626 results about "Eye movement" patented technology

The invention belongs to the field of virtual reality teaching application, and provides a teaching atmosphere automatic adaptation method and system in an immersive teaching environment. The method comprises the following steps: acquiring an eye movement image; recognizing a fixation point; carrying out ROI tracking; carrying out ROI boundary fusion; adaptively optimizing the object; adjusting the brightness of the ROI; and watching object interaction. According to the method, the immersion and interactivity of a future classroom can be improved, the use experience of an immersive virtual environment is facilitated, and deep fusion of an intelligent teaching environment and self cognition of a user is promoted.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention relates to the technical field of intelligent traffic. The driver state recognition and vehicle control method comprises the steps that driver monitoring information comprises eye movement information, voice information and steering wheel operation information, and the eye movement information, the voice information and the steering wheel operation information are input into a multi-modal fusion judgment model; the multi-modal fusion judgment model dynamically adjusts the weight of each modal feature based on the attention mechanism and in combination with preset driver modal preference parameters to obtain fusion feature representation data, determines driver state information based on the fusion feature representation data, matches a preset composite event recognition rule according to the driver state information, and judges whether the driver state information is abnormal or not. And if the driver state information represents any one of a fatigue state, a distraction state or an emotional stress state, determining a corresponding event triggering level, and generating corresponding vehicle control information based on the event triggering level. The method has the effect of improving the driving safety.

The invention discloses a VR panoramic glasses data synchronization and transmission method based on multi-sensor fusion, and relates to the technical field of computer network and multimedia real-time transmission. Time alignment and unified modeling are carried out on various sensor data such as eye movement, inertia measurement, gestures and environment through extended Kalman filtering; a user head position, a posture, a sight line direction and a gesture action are coded into a single user behavior vector, so that a subsequent system does not face original sensor streams with different formats any more, but makes a decision based on a behavior space with a clear structure, thereby remarkably reducing posture jitter and sight line deviation accumulative errors; a long short-term memory network is introduced to carry out time sequence modeling on behavior vectors and environment contexts, interactive hotspot areas in a virtual scene are predicted, and hotspot distribution is mapped into priority weights of different data streams, so that sensor streams related to fast actions in areas near a sight line can still obtain a relatively high service level when the bandwidth is insufficient.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

Disclosed are an intelligent map-making system and device for a We-map, which mainly include an intelligent map-making system for a We-map and an intelligent making device on a mobile terminal. The system includes: a We-map natural interaction making module based on gesture interaction, voice interaction and eye movement interaction, a multi-modal interaction data fusion device, a user portrait building module, a We-map self-adaptive designer and a We-map intelligent generator. The device includes a We-map natural interaction interface designer, a We-map mapping interface optimizer, a We-map data processor, a We-map memory and a We-map user manager.

The invention discloses a vehicle-mounted automobile instrument control display system, which relates to the field of instrument control display and is used for acquiring face, eye movement, grip strength and physiological data, identifying a driving state, acquiring road, weather and traffic target information, analyzing an operation intention, predicting path conflicts and dynamically adjusting an instrument interface according to risks. According to the system, the driver state and environment information are collected, behavior prediction is combined, operation intention recognition and risk early warning are achieved, the content of an instrument interface can be dynamically adjusted before danger occurs, multi-channel prompt intervention is conducted through voice and light, the driving safety is improved, and compared with the prior art, the system has the advantages of being high in practicability and easy to popularize. The vehicle-mounted instrument control system has the advantages of prospective early warning, interface self-adaption and personalized prompt, and the intelligent and situation-aware vehicle-mounted instrument control function is achieved.

The invention relates to the technical field of intelligent wearing monitoring, in particular to a multifunctional head-mounted device for improving anxiety and depression symptoms in the perioperative period, which comprises a main body base, an electric treatment assembly and a power supply assembly are arranged on the main body base, extending ear hooks are arranged at two ends of the main body base, binding straps are arranged on the extending ear hooks, and a nose pad block is arranged at the bottom of the main body base. The electric treatment assembly is in signal connection with an evaluation and control system, heart rate monitoring assemblies are arranged on the extension ear hooks, and an eye movement monitoring assembly is arranged at the bottom of the main body base; a displacement monitoring assembly is arranged on the main body base, and a respiration monitoring assembly is arranged on the nose pad block. According to the invention, physiological parameter monitoring, anxiety and depression assessment and percutaneous nerve stimulation treatment functions are integrated, and multi-node fixed design and real-time dynamic data processing are combined, so that redundant steps depending on preposed medical assessment in the traditional technology are omitted, doctors can remotely and accurately adjust personalized schemes based on dynamic data, and the accuracy of diagnosis and treatment is improved. The anxiety depression intervention efficiency and reliability in the perioperative period are improved.

