Adaptive adjustment method and system for office chair

Abstract

The invention provides an office chair adaptive adjustment method and system, and relates to the technical field of office chair control. The method comprises the following steps: acquiring an image data stream of a user body, and extracting body shape feature data and posture information data of the user by processing the image data stream; according to the image data flow and by analyzing computer information, judging the task type of the current work task of the user; based on the task type, according to the body shape feature data and the posture information data, obtaining adjustment parameters of a plurality of adjustment parts of the office chair; according to the adjusting parameters, the height of the seat, the angle of the backrest, the position of the waist support, the protrusion degree of the waist support, the height of the armrests and the distance between the armrests are adjusted by controlling an electric adjusting mechanism of the office chair. The adaptive adjustment method for the office chair overcomes the limitation that only primary or low-frequency adjustment is carried out in the prior art, realizes linkage accurate adjustment of multiple parts of the office chair, and provides dynamic adaptive support.

Description

Technical Field

[0001] The present invention relates to the technical field of office chair control, and in particular, to an adaptive adjustment method and system for an office chair. Background Art

[0002] In the existing intelligent office environment, office chairs integrated with intelligent functions have been widely deployed. Such intelligent office chairs are usually equipped with an image capture unit, a processing unit, and multiple electric adjustment mechanisms. When a user first sits down, the image capture unit will acquire the body image data of the user. After receiving these image data, the processing unit analyzes them to identify the user's body contour, the positions of key skeletal points, and the relative dimensions of various parts of the body, so as to extract the characteristic parameters related to the user's current body shape. Based on these body shape characteristic parameters, the system will calculate the initial seat adjustment parameters, such as the height of the seat surface, the tilt angle of the backrest, the height, convexity degree, and support angle of the lumbar support, and the height and spacing of the armrests. Subsequently, the electric adjustment mechanisms of the office chair receive these parameters and perform corresponding physical adjustments to enable the seat to initially adapt to the user's basic body shape.

[0003] However, the use of an office chair is often a long-term continuous process. During long working hours, the user's body posture will change slightly due to fatigue and other reasons. For example, the user may gradually change from the initial straight sitting posture to a slightly hunched back within a few hours, or the body center of gravity may shift slightly forward or backward. Although these slight changes in body posture are not sufficient to be determined as obvious non-standard sitting postures, they will significantly change the force distribution when the user's body contacts the seat and the support requirements for various parts of the seat. For example, the original position and convexity degree of the lumbar support accurately adjusted for a straight body posture may no longer be able to effectively support the user's lumbar spine when the user is slightly hunched back, resulting in support failure or deviation of the support position.

[0004] In addition, the user does not maintain a fixed posture on the office chair but will perform a variety of different work tasks. For example, the user needs to type on the keyboard for a long time, and at this time, the body usually leans forward or maintains a neutral sitting posture, and the wrists and forearms may need to be supported; the user may need to read paper documents or electronic screens, and at this time, the body may lean back or sideways, increasing the support requirements for the neck and upper back; during a video conference, the user needs to maintain a specific upper body posture; when writing or drawing, the body may tend to lean forward and be closer to the desktop. Different work tasks will prompt the user to adopt different body postures and attitudes. Even for users with the same body shape, there are significant differences in the support requirements for various parts of the seat when performing different tasks. These differences in support requirements are coupled with the user's specific body shape characteristics.

[0005] Existing office chair adjustment methods based on image recognition mainly focus on body shape recognition and one-time adjustment when the user first sits down, or only perform periodic body posture detection and adjustment with a relatively low frequency. Such a low-frequency adjustment mechanism is difficult to capture the subtle changes in body posture caused by fatigue or task switching during the user's several-hour work period, nor can it dynamically adjust the support strategy according to the work task the user is currently performing. Therefore, the support state of the office chair is likely to gradually deviate from the optimal state during the user's use. If the user stays in a support environment that does not fit their current body shape, body posture, and task requirements for a long time, it may lead to excessive local pressure, muscle fatigue, and even have an adverse impact on spinal health. Summary of the Invention

[0006] The purpose of the present invention is to provide an office chair adaptive adjustment method and system, which overcome the limitations of only performing initial or low-frequency adjustments in the prior art, realize the precise linkage adjustment of multiple components of the office chair, and provide dynamic adaptive support.

[0007] In the first aspect, the present invention provides an office chair adaptive adjustment method, including the following steps: Obtain the image data stream of the user's body, and extract the body shape feature data and body posture information data of the user by processing the image data stream; Judge the task type of the user's current work task according to the image data stream and by analyzing computer information; Based on the task type, obtain the adjustment parameters of multiple adjustment components of the office chair according to the body shape feature data and the body posture information data; According to the adjustment parameters, adjust the seat height, backrest angle, lumbar support position, lumbar support protrusion, armrest height, and armrest spacing by controlling the electric adjustment mechanism of the office chair.

[0008] The office chair adaptive adjustment method provided by the present invention overcomes the limitations of only performing initial or low-frequency adjustments in the prior art. By continuously obtaining and analyzing the user's body images, while identifying the type of work task the user is currently performing, and based on the combined characteristics of body shape, body posture, and task, dynamically and linkably precisely control multiple adjustable components of the office chair to achieve real-time personalized support for the user's current body state and task requirements.

[0009] Further, the step of judging the task type of the user's current work task according to the image data stream and by analyzing computer information includes: S1. Extract the user's arm posture and head posture information by analyzing the image data stream. If the arm posture and head posture information are not detected, execute step S2; otherwise, based on the preset mapping relationships between arm postures and task types, and between head postures and task types, preliminarily determine the task type of the user's current work task, and execute step S2; S2. Detect the type of the currently running program by analyzing the computer information, and based on the preset mapping relationship between program types and task types, preliminarily determine the task type of the user's current work task, and execute step S3; S3. When only the preliminary judgment result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary judgment results of step S1 and step S2 exist simultaneously, if the judgment results of step S1 and step S2 are consistent, determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rules, select the judgment result with the higher priority from the judgment results of step S1 and step S2 as the task type of the user's current work task.

