pressure peak points; querying the key travel damping parameters corresponding to the adaptive keyboard based on the key travel attenuation index; and constructing the key travel adjustment data cluster corresponding to the keys in the adaptive keyboard based on the key travel damping parameters.

[0104] Among them, the key injury area refers to the body part with a relatively high degree of cumulative musculoskeletal injury during the use of the keyboard determined according to the cumulative value of musculoskeletal injuries. For example, when the cumulative value of musculoskeletal injuries shows that some areas of the user's hand are severely damaged, these areas may be the key injury areas, such as the inner side of the wrist and finger joints. The touch pressure peak point refers to the point where the pressure generated when the user's finger contacts the keyboard key reaches the maximum value within the key injury area. During the typing process, the force applied by the finger to press the key is not uniform and constant, and there will be an instant when the force is relatively large. The pressure value corresponding to this instant is the touch pressure peak. The key travel attenuation index refers to the degree calculated based on the touch pressure peak point to measure the adjustment required for the key travel of the keys in the adaptive keyboard. If the pressure at the touch pressure peak point is too large, it means that the current key travel will cause the user to press the key with excessive force. At this time, the key travel attenuation index will increase accordingly, indicating that the key travel needs to be reduced to a greater extent to reduce the pressure when the user presses the key. The key travel damping parameter refers to the resistance situation that the key experiences during the pressing and rebounding processes, which is closely related to the key travel. Different key travels require appropriate damping to ensure the smoothness and comfort of key operations.

[0105] Furthermore, the key injury areas corresponding to the cumulative musculoskeletal injury values can be determined through biomechanical modeling methods. For example, finite element analysis (FEA) can be used to simulate stress distribution to finally obtain the key injury areas. The extraction of the touch pressure peak points in the key injury areas can be achieved through a pressure distribution analysis algorithm. For example, a peak detection algorithm can be applied to identify the local maximum values in the pressure sensor data to finally obtain the touch pressure peak points. The calculation of the key travel attenuation index corresponding to the keys on the adaptive keyboard can be realized through mechanical performance testing methods. For example, a high-precision displacement sensor can be used to measure the change in key travel to finally obtain the key travel attenuation index. The query of the key travel damping parameters corresponding to the adaptive keyboard can be achieved through a hardware characteristic database. For example, the preset damping value can be retrieved from the keyboard specification parameter table to finally obtain the key travel damping parameters. The construction of the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard can be realized through ergonomic optimization methods. For example, based on the user operation habit data, K-means clustering can be used to generate a personalized adjustment plan to finally obtain the key travel adjustment data cluster.

[0106] S4. Integrate the key travel adjustment data cluster with a preset rhythm feedback mechanism to generate a rhythm optimization target corresponding to the adaptive keyboard, extract the collaborative optimization parameters corresponding to the rhythm optimization target, and calculate the collaborative vector value corresponding to the collaborative optimization parameters.

[0107] By integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism, the present invention generates a rhythm optimization target corresponding to the adaptive keyboard, which can significantly improve the usage experience of the adaptive keyboard. And through key travel adjustment, the key operations can conform to human mechanics, reducing the musculoskeletal burden.

[0108] Among them, the preset rhythm feedback mechanism refers to an interaction strategy designed based on human input habits and perception characteristics. From the perspective of tactile feedback, when the user presses a key, the keyboard can give an alternating strong and weak vibration feedback according to a preset rhythm. For example, at a normal typing speed, every time a regular input combination is completed, the keyboard gives a slight vibration to help the user perceive the input rhythm. The rhythm optimization target refers to a comprehensive optimization plan formulated for the adaptive keyboard in combination with the force feedback curve, which covers the adjustment targets of multiple aspects such as the key travel, feedback rhythm, and strength of the keyboard. The aim is to make the operation experience of the keyboard more in line with the user's input habits, reduce input fatigue, and improve typing efficiency and comfort through the optimization of these parameters.