The invention discloses a flight trainee attention and error behavior detection method based on a multi-modal physiological signal, and the method comprises the steps: obtaining the electrocardiogram data, eye movement data and electroencephalogram data of a flight trainee in a flight training process, and subjective evaluation data; based on subjective evaluation data, dividing flight student attention distribution conditions into a high grade, a middle grade and a low grade by adopting a clustering algorithm; performing feature optimization processing on the physiological data, and constructing a physiological feature attention level flight behavior associated data set; constructing an attention distribution level judgment model and a flight error behavior detection and early warning model; and outputting an attention level judgment result and a flight error behavior detection and early warning result. According to the method, association mapping of the multi-modal physiological signals and flight performance is realized, an attention grading standard for a flight training specific scene is established, and conversion from passive response to active early warning is realized.

The invention relates to a cognitive load monitoring method based on multi-source physiological information fusion. The cognitive load monitoring method comprises the following steps that 1, physiological information data collection is conducted through a multi-source data collection module; 2, performing data processing through a data preprocessing module to obtain preprocessed electroencephalogram, eye movement and electrocardio data; 3, performing data layer fusion, feature extraction and feature layer fusion on the preprocessed data to obtain a feature layer fusion matrix; and step 4, constructing a CNN-BiLSTM-Transform hybrid neural network model, carrying out decision fusion, and carrying out iterative optimization on the model through evaluation. According to the method, the core basis problem of multi-source physiological information data fusion is solved, and a high-quality data basis is provided for subsequent feature extraction and model training; the accuracy and environmental adaptability of state evaluation are greatly improved; and the sensitivity and the recognition precision of the model to the dynamic change of the cognitive load are effectively improved.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The invention discloses an ADHD screening method and system based on mobile terminal eye movement tracking and a multi-modal agent. The method comprises the following steps: (1) collecting and preprocessing mobile terminal eye movement data; (2) performing fixation point estimation based on a lightweight MGazeNet model; (3) eye movement feature extraction based on a multi-normal form interaction task; and (4) carrying out comprehensive diagnosis analysis based on machine learning and a large model agent. The invention further provides an ADHD screening system based on mobile terminal eye movement tracking and the multi-modal intelligent agent. The ADHD screening system sequentially comprises the following modules: (1) a front-end data acquisition and preprocessing module; (2) an end side eye movement tracking reasoning module; (3) an interactive task control and feature calculation module; and (4) a multi-mode intelligent diagnosis and analysis module. The ADHD screening method effectively solves the problems that traditional ADHD screening depends on expensive professional equipment and subjective scales, so that the popularity rate is low, and objectivity is poor, screening portability and diagnosis objective accuracy are improved, and the intelligent level of ADHD auxiliary diagnosis and user interaction experience are improved.

The invention relates to the technical field of mobile equipment display, in particular to a self-adaptive brightness adjusting method and system for a mobile phone backlight plate. The method comprises the following steps: acquiring an environment light and shadow dynamic information set and user behavior intention information, and analyzing cross-modal fusion deduction information of light and shadow artistic features and user task intention based on the environment light and shadow dynamic information set in combination with the user behavior intention information to obtain a light and shadow-user situation information set; based on the light and shadow-user situation information set, analyzing a dynamic tension relationship among an art immersion demand, visual task efficiency and visual health, and balancing an active light and shadow fusion strategy, user instantaneous discomfort and long-term eye movement load to obtain a situation backlight fusion strategy set; and based on the contextualized backlight fusion strategy set, flexible intervention matched with the artistic atmosphere and the instantaneous demand of the user is executed on the mobile phone backlight. The smooth backlight change is ensured, the matching with the artistic atmosphere and the instantaneous demand of the user is realized, and the comfort and satisfaction of the user are improved.