[0010] Further, the specific steps in step S2 include: S21. Detect the type of the currently running program, and based on the preset mapping relationship between program types and task types, judge the task types of each running program; S22. Obtain the number of currently running programs and determine the window focus status of each running program; S23. If the number of running programs is greater than 1, judge whether there is a running program with a focus status according to the window focus status of each running program; if so, use the task type corresponding to the running program with the focus status as the preliminary judgment result of step S2; otherwise, according to the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary judgment result of step S2; S24. If the number of running programs is equal to 1, use the task type corresponding to this running program as the preliminary judgment result of step S2.

[0011] Further, the specific steps in step S21 include: S211. Detect the type of the currently running program. For a running program of the browser type, through the process ID, call the operating system API to obtain the URL address of the current tab page under this browser program; S212. By parsing the URL address of the current tab, extract the domain name information in the URL, and based on the mapping relationship between the domain name and the task type preset for the browser program, if the domain name information matches the preset domain name of the video website, then determine that the task type of the browser program is "watching videos"; if the domain name information matches the preset domain name of the online meeting platform, then determine that the task type of the browser program is "online meeting"; otherwise, mark the task type of the browser program as "browsing the web".

[0012] Further, the step of obtaining the adjustment parameters of multiple adjustment components of the office chair based on the task type, the body shape feature data, and the body posture information data includes: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support protrusion adjustment range, armrest height adjustment range, and armrest spacing adjustment range corresponding to the current task type; there is a mapping relationship between the task type and the above adjustment ranges, and the above adjustment ranges are associated with the body shape feature data and the body posture information data; According to the body shape feature data, calculate the height parameter, and based on the height parameter, determine the target seat height value within the seat height adjustment range; calculate the weight parameter, and based on the weight parameter, determine the target lumbar support protrusion value within the lumbar support protrusion adjustment range; calculate the shoulder width parameter, and based on the shoulder width parameter, determine the target armrest spacing value within the armrest spacing adjustment range; According to the body posture information data, quantify the torso curvature and tilt angle, and based on the torso curvature, determine the target backrest angle value within the backrest angle adjustment range; based on the torso tilt angle and the target seat height value, determine the target lumbar support position value within the lumbar support position adjustment range; based on the target seat height value, determine the target armrest height value within the armrest height adjustment range.

[0013] In a second aspect, the present invention provides an office chair adaptive adjustment system, including: A first acquisition module, configured to acquire the image data stream of the user's body, and by processing the image data stream, extract the body shape feature data and the body posture information data of the user; A judgment module, configured to judge the task type of the user's current work task according to the image data stream and by analyzing the computer information; A second acquisition module, configured to obtain the adjustment parameters of multiple adjustment components of the office chair based on the task type, the body shape feature data, and the body posture information data; An adjustment module, configured to adjust the seat height, backrest angle, lumbar support position, lumbar support protrusion, armrest height, and armrest spacing according to the adjustment parameters by controlling the electric adjustment mechanism of the office chair.

[0014] Further, when the judgment module is used to judge the task type of the user's current work task according to the image data stream and by analyzing the computer information, it executes: S1. By analyzing the image data stream, extract the user's arm posture and head posture information; if the arm posture and head posture information are not detected, execute step S2; otherwise, according to the preset mapping relationships between arm postures and task types, and between head postures and task types, preliminarily judge the task type of the user's current work task, and execute step S2; S2. By analyzing the computer information, detect the type of the currently running program, and according to the preset mapping relationship between program types and task types, preliminarily judge the task type of the user's current work task, and execute step S3; S3. When only the preliminary judgment result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary judgment results of step S1 and step S2 exist simultaneously, if the judgment results of step S1 and step S2 are consistent, determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rules, select the judgment result with the higher priority from the judgment results of step S1 and step S2 as the task type of the user's current work task.

[0015] Further, when the judgment module is used to detect the type of the currently running program by analyzing the computer information, and according to the preset mapping relationship between program types and task types, preliminarily judge the task type of the user's current work task, it executes: S21. Detect the type of the currently running program, and according to the preset mapping relationship between program types and task types, judge the task types of each running program; S22. Obtain the number of currently running programs, and determine the window focus status of each running program; S23. If the number of running programs is greater than 1, then according to the window focus status of each running program, judge whether there is a running program with a focus status; if so, use the task type corresponding to the running program with a focus status as the preliminary judgment result of step S2; otherwise, according to the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary judgment result of step S2; S24. If the number of running programs is equal to 1, then use the task type corresponding to this running program as the preliminary judgment result of step S2.

[0016] Further, when the judgment module is used to detect the type of the currently running program, and according to the preset mapping relationship between program types and task types, judge the task types of each running program, it executes: S211 detects the type of the currently running program. For a browser-type running program, the operating system API is called through the process ID to obtain the URL address of the current tab of the browser program; S212. By parsing the URL address of the current tab, extracting the domain name information in the URL, and based on the mapping relationship between the domain name and the task type preset for the browser program, if the domain name information matches the preset video website domain name, then the task type of the browser program is judged to be "watching videos"; if the domain name information matches the preset online conference platform domain name, then the task type of the browser program is judged to be "online conference"; otherwise, the task type of the browser program is marked as "browsing the web".

