A human-machine ergonomics evaluation system based on intelligent interaction of AR glasses

Abstract

The present invention relates to a human-machine ergonomics evaluation system based on AR glasses intelligent interaction, the system comprising a real-time capture and analysis unit, a geometric ergonomics index evaluation unit, a load index analysis unit, a biomechanical index analysis unit, a physiological index analysis unit and a comprehensive evaluation unit; the geometric ergonomics index evaluation unit evaluates the influence of the interface layout and operation flow of AR glasses on the user interaction efficiency in combination with the geometric ergonomics index; the load index analysis unit evaluates the influence of the load index on the user fatigue; the biomechanical index analysis unit analyzes the influence of the user's posture and action on fatigue; the physiological index analysis unit directly reflects the user's fatigue degree by analyzing the user's physiological index; the comprehensive evaluation unit combines the primary and secondary index weighted formulas of the human ergonomics evaluation model to comprehensively evaluate the geometric ergonomics index, load index, biomechanical index and physiological index, and obtains the ergonomics evaluation result of the human-machine interaction.

Description

Technical Field

[0001] The present invention relates to the field of augmented reality technology, and in particular to a human-machine ergonomics evaluation system and method based on intelligent interaction of AR glasses. Background Art

[0002] With the advancement of science and technology, AR (augmented reality) technology has gradually penetrated into various fields. As the core equipment of AR technology, the interaction mode of AR glasses directly affects the user experience and efficiency. However, the ergonomics evaluation of the interaction mode of AR glasses is still imperfect, and there is a lack of systematic evaluation methods and tools. Therefore, it is particularly important to develop a human-machine ergonomics evaluation software system based on the intelligent interaction of AR glasses. Summary of the invention

[0003] The present invention aims to provide a human-machine ergonomics evaluation system and method based on intelligent interaction of AR glasses. The technical problems to be solved include at least how to comprehensively evaluate the ergonomics of the three interaction forms of AR glasses: eye movement, gesture and voice.

[0004] In order to achieve the above-mentioned object, the present invention provides a human-machine ergonomics evaluation system based on intelligent interaction of AR glasses, including a real-time capture and analysis unit, a geometric ergonomics index evaluation unit, a load index analysis unit, a biomechanical index analysis unit, a physiological index analysis unit and a comprehensive evaluation unit;

[0005] The real-time capture and analysis unit is used to capture and analyze the eye movement data, gesture data and voice interaction data of the AR glasses user in real time, and then used to analyze the user's interaction behavior and status;

[0006] The geometric ergonomics index evaluation unit is used to evaluate the impact of the interface layout and operation process of the AR glasses on the user interaction efficiency in combination with the geometric ergonomics index, and to provide feedback and iterative improvements based on the analysis results of the geometric ergonomics index, and to propose improvement suggestions for the interface layout and operation process;

[0007] The load index analysis unit is used to evaluate the impact of the load index on user fatigue by analyzing the user's psychological and physiological load during the use of AR glasses;

[0008] The biomechanical index analysis unit is used to analyze the influence of the user's posture and movement on fatigue during the use of AR glasses;

[0009] The physiological index analysis unit is used to directly reflect the user's fatigue level in the process of using the AR glasses by analyzing the user's physiological indexes in the process of using the AR glasses;

[0010] The comprehensive evaluation unit is used to combine the weighted formula of the first and second level indicators of the human ergonomics evaluation model to comprehensively evaluate the geometric ergonomics indicators, load indicators, biomechanical indicators and physiological indicators to obtain the ergonomics evaluation results of human-computer interaction.

[0011] Preferably, the eye movement data includes gaze point position, gaze time, blinking frequency, pupil diameter and sight track, which are used to evaluate the user's sight distribution, fatigue level and convenience of interface layout.

[0012] Preferably, the gesture data covers finger position and movement trajectory, gesture type, operation speed, strength and pressure, to help understand the user's operation fluency and interaction method.

[0013] Preferably, the voice interaction data includes voice instruction content, speech speed, volume, voice pauses and emotional characteristics, which are used to reflect the user's operational needs, tension level and emotional state.