The invention relates to an English text auxiliary teaching method and system based on AI vision. The method comprises the following steps: collecting a dynamic eye movement track, a mouth shape change video stream and micro-expression time sequence data when a student reads, and generating a dynamic behavior feature vector by using a convolutional neural network; and carrying out multi-dimensional matching on the vector and a preset pronunciation standard model, accurately positioning a pronunciation deviation region and an understanding difficulty point, and carrying out classification by virtue of a support vector machine to obtain a learning state label. And extracting a high-frequency deviation mode from the tag, constructing a comprehensive behavior matrix through association of a clustering algorithm and an eye movement backtracking trajectory, calculating a teaching level evaluation value, finally predicting a learning trend and determining a resource allocation weight by combining historical evaluation data and adopting a linear regression model, and optimizing and generating a personalized teaching plan. By adopting the method, pronunciation deviation and understanding disorder in English reading can be accurately positioned, so that the resource allocation weight is adaptive to the individual learning track of students, and a data-driven technical path is provided for English personalized teaching.

The invention discloses a paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.

The embodiment of the invention provides an emotion detection method based on a multi-mode electroencephalogram eye movement signal. According to the method, a dual-path electroencephalogram modal encoder is used for determining time sequence characteristics of an electroencephalogram in a time domain path, determining frequency domain characteristics of an amplitude spectrum of the electroencephalogram in a frequency domain path, and obtaining dual-path time domain and frequency domain fused electroencephalogram characteristics based on the time sequence characteristics and the frequency domain characteristics; the dual-path eye movement modal encoder is used for determining pupil features of the eye movement data in the pupil diameter path, determining eye movement fixation features of the eye movement data in the fixation point path, and obtaining fused eye movement features based on the pupil features and the eye movement fixation features; and the multi-modal expert mixing module is used for dynamically performing modal calling on the multi-modal mixed characteristics which are subjected to modal alignment and are fused with the electroencephalogram characteristics and the eye movement characteristics, performing emotion detection by utilizing an expert with a modal corresponding to the multi-modal mixed characteristics, and outputting an emotion classification result. According to the embodiment of the invention, alignment is realized through multi-task pre-training, and the depressive emotion is accurately recognized.

The invention discloses an orthopedic surgery navigation drilling and implant placement intention real-time optimization method based on multi-modal perception, and relates to the technical field of computer-aided surgery, man-machine interaction and intelligent medical equipment. Multi-modal signals such as electroencephalogram, eye movement and myoelectricity of a doctor are collected in real time through XR glasses and an intelligent surgical drill; and inputting the pre-trained micro-world model to generate a current intention vector and predict a future bone structure trajectory. The intention level is judged by calculating the energy distance between the intention and the master template and an emotional value function: when the intention meets the master level condition, the system automatically generates an XR green optimal path and assists in operation; and when the intention is identified as the risk intention, triggering the skykeeper system to lock the equipment and send out an alarm. The system supports a data federation learning evolutionary model, and immediately destroys an original multi-mode signal after encoding to guarantee data security. According to the invention, the real-time monitoring, evaluation and guidance of the operation intention are realized, and the accuracy and safety of the operation are improved.

The invention discloses an intelligent cooperative control system and method for vestibular function inspection. The invention discloses an intelligent cooperative control system and method for vestibular function examination, and belongs to the technical field of medical instruments. The system comprises a cold and hot air perfusion module, a dynamic visual target module, a head posture detection module, a sight distance detection module, an eye movement acquisition module, a voice interaction module, an adjustable examination chair and a central controller. The central controller realizes closed-loop control based on multi-source sensing data: during a cold and hot test, if the elevation angle of the head deviates from 30 degrees + / -2 degrees, stimulation is paused and voice prompt is performed; during visual target testing, visual target parameters are automatically compensated according to the actually measured sight distance of 1.5 m + / -0.1 m so as to maintain a constant visual angle, and the upright state (90 degrees + / -2 degrees) of the head is verified; abnormities such as eye closing more than or equal to 2 seconds or head movement more than or equal to 5 degrees per second are monitored in the whole process, graded voice intervention is implemented, and the abnormities are paused. Body position conversion is guided by voice, multi-source data are synchronously stored, and peripheral and central function correlation analysis is supported. According to the invention, the accuracy and compliance of inspection can be improved.