[0017] Furthermore, the second acquisition module is executed when it is used to acquire the adjustment parameters of the plurality of adjustment components of the office chair based on the task type, according to the body shape feature data and the body posture information data: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support convexity adjustment range, armrest height adjustment range and armrest spacing adjustment range corresponding to the current task type; the task type has a mapping relationship with the above adjustment ranges, and the above adjustment ranges are associated with body shape feature data and body posture information data; According to the body shape characteristic data, the height parameter is calculated, and the target seat height value is determined within the seat height adjustment range based on the height parameter; the weight parameter is calculated, and the target lumbar support convexity value is determined within the lumbar support convexity adjustment range based on the weight parameter; the shoulder width parameter is calculated, and the target armrest spacing value is determined within the armrest spacing adjustment range based on the shoulder width parameter; According to the body information data, the trunk curvature and inclination angle are quantified, and the target backrest angle value is determined within the backrest angle adjustment range based on the trunk curvature; the target lumbar support position value is determined within the lumbar support position adjustment range based on the torso inclination angle and the target seat height value; the target armrest height value is determined within the armrest height adjustment range based on the target seat height value.

[0018] As can be seen from the above, the office chair adaptive adjustment method provided by the present invention overcomes the limitations of the initial or low-frequency adjustment in the prior art. By continuously acquiring and analyzing the user's body image, the user's current work task type is identified at the same time. Based on these multi-dimensional information, the system can calculate the optimal office chair adjustment parameters in real time or quasi-real time, and control multiple adjustable components in a linked manner for precise adjustment. This enables the support provided by the office chair to continuously fit the user's current real physical state and task requirements, significantly improve the user's comfort for long-term use, reduce local pressure and muscle fatigue, and help maintain the user's physical health.

[0019] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the embodiments of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description and the drawings. Description of the Drawings

[0020] Figure 1 It is a flowchart of an office chair adaptive adjustment method provided by an embodiment of the present invention.

[0021] Figure 2 It is a schematic structural diagram of an office chair adaptive adjustment system provided by an embodiment of the present invention.

[0022] Label Description: 100, First Acquisition Module; 200, Judgment Module; 300, Second Acquisition Module; 400, Adjustment Module. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the drawings below is not intended to limit the scope of the present invention to be protected, but only represents the selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0024] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0025] Referring to the attached Figure 1 , the present invention provides an office chair adaptive adjustment method, including the following steps: Acquire the image data stream of the user's body, and extract the body shape feature data and body posture information data of the user by processing the image data stream; Judge the task type of the user's current work task according to the image data stream and by analyzing the computer information; Based on the task type, obtain the adjustment parameters of multiple adjustment components of the office chair according to the body shape feature data and body posture information data; According to the adjustment parameters, the electric adjustment mechanism of the office chair is controlled to adjust the seat height, backrest angle, lumbar support position, lumbar support convexity, armrest height, and armrest spacing.

[0026] Obtaining the image data stream of the user's body can be achieved through an image capture unit set on or near the office chair. To process the image data stream and extract body shape feature data and body posture information data, image recognition algorithms can be used to identify the user's body contour and bone points, and calculate the dimensions (body shape features) and postures (body posture information) of various parts of the body. Judging the task type of the user's current work task can be combined with analyzing the user's posture in the image data stream (such as the position of the arms and head) and analyzing the type and status of the application programs currently running in the computer system. To obtain the office chair adjustment parameters, a mapping relationship or model between the task type, body shape features, body posture information, and adjustment parameters can be established, and obtained through table lookup or calculation. Controlling the electric adjustment mechanism of the office chair for adjustment can be achieved by sending a control signal to the motor inside the seat to drive the adjustment components to move to the target position or angle.

[0027] Specifically, this method continuously obtains the image data stream of the user's body to monitor the user's body shape features and body posture information in real time. At the same time, the system analyzes the computer information to identify the specific work task type that the user is performing. For example, by identifying the user's posture, it can be determined whether the user is typing or reading, and by analyzing the computer program, it can be determined whether the user is using document editing software or conducting a video conference. The system comprehensively considers the user's body shape, current body posture, and the task type being executed, and calculates the target parameter values of each adjustment component of the office chair that are most suitable for the current state. For example, for a user with a relatively tall height during a typing task, the system will calculate a higher seat height and a suitable armrest height; for a user who has a slightly hunched back due to long-term reading, the system may calculate a lumbar support position and parameters that are more forward and have a greater convexity. The calculated parameters are then sent to the electric adjustment mechanism of the office chair to drive physical adjustments to the seat height, backrest angle, lumbar support position, lumbar support convexity, armrest height, and armrest spacing. Thus, the office chair can dynamically adapt to changes in the user's body state and tasks, provide continuous and personalized support, solve the problems of untimely and incomplete adjustment of existing office chairs, improve the support fitting degree, and reduce local pressure.

[0028] In some specific embodiments, the image capture unit continuously acquires the video stream of the user's upper body. The image processing module identifies the user's skeletal points from the video stream, calculates the user's height and shoulder width as body shape feature data, and quantifies the spinal curvature angle and trunk tilt angle as postural information data. Meanwhile, the system obtains the program name of the current active window through the operating system API, such as "Microsoft Word". The system determines the program type of "Microsoft Word" as a "document editing" task according to the preset mapping relationship. Based on the "document editing" task type, the calculated height, shoulder width, spinal curvature angle, and trunk tilt angle, the system queries the preset adjustment parameter table. For example, for a user with a height of 175 cm, a shoulder width of 45 cm, a spinal curvature angle of 5 degrees, and a trunk tilt angle of 10 degrees when performing a "document editing" task, the system calculates that the target seat height is 45 cm, the target backrest angle is 100 degrees, the target lumbar support position is 25 cm (from the seat surface), the target lumbar support protrusion is 3 cm, the target armrest height is 22 cm (from the seat surface), and the target armrest spacing is 50 cm. The control module then sends instructions to the seat height motor, backrest angle motor, lumbar support position motor, lumbar support protrusion motor, armrest height motor, and armrest spacing motor to drive each component to move to the calculated target position or angle.