[0014] Preferably, the real-time capture and analysis unit is integrated with the AR glasses hardware device to obtain the user's eye movement, gesture and voice data in real time through an API interface.

[0015] Preferably, the load index analysis unit reflects the user's psychological load by measuring the user's physiological indicators including heart rate and blood pressure.

[0016] Preferably, the biomechanical index analysis unit evaluates the effect of biomechanical index on fatigue by analyzing the user's biomechanical parameters including standing posture and walking style.

[0017] Preferably, the physiological index analysis unit reflects the user's fatigue level by measuring the user's physiological indexes including breathing rate, body temperature, heart rate and blood pressure.

[0018] Preferably, the comprehensive evaluation unit performs weighted calculation according to the importance and weight of each indicator to obtain an ergonomic evaluation result of human-computer interaction.

[0019] Preferably, the primary index weighting formula and the secondary index weighting formula are specifically:

[0020]

[0021] Wherein, m and n are both positive integers greater than or equal to 1; the comprehensive work efficiency score is the weighted result of each primary index.

[0022] Preferably, the specific method for determining the weight index includes the following steps:

[0023] The first step is to use the Delphi method, using multiple rounds of expert questionnaires to determine the list of primary and secondary indicators and reach a consensus on the preliminary weights;

[0024] The second step is to quickly sort or preliminarily assign weights through the order relationship method or expert weighting method before using the hierarchical analysis method to provide a basis for weight setting;

[0025] Step 3: Use the analytic hierarchy process to compare the results of the Delphi method and the ordinal relationship method accurately, and finally determine the weight of each primary and secondary indicator, and ensure the rationality of the weight through consistency test;

[0026] The fourth step is to apply the obtained weights to the weighted formula of the first-level indicators and the weighted formula of the second-level indicators, calculate the comprehensive ergonomic score, and obtain the ergonomic evaluation result of human-computer interaction.

[0027] The present invention also provides a human-machine ergonomics evaluation method based on AR glasses intelligent interaction, comprising the following steps:

[0028] S1, real-time capture and analysis of AR glasses users’ eye movements, gestures and voice interaction data;

[0029] S2. Combine geometric ergonomics indicators to evaluate the impact of AR glasses’ interface layout and operation process on user interaction efficiency;

[0030] S3. Evaluate the impact of load indicators on user fatigue by analyzing the psychological and physiological load of users when using AR glasses;

[0031] S4. Use biomechanical indicators to analyze the impact of user posture and movement on fatigue when using AR glasses;

[0032] S5. Introduce physiological indicators to directly reflect the user's fatigue level in the process of using AR glasses by analyzing the user's physiological indicators in the process of using AR glasses;

[0033] S6. Combined with the weighted formula of the first-level index and the weighted formula of the second-level index of the ergonomics evaluation model, a comprehensive evaluation is conducted on the geometric ergonomics index, load index, biomechanical index and physiological index to obtain the ergonomics evaluation results of human-computer interaction.

[0034] Preferably, in step S2, the interface layout, operation flow, etc. of the AR glasses are optimized and designed according to geometric ergonomic indicators.

[0035] Preferably, in step S2, the user's cognitive load is reduced by reducing the number of operation steps and optimizing the interface layout.

[0036] Preferably, in step S3, the psychological load of the user is reflected by measuring the user's physiological indicators including heart rate and blood pressure.

[0037] Preferably, in step S4, the influence of biomechanical indicators on fatigue is evaluated by analyzing the user's biomechanical parameters including standing posture and walking style.

[0038] Preferably, in step S5, the user's fatigue level is reflected by measuring the user's physiological indicators including breathing frequency, body temperature, heart rate and blood pressure.

[0039] Preferably, in step S6, a weighted calculation is performed according to the importance and weight of each indicator to obtain an ergonomic evaluation result of human-computer interaction.