The invention provides a learning input degree evaluation method and system based on reaction behaviors of human-computer interaction, and relates to the technical field of input degree evaluation, and the method comprises the steps: data collection and synchronous alignment: achieving time sequence alignment through collecting physiological behavior data; feature engineering and scene adaptive modeling are carried out, and a deep learning network is constructed to extract scene sensitive features; performing multi-modal fusion and cognitive state decoding, fusing eye movement, electroencephalogram and behavior data, and outputting cognition, emotion and behavior input degree indexes; performing adaptive intervention decision and execution, and dynamically selecting content highlight or visual guidance based on reinforcement learning; personal baseline evolution and model online updating are carried out, and continuous optimization of the system is realized. The system comprises a multi-mode fusion module, a cognitive state decoding module and the like. According to the method, the cognitive response is actively stimulated through standardized interaction tasks, the problem of data splitting is solved, a multi-level evaluation system is constructed in combination with the education theory, the teaching effectiveness is improved, an evaluation intervention closed loop is formed, and the evaluation accuracy and the personalized level are improved.

The invention belongs to the technical field of psychological health condition evaluation, and particularly relates to a psychological evaluation system and method based on multi-modal data fusion. The psychological assessment system based on multi-modal data fusion comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal feature fusion module, a psychological state analysis module, an AI interaction suggestion module and a system management security module. The psychological state is comprehensively evaluated in combination with multi-dimensional information of eye movement, text and voice; the intelligent dialogue analysis function is achieved through the AI interaction and suggestion module, evaluation is upgraded from static result output to dynamic interaction guidance, the psychological state of a user can be more accurately understood, and personalized support is provided.

A virtual eye test can be conducted to evaluate visual acuity and perception in a virtual reality (VR) environment. The test can be conducted using an electronic device that includes a head-mounted display (HMD) and a camera. The electronic device can generate a VR user interface corresponding to photorealistic virtual environment and render the VR user interface on the HMD. The electronic device can simulate one or more real-world scenarios and while simulating the one or more real-world scenarios, in real time, track eye movements and responses from the wearer for testing visual acuity and perception of the wearer.

The invention relates to the technical field of intelligent driving, and provides an intelligent driving method and system based on driver biofeedback. The method comprises the following steps: firstly, acquiring multi-mode biological signals such as heart rate, electrodermal response and eye movement track of a driver and a vehicle movement signal in real time through an in-vehicle sensor; carrying out cross-modal feature fusion and analysis on the signals by utilizing a driver state analysis model, carrying out parallel calculation on tensity, motion sickness and attention level indexes of the driver, and generating a quantized comfort level score; furthermore, in a trajectory planning stage, a comfort level score corresponding to each candidate trajectory is predicted through a personalized comfort level prediction model, the comfort level score is used as an optimization target parallel to safety and efficiency for comprehensive decision making, and finally an optimal trajectory is selected for execution. According to the invention, the automatic driving system can sense and respond to the subjective state of the driver in real time, the tension and motion sickness of passengers can be effectively relieved, and the riding comfort and personalized experience are improved.

The invention relates to the technical field of endoscope systems, in particular to a display control method, device and equipment of a multi-mode endoscope system.The method comprises the steps that a first image collected by the multi-mode endoscope system based on a first imaging mode is displayed; in response to the first mode switching operation, switching the multimode endoscope system from the first imaging mode to a second imaging mode; the first imaging mode is different from the second imaging mode; the first mode switching operation comprises eye movement operation; and displaying a second image acquired by the multimode endoscope system based on the second imaging mode. According to the display control method and device and the display control equipment, switching display of the multi-mode endoscope system in multiple different imaging modes can be achieved based on eye movement operation, the man-machine interaction efficiency in the mode switching process is improved, and the operation difficulty is reduced; meanwhile, the mode switching operation can be carried out in the video, so that the possibility of misoperation is reduced.