[0109] As an embodiment of the present invention, the integration of the key travel adjustment data cluster and the preset rhythm feedback mechanism to generate the rhythm optimization target corresponding to the adaptive keyboard includes: extracting the pressing distribution parameters in the key travel adjustment data cluster; querying the timing characteristics of the pressing distribution parameters during user input; matching the timing characteristics with the preset rhythm feedback mechanism to obtain a phase matching threshold; based on the phase matching threshold, fitting the force feedback curve corresponding to the adaptive keyboard; based on the force feedback curve, generating the rhythm optimization target corresponding to the adaptive keyboard.

[0110] Among them, the press distribution parameter refers to the press distribution parameter reflecting the frequency, force and position information of each key being pressed when the user operates the adaptive keyboard. For example, in the process of text input, the pressing frequency of high-frequency letter keys such as the letters "e" and "a", as well as the force applied by the user when pressing the function key, all of these data belong to the press distribution parameter; the timing feature refers to the description of the characteristics of the user's input action in the time dimension, including the sequence of keys, the time interval between pressing and releasing each key, and the overall rhythm of the input operation, such as the short interval between keys when typing quickly, or the longer pause caused by cautious operation when entering a password. These time information constitute the timing feature; the phase matching threshold refers to the phase matching threshold of the user. The quantitative index obtained after matching the input timing characteristics with the preset rhythm feedback mechanism is used to measure the degree of fit between the two. For example, the preset rhythm feedback mechanism sets a standard input rhythm cycle of every 0.2 seconds, while the timing characteristics of the user's actual input show that its average input cycle is 0.22 seconds. The value obtained by matching calculation can reflect the degree of difference between the two, which is the phase matching threshold. This threshold guides the subsequent adjustment of the keyboard feedback; the force feedback curve refers to the fitting based on the phase matching threshold, which is used to describe the changes in force feedback that the keyboard should give to the user according to the input rhythm and force during the user's key pressing process. The curve takes time as the horizontal axis and the force feedback size as the vertical axis, which determines the force feedback intensity that the keyboard needs to provide at different times.

[0111] Furthermore, the extraction of the pressing distribution parameters in the key travel adjustment data cluster can be achieved through spatial statistical analysis tools. For example, the kernel density estimation (KDE) algorithm is used to calculate the pressure distribution in the key area, and finally the pressing distribution parameters are obtained. The query of the temporal characteristics of the pressing distribution parameters during user input can be achieved through time series analysis methods. For example, the dynamic time warping (DTW) algorithm is applied to align the operation rhythm, and finally the temporal characteristics are obtained. The matching process of the temporal characteristics with the preset rhythm feedback mechanism can be achieved through signal processing algorithms. For example, cross-correlation analysis is used to calculate the phase difference, and finally the phase matching threshold is obtained. The fitting of the force feedback curve corresponding to the adaptive keyboard can be achieved through curve optimization algorithms. For example, B-spline curve interpolation is used for key force control points, and finally the force feedback curve is obtained. The generation of the rhythm optimization target corresponding to the adaptive keyboard can be achieved through multi-objective optimization methods. For example, based on the NSGA-II algorithm, the response speed and tactile comfort are balanced, and finally the rhythm optimization target is obtained.

[0112] By extracting the collaborative optimization parameters corresponding to the rhythm optimization target, the present invention helps to coordinate the key travel, feedback mechanism and user input rhythm, create a higher-fitting input experience, significantly improve the input efficiency, and push the adaptive keyboard to a new height in ergonomics and practicality.

[0113] Among them, the collaborative optimization parameters refer to a set of comprehensive indicators used to coordinate the work of each component of the adaptive keyboard and improve the user input experience. For example, in a typing scenario, the key travel of key A is shortened by 2 mm to match high-frequency use, and at the same time, the force feedback intensity is increased by 10% to make the feedback rhythm and input timing more compatible. For key B, to cooperate with low-frequency use, the key travel is extended by 1 mm and the force feedback is reduced by 5%. These specific values regarding key travel, force feedback intensity, and feedback rhythm are the collaborative optimization parameters. Optionally, the extraction of the collaborative optimization parameters corresponding to the rhythm optimization target can be achieved through a genetic algorithm. For example, according to indicators such as the accuracy, speed, and comfort of user input, after multiple generations of evolution, a set of parameter combinations that can optimize the fitness function is finally obtained, which is the collaborative optimization parameters.