[0029] In certain embodiments, the steps of determining the task type of the user's current work task based on the image data stream and by analyzing computer information include: S1. By analyzing the image data stream, extract the user's arm posture and head posture information; if the arm posture and head posture information are not detected, then execute step S2; otherwise, according to the preset mapping relationships between arm postures and task types, and between head postures and task types, preliminarily determine the task type of the user's current work task, and execute step S2; S2. By analyzing the computer information, detect the type of the currently running program, and according to the preset mapping relationship between program types and task types, preliminarily determine the task type of the user's current work task, and execute step S3; S3. When only the preliminary judgment result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary judgment results of step S1 and step S2 exist simultaneously, if the judgment results of step S1 and step S2 are consistent, then determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rules, select the judgment result with a higher priority from the judgment results of step S1 and step S2 as the task type of the user's current work task.

[0030] This embodiment aims to solve the problem that the intelligent office chair cannot accurately identify the type of the user's current work task during the user's use, resulting in inaccurate seat adjustment. This method combines the user's posture information and the computer running program information. First, the posture information of the user's arms and head is obtained by analyzing the image data stream. This posture information is associated with specific task types. If the posture information is successfully extracted, the system makes a preliminary task type judgment according to the preset mapping relationship. If the posture information cannot be detected, no posture-based judgment is made. Then, the type of the currently running program is detected by analyzing the computer information. Different program types usually correspond to different work tasks. The system makes a preliminary task type judgment based on the computer information according to the preset mapping relationship between the program type and the task type. Finally, the two preliminary judgment results are integrated. If there is only a judgment result based on the computer information, this result is adopted. If both judgment results exist and are consistent, this consistent result is adopted. If the two judgment results are inconsistent, the system selects the result with a higher priority as the final task type according to the preset priority rules. By combining the posture information and the computer program information, and dealing with the situations of information loss, consistency, and conflict, this solution can more comprehensively and accurately judge the type of the user's current work task, providing more accurate input for the subsequent adaptive adjustment of the office chair.

[0031] In some specific embodiments, the image processing module continuously acquires the user's body image data stream. The image processing module performs pose recognition processing on the image data stream, extracts the positions of the key points of the user's arms and head, and calculates the arm pose parameters and head pose parameters. For example, calculate the head pitch angle and the angle of the arm relative to the torso. The pose judgment module receives the pose parameters and queries the preset pose-task type mapping table. For example, a head pitch angle greater than 15 degrees and a bent arm may be mapped to a "reading" task. At the same time, the computer information analysis module obtains the list of currently running programs and the window focus status of each program through the operating system API. The program type judgment module identifies the program type, such as identifying a browser, a word processing software, a spreadsheet software, etc. The program-task mapping module queries the preset program type-task type mapping table to preliminarily judge the task type of each program. For example, when the "WINWORD.EXE" process is identified, it is mapped to a "document editing" task. If a browser process is detected, the current tab URL is further obtained and the domain name is parsed. For example, when the "zoom.us" domain name is identified, it is mapped to an "online meeting" task. The task integration module receives the preliminary results of the pose judgment module and the preliminary results of the program-task mapping module. If there is no result in the pose judgment, the program task result is directly adopted. If both have results and they are consistent, the consistent result is adopted. If they are inconsistent, for example, the pose judgment is "reading" and the program judgment is "document editing", the task integration module selects "document editing" as the final task type according to the preset priority rules (for example, the program judgment priority is higher than the pose judgment). This final task type is sent to the adjustment parameter acquisition module to determine the adjustment parameters of the office chair.

[0032] In certain embodiments, the specific steps in step S2 include: S21. Detect the types of currently running programs, and judge the task types of each running program according to the preset mapping relationship between program types and task types; S22. Obtain the number of currently running programs and determine the window focus status of each running program; S23. If the number of running programs is greater than 1, judge whether there is a running program with a focus status according to the window focus status of each running program; if so, use the task type corresponding to the running program with a focus status as the preliminary judgment result of step S2; otherwise, according to the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary judgment result of step S2; S24. If the number of running programs is equal to 1, use the task type corresponding to this running program as the preliminary judgment result of step S2.

[0033] When analyzing computer information, this method first executes step S21 to detect the currently active program processes in the operating system and identify the type identifier of each program. According to the correspondence table between program types and task types stored in the system, a preliminary task type is associated with each identified program. Subsequently, step S22 is executed to count the total number of currently running programs and query the current status of each program window through the operating system interface to determine whether it is in the focus position of user interaction. When the number of programs obtained in step S22 exceeds one, the judgment process of step S23 is entered. This process first checks whether there is a program that has obtained the window focus. If so, the task type associated with the focused program is selected as the preliminary judgment result based on the computer information. If there are multiple programs but no program has obtained the focus, then according to the preset program importance sorting rule, the task type associated with the program with the highest priority is selected as the preliminary judgment result. When the number of programs obtained in step S22 is only one, step S24 is executed, and the task type associated with the only running program is directly used as the preliminary judgment result based on the computer information. These steps work together to identify the task type representing the nature of the user's current activity from the computer operating environment.