[0040] Compared with the prior art, the human-machine ergonomics evaluation system based on AR glasses intelligent interaction of the present invention has the following advantages:

[0041] 1. Ability to comprehensively evaluate the ergonomics of the three interaction modes of AR glasses: eye movement, gesture and voice;

[0042] 2. Comprehensive evaluation is carried out by combining multiple ergonomic indicators to improve the accuracy and reliability of the evaluation results;

[0043] 3. It can avoid human errors caused by cumulative fatigue when wearing AR glasses;

[0044] 4. It has a wide range of application scenarios and can be applied to human-computer interaction evaluation in different fields and industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the specific implementation methods of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0046] Figure 1 It is a structural schematic diagram of the human-machine ergonomics evaluation system based on AR glasses intelligent interaction. DETAILED DESCRIPTION

[0047] The present invention is described in more detail hereinafter to facilitate understanding of the present invention.

[0048] like Figure 1 As shown, the human-machine ergonomics evaluation system based on intelligent interaction of AR glasses described in the present invention includes a real-time capture and analysis unit, a geometric ergonomics index evaluation unit, a load index analysis unit, a biomechanical index analysis unit, a physiological index analysis unit and a comprehensive evaluation unit.

[0049] The real-time capture and analysis unit is used to capture and analyze the eye movement data, gesture data and voice interaction data of the AR glasses user in real time; the data can be subdivided into a variety of specific information for analyzing the user's interactive behavior and status. Eye movement data includes gaze point position, gaze time, blinking frequency, pupil diameter and line of sight trajectory, which are used to evaluate the user's line of sight distribution, fatigue level and convenience of interface layout; gesture data covers finger position and movement trajectory, gesture type, operation speed, strength and pressure, which helps to understand the user's operation fluency and interaction method; voice interaction data includes voice command content, speech speed, volume, voice pauses and emotional characteristics, reflecting the user's operation needs, tension level and emotional state. By integrating these data, the system can more accurately analyze the user experience, optimize the interaction design, and improve the efficiency of human-computer interaction and user satisfaction.

[0050] The geometric ergonomic index evaluation unit is used to evaluate the impact of the interface layout, operation process, etc. of the AR glasses on the user interaction efficiency in combination with the geometric ergonomic index;

[0051] After capturing and analyzing the eye movement data, gesture data, and voice interaction data of AR glasses users in real time, the rationality of the interface layout is judged by analyzing the distribution of user gaze points, gaze time, and line of sight movement path. For example, frequent movement of the line of sight or long-term gaze at certain areas may indicate that the interface layout is unreasonable, the operation steps are complicated, or the key information is not clear enough. The convenience of the operation process and the rationality of the interface layout are evaluated by the trajectory, accuracy, and speed of hand movements. If the user's hand movements are frequent or the pause time is long when performing certain operations, it may indicate that the interface layout is unclear or the operation steps are not intuitive. Voice data can help analyze the user's feedback and response efficiency when using voice commands, and combine the frequency, content, and duration of voice interaction to determine the optimization needs of the voice interaction process.

[0052] Combined with the captured data, the geometric ergonomics evaluation unit can calculate some key indicators, such as gaze distance, gaze angle, distance from hand to target area, etc. These indicators are compared with the standard values ​​of ergonomics to determine the comfort and accessibility of the interface and operation. For example, if the gaze angle is too large or the hand operation range is too wide, it may cause fatigue in long-term operation, thereby reducing the interaction efficiency.

[0053] The interaction efficiency evaluation is based on eye movement data and gesture data to analyze which interface elements can be repositioned to reduce the distance of sight or hand movement. Commonly used functions are placed within the sight range or in a location that is easy to reach with gestures to reduce the user's operation steps. If the analysis results show that users have repeated gestures or unnecessary sight switching in certain operations, the operation process can be redesigned, such as reducing unnecessary steps or adding concise voice commands, to improve interaction efficiency.

[0054] According to the analysis results of the geometric ergonomics indicators, feedback and iterative improvements are made, and suggestions for improving the interface layout and operation process are put forward. For example, an improved interface prototype can be provided, and data capture and analysis can be performed again after adjusting the layout to form an iterative optimization process. In this process, the geometric ergonomics indicator evaluation unit can continuously collect new data and verify the effect of each adjustment until the optimal interaction efficiency is achieved. Through the above steps, the real-time capture and analysis unit and the geometric ergonomics indicator evaluation unit can work together to not only optimize the AR glasses interface and operation process, but also effectively improve the user's interaction efficiency.