[0114] Furthermore, by calculating the collaborative vector value corresponding to the collaborative optimization parameters, the present invention can integrate multiple scattered optimization parameters into a quantitative index, clearly measure the collaborative effect among the components of the keyboard, and quickly judge the effectiveness of the current optimization scheme.

[0115] Among them, the collaborative vector value refers to a comprehensive quantitative index used to measure the collaborative cooperation effect among multiple collaborative optimization parameters in the adaptive keyboard. The higher the collaborative vector value, the better the collaboration among these parameters, indicating that the settings of the keyboard in terms of key travel, force feedback, etc. can better meet the user's usage needs.

[0116] As an embodiment of the present invention, the calculating the collaborative vector value corresponding to the collaborative optimization parameter includes:

[0117] The collaborative vector value corresponding to the collaborative optimization parameter is calculated using the following formula:

[0118]

[0119] Wherein, CV represents the collaborative vector value corresponding to the collaborative optimization parameter, M represents the number of parameters of the collaborative optimization parameter, j represents the parameter index of the collaborative optimization parameter, and K i represents the parameter influence factor corresponding to the jth collaborative optimization parameter, D j represents the damping coefficient of the jth collaborative optimization parameter, F j Represents the actual value of the parameter corresponding to the jth collaborative optimization parameter.

[0120] In detail, the parameter influencing factor refers to the jth collaborative optimization parameter, which reflects the importance or influence of the parameter in affecting the overall collaborative effect of the keyboard. For example, for the collaborative optimization parameter of key travel, its parameter influencing factor is relatively high, because the appropriate key travel has a significant impact on the user's typing comfort and efficiency; while for some minor feedback parameters, its influencing factor is relatively low; the damping coefficient refers to the limitation or resistance used to consider the actual adjustment process of the parameter. For example, when adjusting the keyboard force feedback strength, due to hardware performance or technical limitations, the force feedback strength cannot be increased or decreased indefinitely, and the damping coefficient can reflect the impact of this limitation on parameter adjustment; the actual value of the parameter refers to the specific value of the jth collaborative optimization parameter under the current keyboard setting. For example, for the collaborative optimization parameter of key travel, its actual value may be the currently set key travel length (such as 2mm); for the force feedback strength parameter, its actual value is the currently set force feedback strength.

[0121] Further, It is used to calculate the synergy vector value corresponding to the collaborative optimization parameter. Its purpose is to comprehensively evaluate the synergy effect among multiple collaborative optimization parameters. It first calculates each parameter based on its characteristics, and then summarizes and normalizes them to obtain a value that can reflect the overall synergy.

[0122] S5. Based on the health loss index, identify the local fluctuation state of the key area in the adaptive keyboard, extract the key fluctuation factors in the local fluctuation state, and analyze the correction priorities corresponding to the key fluctuation factors. Based on the correction priorities, generate a health protection plan for the adaptive keyboard for the target user.

[0123] Based on the health loss index, the present invention identifies the local fluctuation state of the key areas in the adaptive keyboard, and can timely discover which key areas cause excessive health loss to users due to high-frequency use or unreasonable design, thereby significantly improving the comfort and sustainability of keyboard use.

[0124] Among them, the local fluctuation state refers to a comprehensive description of the pressure change and other conditions in the key areas of the adaptive keyboard, which reflects the stability, regularity and other characteristics of the pressure change during the use of the key areas. And according to information such as regional fluctuation entropy, it is judged whether a certain key area is in a stable and regular low-fluctuation state or a complex and disordered high-fluctuation state.