[0034] Specifically, for the problem that when analyzing computer information to judge the user's task type, running multiple programs on the computer at the same time may lead to unclear judgment, this solution provides a processing mechanism. This mechanism identifies the types of all running programs and associates preliminary task types through step S21. Then, step S22 obtains the number of programs and the window focus status, which reflect the user's interaction with the programs. When the number of programs is greater than one, step S23 uses the focus status as the primary judgment basis and preferentially selects the task type corresponding to the focused program, which reflects the user's current direct operation intention and improves the judgment accuracy. If there is no focused program, then the preset program priority is used for inference, providing a judgment strategy in the absence of clear interaction. When the number of programs is equal to one, step S24 directly determines the task type, simplifying the judgment process. By combining program types, quantities, focus status, and priorities, this solution can identify the user's current main task type in a multi-program environment and provide input information for the subsequent adaptive adjustment of the office chair.

[0035] In some specific embodiments, the system detects that there are three programs currently running on the user's computer: a text editor, a spreadsheet program, and a browser. In step S21, the system identifies that the text editor corresponds to the "document editing" task type, the spreadsheet program corresponds to the "data analysis" task type, and the browser corresponds to the "web browsing" task type. In step S22, the system determines that the number of running programs is 3 and detects that the browser window is currently in the focus state. Since the number of programs is greater than 1 and there is a focused program, step S23 is executed. According to the focus state judgment rule, the system selects the "web browsing" task type corresponding to the browser as the preliminary judgment result based on the computer information. Thus, the system obtains preliminary judgment information that the user may currently be performing the "web browsing" task, and this information can be used for subsequent comprehensive judgment of the task type.

[0036] It should be noted that the specific implementation of the "window focus state" can be obtained by calling the API (Application Programming Interface) provided by the operating system. For example, in the Windows system, functions such as "Get Foreground Window" can be used to obtain the handle of the currently focused window, and then obtain the corresponding program information.

[0037] In some embodiments, the specific steps in step S21 include: S211. Detect the type of the currently running program. For a running program of the browser type, through the process ID, call the operating system API to obtain the URL address of the current tab page under this browser program; S212. By parsing the URL address of the current tab page, extract the domain name information in the URL, and based on the mapping relationship between the domain name and the task type preset for the browser program, if the domain name information matches the preset domain name of the video website, then judge that the task type of this browser program is "watching video"; if the domain name information matches the preset domain name of the online meeting platform, then judge that the task type of this browser program is "online meeting"; otherwise, mark the task type of this browser program as "web browsing".

[0038] Detect the type of the currently running program. When it is identified that the running program is a browser, obtain the URL address of the current active tab page of this browser program. The acquisition of the URL address is achieved by calling the application programming interface provided by the operating system, and this call process uses the process identifier of the browser program as the input parameter. After receiving the obtained URL address, parse it to extract the domain name information. The extraction of the domain name information is achieved by processing the URL string. The extracted domain name information is then compared with the preset domain name list. The preset domain name list contains the domain names of known video websites and online meeting platforms.

[0039] Specifically, in actual applications, simply identifying the program type (such as "browser") may not be precise enough because a user may perform multiple tasks in a browser (such as browsing the web, watching videos, and participating in online meetings). This embodiment aims to improve the accuracy of determining the type of user work tasks by analyzing computer information, especially for browser programs. First, the system detects the type of program currently running. When it detects that the running program is a browser, the system uses the process identifier of the browser program to call the API provided by the operating system to obtain the URL address of the currently active tab in the browser. After obtaining the URL address, the system parses the URL address and extracts the domain name information from it. The extracted domain name information is then used to determine the specific activity that the user is performing in the browser. The system maintains a preset mapping table of domain names and task types. If the extracted domain name matches any of the domain names in the preset video website domain name list, the system determines that the task type of the browser program is "watching videos". If the extracted domain name matches any of the domain names in the preset online meeting platform domain name list, the system determines that the task type of the browser program is "online meeting". If the extracted domain name does not match any of the above lists, the system marks the task type of the browser program as "browsing the web". In this way, the system can distinguish whether the user is watching videos, participating in an online meeting, or performing general web browsing when using the browser, thereby obtaining more accurate user task information and providing more accurate input for the subsequent adaptive adjustment of the office chair.

[0040] In some specific embodiments, when the system detects that the user is running a browser program, such as the Chrome browser with a process ID of 1234, the system calls the operating system API, inputs the process ID 1234, and obtains the URL address of the currently active tab, such as "https: / / meet.google.com / abc-defg-hij". The system parses the URL address and extracts the domain name information "meet.google.com". The system queries the preset mapping table of domain names and task types and finds that "meet.google.com" matches the preset online meeting platform domain name list. Thus, the system determines that the task type of the Chrome program is "online meeting". If the obtained URL is "https: / / www.bilibili.com / video / BV1xxxx", the system parses and extracts the domain name "bilibili.com" and finds that it matches the preset video website domain name list, then determines that the task type is "watching videos". If the URL is "https: / / www.example.com / news", the system parses and extracts the domain name "example.com" and finds that it does not match either the preset video or meeting domain name list, then determines that the task type is "browsing the web".

[0041] In some embodiments, based on the task type, the steps of obtaining adjustment parameters of multiple adjustment components of the office chair according to body shape feature data and body posture information data include: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support protrusion adjustment range, armrest height adjustment range, and armrest spacing adjustment range corresponding to the current task type; there is a mapping relationship between the task type and the above adjustment ranges, and the above adjustment ranges are associated with the body shape feature data and body posture information data; According to the body shape feature data, calculate the height parameter, and determine the target seat height value within the seat height adjustment range based on the height parameter; calculate the weight parameter, and determine the target lumbar support protrusion value within the lumbar support protrusion adjustment range based on the weight parameter; calculate the shoulder width parameter, and determine the target armrest spacing value within the armrest spacing adjustment range based on the shoulder width parameter; According to the body posture information data, quantify the trunk curvature and tilt angle, and determine the target backrest angle value within the backrest angle adjustment range based on the trunk curvature; based on the trunk tilt angle and the target seat height value, determine the target lumbar support position value within the lumbar support position adjustment range; determine the target armrest height value within the armrest height adjustment range based on the target seat height value.