[0055] The load index analysis unit is used to evaluate the impact of the user's psychological and physiological load on user fatigue by analyzing the user's psychological and physiological load during the use of AR glasses.

[0056] Eye movement data such as gaze frequency and gaze duration can reflect the user's attention needs. If the user has to stare at certain interface areas for a long time, it may mean that the task complexity is high, which increases the user's cognitive load. In addition, frequent switching of sight lines may indicate that the interface layout is unreasonable, resulting in an increase in the user's psychological load. Maintaining a certain perspective for a long time or frequently moving the sight line may cause eye fatigue, reflecting an increase in physiological load. By monitoring indicators such as blinking frequency, it is possible to further determine whether the user is in a state of visual fatigue.

[0057] Gesture data can reflect the user's psychological state through the speed and accuracy of the operation. Unsmooth operation or frequent click errors may be a sign of excessive psychological load, indicating that the task is too complex or the interface is not intuitive. The force, speed and frequency of repeated operations in gesture data can reflect the physiological burden of muscles. For example, long-term high-frequency operation may cause hand fatigue, while high-force operation may mean that the user is operating in a tense state, increasing the physiological load.

[0058] The frequency of commands, pause time, and emotional characteristics of voice in voice interaction can reflect the user's psychological state. If the user repeats commands many times or expresses impatience in the voice, it means that the interface may increase the user's cognitive load. Physiological load is usually not directly manifested in voice interaction, but if the interaction process is complex and frequent, it may lead to the accumulation of psychological pressure, thereby indirectly causing fatigue.

[0059] The biomechanical index analysis unit is used to analyze the impact of the user's posture, movements, etc. on fatigue when using the AR glasses.

[0060] Eye movement data can provide information about the user's gaze direction and angle, reflecting the user's head and neck posture during operation. If the user frequently moves his or her gaze up and down (for example, looking up or down), it may cause neck fatigue, indicating that the interface layout may not meet the natural viewing requirements. The distribution of gaze areas and gaze frequency in eye movement data can reflect the user's visual load. If a certain gaze direction is maintained for a long time, or the gaze area is switched frequently, it may put a burden on the eyes and neck muscles, thereby affecting the user's overall posture and comfort.

[0061] Gesture data can reflect the frequency, strength, and precision of user hand operations. If users need to frequently use strong or complex gestures during interaction, it may cause muscle fatigue in the arms, shoulders, or upper back. It can also reveal the user's operating space and arm range of motion. If the interaction requires a large range of arm movements or uncomfortable operating postures, it may cause muscle strain. This data helps identify whether the interface layout needs to be optimized to reduce arm fatigue.

[0062] Voice data can indirectly reflect the frequency of user posture changes. Users may remain still in a specific posture in order to clearly issue voice commands. If users frequently adjust their posture during voice interaction, it may indicate that the operating posture is not ergonomically designed. The speed, volume, or emotional changes in the voice can reflect the user's fatigue state. If the speed of speech slows down or the volume decreases, it may indicate that the user is in a state of fatigue, suggesting that the biomechanical posture or operating action needs to be optimized.

[0063] Through eye movement data, gesture data and voice data, the biomechanical index analysis unit can more comprehensively evaluate the impact of user posture and movement on fatigue when using AR glasses. The gaze direction and angle revealed by the eye movement data can identify whether the user is in an uncomfortable head and neck posture for a long time, reminding the design to optimize the viewing angle range. Gesture data reveals the user's operation frequency and strength, identifies whether the arm range of motion is reasonable, and avoids fatigue caused by large-scale and frequent operations. Voice data reflects the frequency of posture adjustments and signs of user fatigue, which can help improve the interface layout or adjust the triggering method of voice recognition to ensure that users operate smoothly in a natural posture. Through the analysis of these data, the biomechanical index analysis unit can evaluate and optimize the interface layout and operation process of AR glasses, allowing users to complete interactions in a natural and comfortable posture, reduce fatigue and improve the experience.