[0125] As an embodiment of the present invention, the identifying the local fluctuation state of the key areas in the adaptive keyboard based on the health loss index includes: extracting the dynamic attenuation characteristics in the health loss index; dividing the abnormal key areas in the adaptive keyboard based on the dynamic attenuation characteristics; detecting the pressure change value in the abnormal key areas; calculating the regional fluctuation entropy corresponding to the pressure change value; and identifying the local fluctuation state of the key areas in the adaptive keyboard according to the regional fluctuation entropy.

[0126] Among them, the dynamic attenuation characteristic refers to the gradually weakening or decreasing trend characteristic presented during the change of the health loss index over time or the number of uses. For example, as the user uses the keyboard for a long time, the health loss index corresponding to some key areas does not always remain at a high level, but will slowly decline. The rate, amplitude, etc. of this decline are the dynamic attenuation characteristics; the abnormal key area refers to the key area divided according to the dynamic attenuation characteristics, and its health loss situation is significantly different from the normal situation. It can be that the health loss index is too high, far exceeding the average level, indicating that the area brings greater health risks to the user when used; it can also be that the dynamic attenuation characteristic does not conform to the conventional mode, such as sudden sharp decline or rise and other abnormal changes; the pressure change value refers to the change of the pressure exerted by the user's finger when pressing the key in the abnormal key area, and the pressure change value records information such as the amplitude and frequency of this fluctuation. For example, some keys require the user to press hard, and the pressure change amplitude is large; while some keys are easy to press, and the pressure change is relatively small; the regional fluctuation entropy refers to the disorder degree or complexity degree of the pressure change in the abnormal key area calculated according to the pressure change value. The higher the entropy value, the more complex and irregular the pressure change in the area; the lower the entropy value, the relatively simple and regular the pressure change.

[0127] Furthermore, the extraction of dynamic attenuation features in the health loss index can be achieved through a time series decomposition algorithm, such as: using STL to decompose the long-term attenuation trend, and finally obtaining the dynamic attenuation features; the division of the abnormal key area in the adaptive keyboard can be achieved through a spatial clustering algorithm, such as: applying DBSCAN density clustering to identify the pressure abnormality clustering area, and finally obtaining the abnormal key area; the detection of the pressure change value in the abnormal key area can be achieved through a differential signal processing method, such as: using a Savitzky-Golay filter to calculate the pressure gradient, and finally obtaining the pressure change value; the calculation of the regional fluctuation entropy corresponding to the pressure change value can be achieved through an information entropy analysis method, such as: quantifying the pressure fluctuation uncertainty based on Shannon entropy, and finally obtaining the regional fluctuation entropy; the identification of the local fluctuation state of the key area in the adaptive keyboard can be achieved through a state space modeling method, such as: using a hidden Markov model to identify the fluctuation mode conversion, and finally obtaining the local fluctuation state.

[0128] The present invention reveals the core factors affecting the health loss of the key area by extracting the key fluctuation factors in the local fluctuation state and analyzing the correction priorities corresponding to the key fluctuation factors. Analyzing the correction priorities can distinguish the importance of problems, concentrate resources, give priority to solving key problems, and effectively improve the safety and comfort of keyboard use.

[0129] Among them, the key fluctuation factors refer to the factors that have a decisive influence on the health loss caused by keyboard use in the local fluctuation state. These factors can highlight the core of abnormal pressure changes in keyboard design or user operation. For example, if the key travel of a certain key area is too long, causing the user to press too hard, then the excessive key travel is a key fluctuation factor; the correction priorities refer to the matters that need to be given priority in the process of optimizing keyboard design and reducing health loss based on the analysis of key fluctuation factors. For example, if the pressure change in a certain key area is abnormal and it has been found that it is caused by unreasonable key travel design, and the key travel adjustment is relatively simple, then adjusting the key travel of this area will be listed as a correction priority. Optionally, the extraction of key fluctuation factors in the local fluctuation state can be achieved by principal component analysis method, such as: using PCA dimensionality reduction to extract the main fluctuation mode, and finally obtaining key fluctuation factors; the analysis of the correction priorities corresponding to the key fluctuation factors can be achieved by multi-criteria decision-making method, such as: using AHP hierarchical analysis method to construct a priority evaluation matrix, and finally obtaining correction priorities.