[0042] This embodiment solves the problem of how to effectively combine task types, body size feature data, and body posture information data to determine the specific adjustment values of multiple office chair adjustment components. The method first looks up the adjustment ranges of the corresponding adjustment components from the preset mapping relationship according to the current task type, setting task-related boundaries for subsequent fine-tuning. The setting of these adjustment ranges takes into account the influence of body size feature data and body posture information data. Then, the method uses relatively stable parameters in the body size feature data, such as height, weight, and shoulder width, to calculate the target adjustment values directly related to body size, such as seat height, lumbar support protrusion, and armrest spacing, within the adjustment range determined by the task type. Thus, the adjustment results can adapt to the user's basic body size. Next, the method uses the parameters reflecting the dynamic changes of the user's current sitting posture in the body posture information data, such as torso curvature and tilt angle, to calculate the target adjustment values related to the sitting posture, such as backrest angle and lumbar support position, within the adjustment range determined by the task type. When determining the lumbar support position and armrest height, the already determined target seat height value is also considered, reflecting the linkage relationship between different adjustment components. Through the above step-by-step and interrelated calculation process, the method can calculate the adjustment values applicable to multiple adjustment components according to the user's current task, body size, and body posture, thereby converting various information into specific adjustment parameters to achieve the adaptive adjustment of the office chair. Thus, the support state of the office chair can better meet the user's current needs, reduce local pressure, and relieve muscle fatigue.

[0043] In some specific embodiments, it is assumed that the current task type is "reading a document". The system queries the preset mapping relationship and determines that the seat height adjustment range corresponding to the "reading a document" task type is 42 cm - 52 cm, the backrest angle adjustment range is 105 degrees - 120 degrees, the lumbar support position adjustment range is 18 cm - 25 cm (measured from the seat surface), the lumbar support protrusion adjustment range is 2 cm - 4 cm, the armrest height adjustment range is 22 cm - 28 cm (measured from the seat surface), and the armrest spacing adjustment range is 45 cm - 55 cm. It is assumed that based on the body type characteristic data, the 70th percentile of the height distribution corresponding to the user's height parameter, the 60th percentile of the weight distribution corresponding to the weight parameter, and the 80th percentile of the shoulder width distribution corresponding to the shoulder width parameter are calculated. Based on the height parameter, within the range of 42 cm - 52 cm, the target seat height value is calculated as 42 + (52 - 42) * 0.7 = 49 cm. Based on the weight parameter, within the range of 2 cm - 4 cm, the target lumbar support protrusion value is calculated as 2 + (4 - 2) * 0.6 = 3.2 cm. Based on the shoulder width parameter, within the range of 45 cm - 55 cm, the target armrest spacing value is calculated as 45 + (55 - 45) * 0.8 = 53 cm. It is assumed that based on the body posture information data, the 80th percentile of the backrest angle adjustment range corresponding to the trunk curvature is quantified, the 75th percentile of the lumbar support position adjustment range corresponding to the combination of the trunk tilt angle and the target seat height value, and the 70th percentile of the armrest height adjustment range corresponding to the target seat height value. Based on the trunk curvature, within the range of 105 degrees - 120 degrees, the target backrest angle value is calculated as 105 + (120 - 105) * 0.8 = 117 degrees. Based on the trunk tilt angle and the target seat height value, within the range of 18 cm - 25 cm, the target lumbar support position value is calculated as 18 + (25 - 18) * 0.75 = 23.25 cm. Based on the target seat height value, within the range of 22 cm - 28 cm, the target armrest height value is calculated as 22 + (28 - 22) * 0.7 = 26.2 cm. Thus, it is determined that the seat height is 49 cm, the backrest angle is 117 degrees, the lumbar support position is 23.25 cm, the lumbar support protrusion is 3.2 cm, the armrest height is 26.2 cm, and the armrest spacing is 53 cm as the adjustment parameters.

[0044] Refer to the appendix Figure 2 , the present invention provides an office chair adaptive adjustment system, including: A first acquisition module 100, configured to acquire an image data stream of the user's body and extract the body type characteristic data and body posture information data of the user by processing the image data stream; A judgment module 200, configured to judge the task type of the user's current work task according to the image data stream and by analyzing the computer information; The second acquisition module 300 is configured to obtain adjustment parameters of multiple adjustment components of the office chair based on the task type, according to the body shape feature data and the body posture information data; The adjustment module 400 is configured to adjust the seat height, the backrest angle, the lumbar support position, the lumbar support convexity, the armrest height, and the armrest spacing by controlling the electric adjustment mechanism of the office chair according to the adjustment parameters.

[0045] In some embodiments, when the determination module 200 is used to determine the task type of the user's current work task according to the image data stream and by analyzing the computer information, it performs: S1. By analyzing the image data stream, extract the user's arm posture and head posture information; if the arm posture and head posture information are not detected, then perform step S2; otherwise, according to the preset mapping relationships between the arm posture and the task type, and between the head posture and the task type, preliminarily determine the task type of the user's current work task, and perform step S2; S2. By analyzing the computer information, detect the type of the currently running program, and according to the preset mapping relationship between the program type and the task type, preliminarily determine the task type of the user's current work task, and perform step S3; S3. When only the preliminary determination result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary determination results of step S1 and step S2 exist simultaneously, if the determination results of step S1 and step S2 are consistent, then determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rules, select the determination result with the higher priority from the determination results of step S1 and step S2 as the task type of the user's current work task.