[0064] The physiological index analysis unit is used to directly reflect the user's fatigue level in the process of using the AR glasses by analyzing the user's physiological indexes in the process of using the AR glasses;

[0065] Fatigue usually affects the blinking frequency. For example, fewer or more blinks can reflect eye fatigue. By monitoring changes in blinking frequency, the system can assess the user's fatigue level. Staring at a fixed point for a long time or frequent eye movements may cause eye fatigue. Prolonged gaze time, frequent switching of sight lines, unstable gaze, etc. can all reflect the user's fatigue state under high physiological load. Under high cognitive load or fatigue, the pupil may dilate. Changes in pupil diameter can be used as a potential physiological indicator of fatigue and stress to identify user fatigue.

[0066] When a user is fatigued, their gestures may become slower and the force of the operation may decrease. The fluency and precision of hand movements are usually reduced. Changes in gesture speed and force can be used as a representation of physiological fatigue. Fatigue affects the accuracy of operation, so the error rate in gestures may increase, such as an increase in misoperations. The error frequency in gesture data can reflect the degree of fatigue, suggesting that the user may need to rest. At the same time, the user may reduce the frequency of repeated actions, and this indicator can be used to further judge the user's physiological load.

[0067] Fatigue usually causes slower speech and lower volume. If a user's speech speed slows down and their volume decreases during voice interaction, it means that their physiological state may be affected by fatigue. At the same time, the user's tone of voice may become stable and lack variation. By analyzing the stability of the tone of voice, signs of fatigue can be identified. When fatigued, users may forget the instructions they have issued or have difficulty giving instructions accurately, resulting in repeated instructions or intermittent speech. The repetition frequency and interruption of instructions can be used to assess the degree of fatigue.

[0068] Through indicators such as blinking frequency, gaze duration, and pupil changes in eye movement data, the system can detect the user's visual fatigue and infer the level of physiological load. The operation accuracy, speed, and strength of gesture data reflect the user's muscle fatigue state. For example, if the gesture is slow and unstable, it may indicate that the user's muscles are already fatigued. The speed, volume, and tone changes of voice data can indicate the user's overall fatigue state, especially repeated instructions and slow speech during the interaction process, indicating that the user may be in a cognitive and physiological state of fatigue. Through these data, the physiological indicator analysis unit can identify the user's fatigue state when using AR glasses, promptly prompt the user to rest or adjust the operation, and provide a basis for the system's interface and interaction optimization, thereby helping to reduce the user's physiological load and improve the user experience.

[0069] The comprehensive evaluation unit is used to combine the weighted formula of the first and second level indicators of the ergonomic evaluation model to calculate the comprehensive weight of each indicator in the multi-level indicator system to determine the degree of influence of each indicator on the overall evaluation result; comprehensively evaluate the geometric ergonomic indicators, load indicators, biomechanical indicators and physiological indicators, and obtain the ergonomic evaluation result of human-computer interaction by integrating information from multiple indicators or dimensions and comprehensively analyzing and evaluating the overall performance or effect of the system or solution.

[0070] The weighted formula for primary and secondary indicators means that the score of each primary indicator is the result of the weighted calculation of its secondary indicators, and the comprehensive work efficiency score is the result of the weighted calculation of each primary indicator. The specific weighted formula is as follows:

[0071] 1. Calculation of first-level indicator scores:

[0072]

[0073] 2. Calculation of comprehensive work efficiency score:

[0074]

[0075] There are generally several methods for weight index analysis: Delphi Method, Expert Weighting Method, Hierarchy Analysis Method (AHP Method), and G1 Method. In actual weight analysis, one or more weight analysis methods are generally used in combination. For example, Wang Yaping used the G1 method to allocate index weights in "Establishment and Weight Allocation of Ergonomic Index System for Individual Soldier Systems"; Xu Weitong determined the basic weights of each index based on the combination of G1 method and expert weighting method in "Research on the Construction of Human-Machine Ergonomics Evaluation Index System for Touch Display System in Civil Aircraft Cockpit"; Lian Jinqiang used the Delphi method and G1 method to allocate index weights in "Human-Machine Ergonomics Evaluation in Helicopter Concept Design".