[0130] Furthermore, based on the corrected priorities, the present invention generates a health protection plan for the target user regarding the adaptive keyboard, which can effectively reduce the musculoskeletal injuries caused by the long-term use of the keyboard by the target user, save protection resources, and ensure that they maintain a good health state while enjoying an efficient input experience.

[0131] Among them, the health protection plan refers to a set of comprehensive strategies designed to reduce the health risks when users use the adaptive keyboard. For example, after analysis, it is found that the excessive key travel and excessive pressure in specific areas are the main problems. The plan will specifically shorten the excessive key travel, optimize the keyboard structure to make it easier for users to press the keys; add soft silicone pads in the areas with excessive pressure to disperse the pressure; at the same time, set an intelligent reminder function. When the user continuously uses the keyboard for 1 hour, it will remind the user to get up and move around to relax the hands, comprehensively protecting the health of the user when using the keyboard. Optionally, the generation of the health protection plan for the target user regarding the adaptive keyboard can be achieved through plan generation tools, such as tools like NI LabVIEW, TensorFlow, etc.

[0132] Compared with the problems described in the background art, the present invention can analyze the joint cumulative stress when the target user uses the adaptive keyboard by obtaining the pressure distribution map during the adaptive keyboard input, can identify the local fluctuation state of the keyboard key area, formulate a targeted health protection plan, and effectively reduce the health risks brought by the long-term use of the keyboard by the user. By extracting the fatigue response parameters in the joint stress accumulation curve, the present invention can accurately calculate the keyboard layout adaptability, evaluate the impact of the existing keyboard layout on the user's health, and can specifically optimize the keyboard key travel and formulate a health protection plan to effectively prevent health problems caused by the long-term use of the keyboard. Further, based on the layout adaptability, the present invention simulates the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario, can intuitively show the long-term impact of different keyboard layouts on the user's musculoskeletal system, and accordingly can specifically optimize the keyboard design or provide reasonable usage suggestions for the user to effectively reduce the risk of musculoskeletal injuries caused by the long-term use of the keyboard. Further, by integrating the key travel adjustment data cluster and the preset rhythm feedback mechanism, the present invention generates the rhythm optimization target corresponding to the adaptive keyboard, can significantly improve the use experience of the adaptive keyboard, and through key travel adjustment, makes the key operation conform to human mechanics and reduces the musculoskeletal burden. Finally, based on the health loss index, the present invention identifies the local fluctuation state of the key area in the adaptive keyboard, can timely discover which key areas cause excessive health loss to the user due to high-frequency use or unreasonable design, thereby significantly improving the comfort and sustainability of keyboard use. Therefore, a method and system for protecting the health of users by an intelligent adaptive keyboard provided by the embodiments of the present invention can reduce the health risks brought by the long-term use of the keyboard

[0133] Embodiment 2:

[0134] As shown Figure 2 in the figure, it is a functional module diagram of an intelligent adaptive keyboard for protecting the health of users according to the present invention.

[0135] The intelligent adaptive keyboard for protecting the health of users 200 according to the present invention can be installed in an electronic device. According to the implemented functions, the intelligent adaptive keyboard for protecting the health of users can include a curve construction module 201, a fitting degree calculation module 202, a data cluster module 203, a vector value calculation module 204, and a solution generation module 205. The modules according to the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0136] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0137] The curve construction module 201 obtains the pressure distribution map of the target user during the input on the adaptive keyboard, based on the pressure distribution map, analyzes the joint cumulative stress corresponding to the target user using the adaptive keyboard, and constructs the joint stress accumulation curve corresponding to the target user according to the joint cumulative stress;

[0138] The fitting degree calculation module 202 is used to extract the fatigue response parameters in the joint stress accumulation curve, analyze the peak load threshold and the continuous risk index in the fatigue response parameters, and calculate the layout fitting degree corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index;

[0139] The data cluster module 203 is used to simulate the cumulative value of musculoskeletal injuries of the target user in the long-term input scenario based on the layout fitting degree, and construct the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injuries;