[0046] In some embodiments, when the determination module 200 is used to detect the type of the currently running program by analyzing the computer information, and according to the preset mapping relationship between the program type and the task type, preliminarily determine the task type of the user's current work task, it performs: S21. Detect the type of the currently running program, and according to the preset mapping relationship between the program type and the task type, judge the task types of the respective running programs; S22. Obtain the number of currently running programs, and determine the window focus status of the respective running programs; S23. If the number of running programs is greater than 1, then according to the window focus status of the respective running programs, judge whether there is a running program with a focus status; if so, use the task type corresponding to the running program with the focus status as the preliminary determination result of step S2; otherwise, according to the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary determination result of step S2; S24. If the number of running programs is equal to 1, then use the task type corresponding to the running program as the preliminary judgment result in step S2.

[0047] In some embodiments, when the judgment module 200 is used to detect the type of the currently running program and determine the task type of each running program according to the preset mapping relationship between the program type and the task type, it executes: S211. Detect the type of the currently running program. For a running program of the browser type, use the process ID to call the operating system API to obtain the URL address of the current tab page under the browser program; S212. By parsing the URL address of the current tab page, extract the domain name information in the URL, and based on the preset mapping relationship between the domain name and the task type for the browser program, if the domain name information matches the preset domain name of the video website, then determine that the task type of the browser program is "watching video"; if the domain name information matches the preset domain name of the online meeting platform, then determine that the task type of the browser program is "online meeting"; otherwise, mark the task type of the browser program as "browsing the web".

[0048] In some embodiments, when the second acquisition module 300 is used to obtain the adjustment parameters of multiple adjustment components of the office chair based on the task type according to the body shape feature data and the body posture information data, it executes: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support protrusion adjustment range, armrest height adjustment range, and armrest spacing adjustment range corresponding to the current task type; there is a mapping relationship between the task type and the above adjustment ranges, and the above adjustment ranges are associated with the body shape feature data and the body posture information data; According to the body shape feature data, calculate the height parameter, and determine the target seat height value within the seat height adjustment range based on the height parameter; calculate the weight parameter, and determine the target lumbar support protrusion value within the lumbar support protrusion adjustment range based on the weight parameter; calculate the shoulder width parameter, and determine the target armrest spacing value within the armrest spacing adjustment range based on the shoulder width parameter; According to the body posture information data, quantify the trunk curvature and tilt angle, and determine the target backrest angle value within the backrest angle adjustment range based on the trunk curvature; based on the trunk tilt angle and the target seat height value, determine the target lumbar support position value within the lumbar support position adjustment range; determine the target armrest height value within the armrest height adjustment range based on the target seat height value.

[0049] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0050] The above are only the embodiments of the present invention and are not intended to limit the protection scope of the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An adaptive adjustment method for an office chair, characterized in that, It includes the following steps: Obtain the image data stream of the user's body, and by processing the image data stream, extract the body shape feature data and body posture information data of the user; Based on the image data stream and by analyzing the computer information, determine the task type of the user's current work task; Based on the task type, obtain the adjustment parameters of multiple adjustment components of the office chair according to the body shape feature data and the body posture information data; According to the adjustment parameters, adjust the seat height, backrest angle, lumbar support position, lumbar support convexity, armrest height, and armrest spacing by controlling the electric adjustment mechanism of the office chair.

2. The office chair adaptive adjustment method according to claim 1, characterized in that, The step of determining the task type of the user's current work task based on the image data stream and by analyzing the computer information includes: S1. By analyzing the image data stream, extract the user's arm posture and head posture information; if the arm posture and head posture information are not detected, execute step S2; otherwise, based on the preset mapping relationships between arm postures and task types, and between head postures and task types, preliminarily determine the task type of the user's current work task, and execute step S2; S2. By analyzing the computer information, detect the type of the currently running program, and based on the preset mapping relationship between the program type and the task type, preliminarily determine the task type of the user's current work task, and execute step S3; S3. When only the preliminary judgment result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary judgment results of step S1 and step S2 exist simultaneously, if the judgment results of step S1 and step S2 are consistent, determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rules, select the judgment result with a higher priority from the judgment results of step S1 and step S2 as the task type of the user's current work task.

3. The office chair adaptive adjustment method according to claim 2, wherein The specific steps in step S2 include: S21. Detect the type of the currently running program, and based on the preset mapping relationship between the program type and the task type, judge the task types of each running program; S22. Obtain the number of currently running programs, and determine the window focus status of each running program; S23. If the number of running programs is greater than 1, then based on the window focus status of each running program, judge whether there is a running program with a focus status; if so, use the task type corresponding to the running program with a focus status as the preliminary judgment result of step S2; otherwise, according to the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary judgment result of step S2; S24. If the number of running programs is equal to 1, use the task type corresponding to this running program as the preliminary judgment result of step S2.

4. The office chair adaptive adjustment method according to claim 3, wherein, The specific steps in step S21 include: S211. Detect the type of the currently running program. For a running program of the browser type, through the process ID, call the operating system API to obtain the URL address of the current tab page under this browser program; S212. By parsing the URL address of the current tab page, extract the domain name information in the URL, and based on the mapping relationship between the domain name and the task type preset for the browser program, if the domain name information matches the preset domain name of the video website, then determine that the task type of the browser program is "watching videos"; if the domain name information matches the preset domain name of the online meeting platform, then determine that the task type of the browser program is "online meeting"; otherwise, mark the task type of the browser program as "browsing the web".