[0076] This application uses the Delphi method + AHP as the dominant method, and the G1 method and the expert weighting method as auxiliary methods. The specific steps are to first use the Delphi method, use multiple rounds of expert questionnaires to determine the list of primary and secondary indicators, and reach a consensus on the preliminary weights. Before AHP, use the G1 method or the expert weighting method to quickly sort or preliminarily assign weights to provide a basis for weight setting. Use the AHP method to accurately compare the results obtained by the Delphi method and the G1 method, and finally determine the weights of each primary and secondary indicator, and ensure the rationality of the weights through consistency checks. The obtained weights are applied to the weighted formulas of the primary and secondary indicators, and the comprehensive ergonomic scores are calculated to obtain the ergonomic evaluation results of human-computer interaction.

[0077] This weight allocation scheme dominated by "Delphi method + AHP" and combined with "G1 method and expert empowerment method" is scientific, reasonable and efficient. The Delphi method collects expert consensus through multiple rounds of feedback to ensure that the indicator setting has a scientific basis and reduces deviations, while the paired comparison and consistency test of the AHP method further enhance the logic of weight allocation and is suitable for decision-making in complex multi-level indicator systems. In addition, as auxiliary methods, the G1 method and the expert empowerment method can quickly provide weight rankings in the preliminary stage, reduce the workload of AHP, and improve the flexibility and efficiency of the overall process, making them particularly suitable for multi-dimensional ergonomics evaluation.

[0078] In a specific embodiment, assuming that the ergonomics of an AR glasses is to be evaluated, it is assumed that the indicator weights have been obtained, and the first- and second-level indicators are divided and scored (Table 1). For example, the second-level indicators of the first-level geometric ergonomics indicators include accessibility, viewing angle comfort, operating space, and button and control size; the second-level indicators of the first-level load indicators include psychological load, physiological load, task complexity, and feedback response time; the second-level indicators of the first-level biomechanical indicators include posture comfort, movement frequency, arm and shoulder load, and stability requirements; the second-level indicators of the first-level physiological indicators include heart rate, blinking frequency, pupil diameter, and body temperature changes.

[0079] Table 1

[0080]

[0081]

[0082] (1) Geometric ergonomic score = (85 × 0.4) + (78 × 0.3) + (90 × 0.2) + (82 × 0.1)

[0083] Calculation results:

[0084] Geometric ergonomic score = 34 + 23.4 + 18 + 8.2 = 83.6

[0085] (2) Load efficiency score = (70 × 0.3) + (75 × 0.2) + (80 × 0.3) + (68 × 0.2)

[0086] Calculation results:

[0087] Load efficiency score = 21 + 15 + 24 + 13.6 = 73.6

[0088] (3) Biomechanical score = (82 × 0.3) + (76 × 0.3) + (78 × 0.2) + (82 × 0.2)

[0089] Calculation results:

[0090] Biomechanical score = 24.6 + 22.8 + 15.6 + 16 = 79

[0091] (4) Physiological index score = (74 × 0.4) + (88 × 0.2) + (70 × 0.2) + (72 × 0.2)

[0092] Calculation results:

[0093] Physiological index score = 29.6 + 17.6 + 14 + 14.4 = 75.6

[0094] The comprehensive work efficiency score is calculated using the first-level indicator weighted formula:

[0095] Comprehensive work efficiency score = (83.6×0.3)+(73.6×0.25)+(79×0.2)+(75.6×0.25)

[0096] Calculation results:

[0097] Comprehensive work efficiency score = 25.08 + 18.4 + 15.8 + 18.9 = 78.18

[0098] The final comprehensive ergonomic score is 78.18, which reflects the comprehensive ergonomic performance of AR glasses in terms of geometric ergonomics, load, biomechanics, and physiology. The higher the score, the better the system performs in meeting ergonomic requirements. Through such a hierarchical and weighted calculation method, the comprehensive ergonomic evaluation model can provide objective data support for further optimizing the design of AR glasses.