[0140] The vector value calculation module 204 is used to integrate the key travel adjustment data cluster and a preset rhythm feedback mechanism, generate the rhythm optimization target corresponding to the adaptive keyboard, extract the collaborative optimization parameters corresponding to the rhythm optimization target, and calculate the collaborative vector value corresponding to the collaborative optimization parameters;

[0141] The solution generation module 205 is used to identify the local fluctuation state of the key area on the adaptive keyboard based on the health loss index, extract the key fluctuation factors in the local fluctuation state, analyze the correction priority items corresponding to the key fluctuation factors, and generate the health protection solution for the target user for the adaptive keyboard based on the correction priority items.

[0142] Specifically, each module in the intelligent adaptive keyboard for protecting user health system 200 in the embodiments of the present invention adopts the same technical means as the intelligent adaptive keyboard for protecting user health method described in the above Figure 1 and can produce the same technical effects, which will not be elaborated here.

[0143] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for an intelligent adaptive keyboard to protect user health, characterized in that, The method includes: Obtaining a pressure distribution map during the target user's input on the adaptive keyboard, based on the pressure distribution map, analyzing the joint cumulative stress corresponding to the target user's use of the adaptive keyboard, and constructing a joint stress accumulation curve corresponding to the target user according to the joint cumulative stress; Extracting fatigue response parameters from the joint stress accumulation curve, analyzing the peak load threshold and continuous risk index in the fatigue response parameters, and calculating the layout adaptability corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index; Based on the layout adaptability, simulating the cumulative value of musculoskeletal injuries of the target user in a long-term input scenario, and constructing a key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injuries; Integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism to generate a rhythm optimization target corresponding to the adaptive keyboard, extracting collaborative optimization parameters corresponding to the rhythm optimization target, and calculating a collaborative vector value corresponding to the collaborative optimization parameters; Based on the health loss index, identifying the local fluctuation state of the key areas on the adaptive keyboard, extracting the key fluctuation factors in the local fluctuation state, and analyzing the correction priority items corresponding to the key fluctuation factors, and generating a health protection plan for the target user for the adaptive keyboard based on the correction priority items.

2. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, wherein, The constructing a joint stress accumulation curve corresponding to the target user according to the joint cumulative stress includes: Extracting stress accumulation data from the joint cumulative stress; Generating a stress change sequence corresponding to the stress accumulation data; Performing a segmentation process on the stress change sequence to obtain stress fluctuation intervals; Identifying stress accumulation segments in the stress fluctuation intervals; Constructing a joint stress accumulation curve corresponding to the target user based on the stress accumulation segments.

3. The method for an intelligent adaptive keyboard to protect user health according to claim 1, wherein The extracting fatigue response parameters from the joint stress accumulation curve includes: Collecting curve load parameters in the joint stress accumulation curve; Querying the stress amplitude and number of cycles in the curve load parameters; Fitting a fatigue evolution matrix corresponding to the stress amplitude and number of cycles; Calibrating fatigue response points in the fatigue evolution matrix; Extracting fatigue response parameters from the joint stress accumulation curve according to the fatigue response points.

4. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, wherein, The calculating the layout adaptability corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index includes: Calculating the layout adaptability corresponding to the adaptive keyboard using the following formula: Among them, Bd represents the layout adaptation degree corresponding to the adaptive keyboard, t represents the time when the user uses the keyboard, n represents the total number of usage scenarios corresponding to the adaptive keyboard, i represents the number index corresponding to the usage scenario, and P i represents the peak load threshold under the i-th usage scenario, and R i represents the continuous risk index under the i-th usage scenario, and T i represents the cumulative duration of the i-th usage scenario within the time t, and S represents the normalization constant.

5. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, characterized in that, The simulating the cumulative value of musculoskeletal injuries of the target user in a long-term input scenario based on the layout adaptability includes: Analyzing the posture adaptation parameters corresponding to the layout adaptability; Based on the posture adaptation parameters, analyzing the joint load intensity of the target user in different input actions; Quantifying the joint fatigue index of the target user during continuous input based on the joint load intensity; Constructing a musculoskeletal fatigue framework corresponding to the target user based on the joint fatigue index; Simulating the cumulative value of musculoskeletal injuries of the target user in a long-term input scenario based on the musculoskeletal fatigue framework.

6. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, wherein Constructing the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injury includes: Analyzing the key injury areas corresponding to the cumulative value of musculoskeletal injury; Extracting the touch pressure peak points in the key injury areas; Calculating the key travel attenuation index corresponding to the keys on the adaptive keyboard based on the touch pressure peak points; Querying the key travel damping parameters corresponding to the adaptive keyboard based on the key travel attenuation index; Constructing the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard based on the key travel damping parameters.

7. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, wherein, Integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism to generate the rhythm optimization target corresponding to the adaptive keyboard includes: Extracting the pressing distribution parameters in the key travel adjustment data cluster; Querying the timing characteristics of the pressing distribution parameters during user input; Performing a matching process on the timing characteristics and the preset rhythm feedback mechanism to obtain a phase matching threshold; Fitting the force feedback curve corresponding to the adaptive keyboard based on the phase matching threshold; Generating the rhythm optimization target corresponding to the adaptive keyboard based on the force feedback curve.

8. The method for protecting the health of users by an intelligent adaptive keyboard according to claim 1, characterized in that, Calculating the collaborative vector value corresponding to the collaborative optimization parameter includes: Calculating the collaborative vector value corresponding to the collaborative optimization parameter using the following formula: Among them, CV represents the collaborative vector value corresponding to the collaborative optimization parameter, M represents the number of parameters of the collaborative optimization parameter, j represents the parameter index of the collaborative optimization parameter, K i represents the parameter influence factor corresponding to the j-th collaborative optimization parameter, D j represents the damping coefficient of the j-th collaborative optimization parameter, F j represents the actual value of the parameter corresponding to the j-th collaborative optimization parameter.

9. The method for an intelligent adaptive keyboard to protect user health according to claim 1, wherein Identifying the local fluctuation state of the key areas on the adaptive keyboard based on the health loss index includes: Extracting the dynamic attenuation characteristics in the health loss index; Dividing the abnormal key areas on the adaptive keyboard based on the dynamic attenuation characteristics; Detecting the pressure change value within the abnormal key areas; Calculating the regional fluctuation entropy corresponding to the pressure change value; Identifying the local fluctuation state of the key areas on the adaptive keyboard according to the regional fluctuation entropy.

10. An intelligent adaptive keyboard protection user health system, characterized in that, The system includes: A curve construction module that obtains the pressure distribution map of the target user during input on the adaptive keyboard, analyzes the joint cumulative stress corresponding to the target user using the adaptive keyboard based on the pressure distribution map, and constructs the joint stress accumulation curve corresponding to the target user according to the joint cumulative stress; A fitness calculation module for extracting the fatigue response parameters in the joint stress accumulation curve, analyzing the peak load threshold and the continuous risk index in the fatigue response parameters, and calculating the layout fitness corresponding to the adaptive keyboard based on the peak load threshold and the continuous risk index; A data cluster module for simulating the cumulative value of musculoskeletal injury of the target user in a long-term input scenario based on the layout fitness, and constructing the key travel adjustment data cluster corresponding to the keys on the adaptive keyboard according to the cumulative value of musculoskeletal injury; A vector value calculation module for integrating the key travel adjustment data cluster with a preset rhythm feedback mechanism to generate the rhythm optimization target corresponding to the adaptive keyboard, extracting the collaborative optimization parameters corresponding to the rhythm optimization target, and calculating the collaborative vector value corresponding to the collaborative optimization parameters; A solution generation module, configured to identify the local fluctuation state of the key area in the adaptive keyboard based on the health loss index, extract the key fluctuation factors in the local fluctuation state, analyze the correction priority items corresponding to the key fluctuation factors, and generate a health protection solution for the adaptive keyboard by the target user based on the correction priority items.

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