5. The office chair adaptive adjustment method according to claim 1, characterized in that The steps of obtaining the adjustment parameters of multiple adjustment components of the office chair based on the task type, according to the body shape feature data and the body posture information data include: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support protrusion adjustment range, armrest height adjustment range and armrest spacing adjustment range corresponding to the current task type; there is a mapping relationship between the task type and the above adjustment ranges, and the above adjustment ranges are associated with the body shape feature data and the body posture information data; According to the body shape feature data, calculate the height parameter, and determine the target seat height value within the seat height adjustment range based on the height parameter; calculate the weight parameter, and determine the target lumbar support protrusion value within the lumbar support protrusion adjustment range based on the weight parameter; calculate the shoulder width parameter, and determine the target armrest spacing value within the armrest spacing adjustment range based on the shoulder width parameter; According to the body posture information data, quantify the trunk curvature and tilt angle, and determine the target backrest angle value within the backrest angle adjustment range based on the trunk curvature; based on the trunk tilt angle and the target seat height value, determine the target lumbar support position value within the lumbar support position adjustment range; determine the target armrest height value within the armrest height adjustment range based on the target seat height value.

6. An office chair adaptive adjustment system, characterized in that, including: The first acquisition module is used to acquire the image data stream of the user's body, and by processing the image data stream, extract the body shape feature data and the body posture information data of the user; The judgment module is used to judge the task type of the user's current work task according to the image data stream and by analyzing the computer information; The second acquisition module is used to acquire the adjustment parameters of multiple adjustment components of the office chair based on the task type, according to the body shape feature data and the body posture information data; The adjustment module is used to adjust the seat height, backrest angle, lumbar support position, lumbar support protrusion, armrest height and armrest spacing by controlling the electric adjustment mechanism of the office chair according to the adjustment parameters.

7. The office chair adaptive adjustment system according to claim 6, characterized in that, When the judgment module is used to judge the task type of the user's current work task according to the image data stream and by analyzing the computer information, it executes: S1. By analyzing the image data stream, extract the user's arm posture and head posture information; if the arm posture and head posture information are not detected, then execute step S2; otherwise, according to the preset mapping relationship between the arm posture and the task type, and the head posture and the task type, preliminarily judge the task type of the user's current work task, and execute step S2; S2. By analyzing the computer information, detect the type of the currently running program, and based on the preset mapping relationship between the program type and the task type, preliminarily determine the task type of the user's current work task, and execute step S3; S3. When only the preliminary judgment result of step S2 exists, determine the result of step S2 as the task type of the user's current work task; when the preliminary judgment results of step S1 and step S2 exist simultaneously, if the judgment results of step S1 and step S2 are consistent, then determine the consistent result as the task type of the user's current work task; otherwise, according to the preset priority rule, select the judgment result with the higher priority from the judgment results of step S1 and step S2 as the task type of the user's current work task.

8. The office chair adaptive adjustment system according to claim 7, wherein When the judgment module is used to analyze the computer information, detect the type of the currently running program, and based on the preset mapping relationship between the program type and the task type, preliminarily determine the task type of the user's current work task, it executes: S21. Detect the type of the currently running program, and based on the preset mapping relationship between the program type and the task type, judge the task types of the respective running programs; S22. Obtain the number of currently running programs, and determine the window focus status of each running program; S23. If the number of running programs is greater than 1, then based on the window focus status of each running program, judge whether there is a running program with a focus status; If there is, use the task type corresponding to the running program with the focus status as the preliminary judgment result of step S2; otherwise, based on the preset program priority, use the task type corresponding to the running program with the highest priority as the preliminary judgment result of step S2; S24. If the number of running programs is equal to 1, use the task type corresponding to this running program as the preliminary judgment result of step S2.

9. The office chair adaptive adjustment system according to claim 8, wherein, When the judgment module is used to detect the type of the currently running program and judge the task types of the respective running programs based on the preset mapping relationship between the program type and the task type, it executes: S211. Detect the type of the currently running program. For a running program of the browser type, call the operating system API through the process ID to obtain the URL address of the current tab page under this browser program; S212. By parsing the URL address of the current tab page, extract the domain name information in the URL, and based on the preset mapping relationship between the domain name and the task type for the browser program, if the domain name information matches the preset video website domain name, then judge the task type of this browser program as "watching videos"; If the domain name information matches the preset online meeting platform domain name, then judge the task type of this browser program as "online meeting"; otherwise, mark the task type of this browser program as "browsing the web".

10. The office chair adaptive adjustment system according to claim 6, characterized in that, When the second acquisition module is used to obtain the adjustment parameters of multiple adjustment components of the office chair based on the task type, according to the body shape feature data and the body posture information data, it executes: Query the seat height adjustment range, backrest angle adjustment range, lumbar support position adjustment range, lumbar support protrusion adjustment range, armrest height adjustment range, and armrest spacing adjustment range corresponding to the current task type; there is a mapping relationship between the task type and the above adjustment ranges, and the above adjustment ranges are associated with body type characteristic data and body posture information data; Calculate the height parameter based on the body type characteristic data, and determine the target seat height value within the seat height adjustment range based on the height parameter; Calculate the weight parameter, and determine the target lumbar support protrusion value within the lumbar support protrusion adjustment range based on the weight parameter; Calculate the shoulder width parameter, and determine the target armrest spacing value within the armrest spacing adjustment range based on the shoulder width parameter; Quantify the trunk curvature and tilt angle according to the body posture information data, and determine the target backrest angle value within the backrest angle adjustment range based on the trunk curvature; Determine the target lumbar support position value within the lumbar support position adjustment range based on the trunk tilt angle and the target seat height value; Determine the target armrest height value within the armrest height adjustment range based on the target seat height value.

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