[0099] Preferably, the real-time capture and analysis unit is integrated with the AR glasses hardware device to obtain the user's eye movement, gesture and voice data in real time through an API interface.

[0100] Preferably, the load index analysis unit reflects the user's psychological load by measuring physiological indicators such as the user's heart rate and blood pressure.

[0101] Preferably, the biomechanical index analysis unit evaluates the impact of the user's standing posture, walking style and other biomechanical parameters on fatigue by analyzing them.

[0102] Preferably, the physiological index analysis unit reflects the user's fatigue level by measuring physiological indexes such as the user's breathing rate, body temperature, heart rate and blood pressure.

[0103] Preferably, the comprehensive evaluation unit performs weighted calculation according to the importance and weight of each indicator to obtain an ergonomic evaluation result of human-computer interaction.

[0104] The weighted formula for the primary and secondary indicators described in this application is a weight calculation method for a multi-level indicator system, which comprehensively evaluates the relative importance of each indicator to the overall goal by assigning weights to the primary and secondary indicators. The above indicators are comprehensively evaluated, specifically, by summarizing and analyzing multiple indicators or factors to comprehensively measure the overall performance of the system or solution, and obtain the ergonomic evaluation results of human-computer interaction.

[0105] The present invention also provides a human-machine ergonomics evaluation method based on AR glasses intelligent interaction, comprising the following steps:

[0106] S1, real-time capture and analysis of AR glasses users’ eye movements, gestures and voice interaction data;

[0107] S2. Combine geometric ergonomics indicators to evaluate the impact of AR glasses’ interface layout and operation process on user interaction efficiency;

[0108] S3. Introduce load indicators to analyze the psychological and physiological load of users in the process of using AR glasses and evaluate their impact on user fatigue. For example, the psychological load can be reflected by measuring physiological indicators such as the user's heart rate and blood pressure.

[0109] S4. Use biomechanical indicators to analyze the impact of user posture, movement, etc. on fatigue when using AR glasses. For example, the impact on fatigue can be evaluated by analyzing biomechanical parameters such as the user's standing posture and walking style.

[0110] S5. Introduce physiological indicators, such as breathing rate, temperature, etc., to directly reflect the user's fatigue level when using AR glasses. For example, the user's fatigue level can be reflected by measuring physiological indicators such as heart rate and blood pressure.

[0111] S6. Combine the weighted formula of the first and second level indicators of the ergonomics evaluation model to comprehensively evaluate the above indicators and obtain the ergonomics evaluation results of human-computer interaction. For example, weighted calculation can be performed according to the importance and weight of each indicator to obtain the ergonomics evaluation results of human-computer interaction.

[0112] Preferably, in step S1, the user's eye movement, gesture and voice data are acquired in real time through the API interface of the AR glasses hardware device.

[0113] Preferably, in step S2, the interface layout, operation flow, etc. of the AR glasses are optimized and designed according to geometric ergonomic indicators to improve user interaction efficiency.

[0114] For example, based on geometric ergonomic indicators, analyze the user's viewing comfort and accessibility, and optimize the interface layout. Ensure that key interactive controls are placed within the user's natural viewing angle to reduce excessive movement of the head and eyes. For example, place commonly used function buttons in the center of the user's field of view or where the fingers naturally reach to reduce arm extension and improve the convenience and comfort of operation; the user's psychological load and cognitive load during operation can also be analyzed through load indicators. For functions with high frequency operations, the process steps can be simplified and unnecessary prompts or interaction steps can be reduced. For example, use quick gestures or voice commands to replace multi-step operations to reduce the user's psychological load and improve operation efficiency. In addition, based on biomechanical indicators, analyze the user's posture comfort while wearing AR glasses. For example, if it is found that the user keeps looking up or looking down for a long time, the height of the display content of the AR glasses can be adjusted to ensure that the user operates at a natural head and neck angle to reduce fatigue caused by bad posture; use load and physiological indicators to analyze the user's reaction speed and fatigue to interface feedback. For example, for buttons with touch feedback, appropriate tactile feedback or visual feedback can be set to ensure that the user can perceive the system response in time and reduce cognitive load. In addition, by monitoring the user's eye movement data, timely rest reminders are provided to prevent fatigue caused by long-term use. The above optimization design can effectively optimize the interface layout and operation process of AR glasses, thereby improving the efficiency and comfort of user interaction and improving user interaction efficiency.