The invention discloses a safe takeover and man-machine cooperation automatic driving control method. The method comprises the following steps: acquiring a face image, eye movement data, a head posture, a sight direction, a blinking frequency and a steering wheel holding state of a driver in real time through a vehicle-mounted camera and a steering wheel torque sensor; the collected driver state information is input into a driver ability evaluation module constructed based on a time sequence model, a driver ability level C is output, and the ability level C is divided into four levels of C0 to C3; and acquiring current driving scene information through an environment sensing sensor carried by the vehicle. According to the invention, all-weather and full-scene environment understanding is realized, the camera provides semantic information of lane lines and traffic signs, the millimeter-wave radar can still accurately measure distance at rainy and foggy nights, the laser radar generates high-precision three-dimensional point cloud, depicts the contour of an obstacle, and injects prior knowledge into a high-precision map.

The invention provides an iris collection method and device based on eye movement prediction, equipment and a storage medium, and the method comprises the steps: collecting image frames and event streams in a mixed manner through an event camera and an RGB camera, obtaining eye feature point coordinates through key point detection, and converting the event streams into an event frame sequence; performing spatial-temporal feature extraction and filtering processing on the eye feature point coordinates and the event frame sequence by using a time sequence prediction network, and predicting eyeball positions and gazing directions at future moments; calculating the optimal optical axis direction of iris imaging according to the predicted eyeball position and gazing direction, and converting the optimal optical axis direction into control parameters such as a pan-tilt angle and a lens focal length of an imaging system; and synchronously adjusting the imaging system and triggering acquisition when the prediction time arrives. According to the method, active iris tracking acquisition is realized by predicting the eyeball movement track, the problem of response lag of traditional passive tracking is solved, and the success rate of iris acquisition is improved.

The invention particularly relates to a self-adaptive closed-loop brain-computer interface neural feedback training system, and relates to the technical field of brain-computer interfaces and neural feedback. A multi-modal feature extraction module; a coefficient fusion module; and a feedback training module. In the invention, a high-precision crystal oscillator clock is adopted to realize time synchronization of electroencephalogram, eye movement and behavior signals, fusion distortion caused by signal dislocation is thoroughly eliminated, and the three types of signals respectively cover cognitive states, visual attention and motion characteristics to form complementary state evaluation dimensions; a refined quantization algorithm is designed for each mode, wherein instantaneous artifacts are eliminated through extreme value screening of the electroencephalogram coefficient, the pixel diameter is calibrated into the physical diameter through the eye movement coefficient so as to eliminate imaging interference, and the large-amplitude movement intensity and high-frequency posture micro change are considered in the behavior coefficient.

The described voting system comprises two main components: an online verification device and an offline voting device. The verification device utilises a data interface to communicate with central databases for real-time voter verification, employing biometric sensors to ensure accurate voter identification and prevent duplicate voting. This system enhances security by generating a unique verification code for each verified voter. The offline voting device, designed to be immune to network-based threats, uses this code along with biometric verification to authenticate voters. It features a user interface that employs eye gesture technology, allowing voters to select candidates privately and securely through eye movements, without any visible indication of their choices to onlookers. This setup not only protects the integrity of the vote from cyber threats but also safeguards voter privacy and reduces the potential for voter intimidation, for a free, fair, and secure voting process.

The application discloses an AIGC interaction experience quality evaluation method and system based on eye movement and brain electrical dual modalities, relates to the technical field of man-machine interaction evaluation and cognitive neuroscience, and comprises the following steps: collecting brain electrical and eye movement signals of a user performing an AIGC interaction task in an entire process, and synchronously recording all key interaction events; pre-processing the signals, dividing the signals by taking the key interaction events as anchor points, and constructing event-related time sequence data segments; for each segment, carrying out dual-modality time sequence feature extraction by using an eye movement flow encoder and a brain electrical flow encoder, fusing and splicing the extracted brain electrical and eye movement features, and obtaining an interaction fluency hidden vector; based on the brain electrical and eye movement features and the interaction fluency hidden vector, combining preset indexes, and respectively calculating original scores of fluency, satisfaction and usability; constructing a scene vector according to a user interaction scene, generating weights through a dynamic adaptive weight network, dynamically weighting each original score, and obtaining a comprehensive score of the interaction experience quality.