[0115] Preferably, in step S2, the user's cognitive load is reduced by reducing the operation steps, optimizing the interface layout, etc.

[0116] The human-machine ergonomics evaluation system based on intelligent interaction of AR glasses described in the present invention can comprehensively evaluate the ergonomics of three interaction forms of AR glasses: eye movement, gesture and voice, and perform a comprehensive evaluation in combination with multiple ergonomics indicators.

[0117] The above describes the preferred embodiments of the present invention, but it is not intended to limit the present invention. Those skilled in the art may make improvements and changes to the embodiments disclosed herein without departing from the scope and spirit of the present invention.

Claims

1. A human-machine ergonomics evaluation system based on AR glasses intelligent interaction, characterized in that: The human-machine ergonomics evaluation system based on intelligent interaction of AR glasses includes a real-time capture and analysis unit, a geometric ergonomics index evaluation unit, a load index analysis unit, a biomechanical index analysis unit, a physiological index analysis unit and a comprehensive evaluation unit; The real-time capture and analysis unit is used to capture and analyze the eye movement data, gesture data and voice interaction data of the AR glasses user in real time, and to analyze the interaction data of the user's gaze, operation and command behavior in the interface, to help evaluate the user's operation intention, concentration and interaction efficiency; The geometric ergonomics index evaluation unit is used to evaluate the impact of the interface layout and operation process of the AR glasses on the user interaction efficiency in combination with the geometric ergonomics index, and to provide feedback and iterative improvements based on the analysis results of the geometric ergonomics index, and to propose improvement suggestions for the interface layout and operation process; The load index analysis unit is used to evaluate the impact of the load index on user fatigue by analyzing the user's psychological and physiological load during the use of AR glasses; The biomechanical index analysis unit is used to analyze the influence of the user's posture and movement on fatigue during the use of AR glasses; The physiological index analysis unit is used to directly reflect the user's fatigue level in the process of using the AR glasses by analyzing the user's physiological indexes in the process of using the AR glasses; The comprehensive evaluation unit is used to combine the primary index weighted formula and the secondary index weighted formula of the human ergonomics evaluation model to comprehensively evaluate the geometric ergonomics index, load index, biomechanical index and physiological index to obtain the ergonomics evaluation result of human-computer interaction.

2. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The real-time capture and analysis unit is integrated with the AR glasses hardware device to obtain the user's eye movement, gesture and voice data in real time through the API interface.

3. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The load index analysis unit reflects the user's psychological load by measuring the user's physiological indexes including heart rate and blood pressure.

4. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The biomechanical index analysis unit evaluates the influence of biomechanical index on fatigue by analyzing the biomechanical parameters of the user including the standing posture and walking style.

5. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The physiological index analysis unit reflects the user's fatigue level by measuring the user's physiological indexes including breathing frequency, body temperature, heart rate and blood pressure.

6. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The comprehensive evaluation unit performs weighted calculation according to the importance and weight of each indicator to obtain the ergonomic evaluation result of human-computer interaction.

7. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The eye movement data includes gaze point position, gaze time, blinking frequency, pupil diameter and line of sight trajectory, which are used to evaluate the user's line of sight distribution, fatigue level and convenience of interface layout.

8. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The gesture data includes finger position and movement trajectory, gesture type, operation speed, strength and pressure, which helps to understand the user's operation fluency and interaction method.

9. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1 is characterized in that: The voice interaction data includes voice instruction content, speech speed, volume, voice pauses and emotional characteristics, which are used to reflect the user's operational needs, tension level and emotional state.

10. The human-machine ergonomics evaluation system based on AR glasses intelligent interaction according to claim 1, characterized in that: The first-level index weighting formula and the second-level index weighting formula are specifically: Wherein, m and n are both positive integers greater than or equal to 1; the comprehensive work efficiency score is the weighted result of each primary index.

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