A deep learning-based three-dimensional eye movement data emotion change analysis method

Abstract

The application discloses a kind of based on deep learning three-dimensional eye movement data emotional change analysis method, comprising the following steps: S1, three-dimensional eye movement data is collected, and eye movement trajectory sequence is formed;S2, behavior feature sequence is generated based on eye movement trajectory sequence;S3, one-dimensional convolution, gate calculation and state updating operation are carried out to behavior feature sequence, and form latent state sequence;S4, SimSiam method is used to carry out unsupervised contrast learning, and difference calculation is carried out;S5, clustering and change analysis operation are carried out to difference calculation result, and change section sequence is constructed;S6, continuity analysis and mutation detection operation are carried out, and fluctuation feature sequence is constructed;S7, extract emotional classification result, generate emotional change analysis report.The application proposes a kind of fusion three-dimensional eye movement data modeling and unsupervised contrast learning emotional change analysis method, with strong adaptability, high continuity and high stability.

Description

Technical Field

[0001] This invention relates to the field of emotion analysis technology, and in particular to a method for analyzing emotion changes based on three-dimensional eye-tracking data using deep learning. Background Technology

[0002] With the development of human-computer interaction, virtual reality, attention perception systems, and digital health management technologies, eye-tracking data, as an important perceptual signal of user intent and psychological state, has received increasing attention. Compared to traditional two-dimensional eye-tracking data, three-dimensional eye-tracking data can provide richer spatial dynamic information such as gaze direction, spatial fixation point, and eye movement path, making it suitable for multi-degree-of-freedom interactive scenarios, immersive experience environments, and psychological and behavioral research tasks.

[0003] In existing research, some emotion recognition methods attempt to model based on static statistical features such as fixation time, saccade count, and pupil diameter. However, these features often lack a deep expression of the temporal evolution process and the dynamic structure of behavior, making it difficult to accurately capture the continuous changing trends of emotional states. Some sequence modeling methods introduce convolutional neural networks or recurrent neural network structures, but most are only used for low-dimensional behavioral signals. They lack effective structured processing mechanisms for high-dimensional three-dimensional eye-tracking trajectories, especially lacking a unified closed loop in key aspects such as trajectory segmentation, temporal behavioral feature extraction, and latent state modeling.

[0004] Furthermore, existing technologies generally rely on manual labeling in the process of emotion state modeling. However, as a subjective and time-varying psychological state, emotion is costly to label and suffers from poor consistency, leading to the training model being prone to overfitting or having weak generalization ability. In the field of unsupervised learning, existing contrastive learning methods such as SimCLR and BYOL are mostly applied to static tasks such as image classification, and are difficult to directly adapt to the feature perturbation strategies and coding structures of dynamic data such as 3D trajectory. Especially in the stages of emotion trend recognition and emotion classification, current methods lack processing steps such as trend extraction, mutation detection, and fluctuation modeling of the emotion state evolution process. This results in the model's insufficient ability to express the continuity and fluctuation patterns of emotion, making it difficult to meet the needs of refined and dynamic emotion analysis in practical applications.

[0005] Therefore, how to provide a method for analyzing emotion changes based on deep learning in 3D eye-tracking data is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a deep learning-based method for analyzing emotion changes in 3D eye-tracking data. This invention fully utilizes 3D eye-tracking data modeling, temporal feature extraction, and unsupervised contrastive learning techniques to perform multi-level analysis of the user's eye-tracking trajectories during interaction. It achieves emotion representation learning by constructing sliding trajectory segments, extracting behavioral feature sequences, establishing latent state representations, and introducing the SimSiam method. Furthermore, it combines emotion change segment identification, continuity analysis, and multilayer perceptron classification to achieve automatic analysis and discrimination of the emotion change process. In addition, this invention can model emotion change trends without manual annotation, possessing advantages such as strong adaptability to complex eye-tracking behaviors, good continuity in emotion change recognition, and high stability of analysis results. It is suitable for applications such as emotion perception, human-computer interaction evaluation, and behavior analysis.

[0007] A method for analyzing emotion changes based on three-dimensional eye-tracking data according to an embodiment of the present invention includes the following steps:

[0008] S1. Collect the user's three-dimensional eye movement data, identify the position of the gaze point by calculating the gaze origin and gaze direction, and form an eye movement trajectory sequence;

[0009] S2. Construct a fixed-length sliding time window for the eye-tracking trajectory sequence, extract multiple trajectory segments, calculate the velocity change rate, angle change rate, and position offset of each trajectory segment, fuse them, and arrange the fusion results into a behavioral feature sequence.

[0010] S3. Perform a one-dimensional convolution operation on the behavioral feature sequence, and perform gating computation and state update operations through a gated recurrent unit to extract the latent state vector of each time step of the convolution result and form a latent state sequence.

[0011] S4. The SimSiam method is used to perform unsupervised contrastive learning on the latent state sequence. Masking and perturbation operations are performed on each latent state vector, and the difference between the masking and perturbation results is calculated.

[0012] S5. Perform clustering and change analysis on the difference calculation results to identify emotional change segments and extract the start time step, end time step and change amplitude to form a change segment sequence.

[0013] S6. Perform continuity analysis and mutation detection on the changed segment sequence, calculate the emotion change rate and trend slope, and construct the fluctuation characteristic sequence;

[0014] S7. Use a multilayer perceptron to extract the emotion classification results at each time step of the fluctuation feature sequence and generate an emotion change analysis report.

[0015] Optionally, the three-dimensional eye-tracking data represents a set of three-dimensional coordinate data collected in a spatial coordinate system showing the changes in the user's gaze direction and eye position over time. The fixation point position represents the coordinate position of the user's gaze point in three-dimensional space. The velocity change rate represents the degree of change in the spatial movement velocity between adjacent fixation points in the trajectory segment over time. The angle change rate represents the magnitude of the deflection of the gaze direction formed by adjacent fixation points in the trajectory segment over time. The position offset represents the distance difference between the starting and ending positions in the trajectory segment in three-dimensional coordinate space. The emotion change rate represents the rate of change of the change amplitude over time within the change segment. The trend slope represents the overall evolution trend of the change amplitude within the change segment.

[0016] Optionally, S1 specifically includes:

[0017] S11. Collect the user's three-dimensional eye-tracking data during the interaction process and record the starting position and direction of the gaze at each time step.

[0018] S12. Calculate the spatial intersection point based on the starting position and direction information of the line of sight at each time step to construct the line of sight ray, and extract the initial intersection point position between the line of sight ray and the set spatial reference plane;

[0019] S13. Perform least squares fitting on the initial intersection positions of multiple consecutive time steps to construct a smoothed gaze point trajectory and extract the gaze point position corresponding to the current time step.

[0020] S14. Arrange all fixation point positions in chronological order to construct an eye movement trajectory sequence containing a time index. The eye movement trajectory sequence represents the user's gaze position change process within a continuous time range.

[0021] Optionally, S12 specifically includes:

[0022] S121. Construct a spatial ray vector based on the starting position of the line of sight and the direction of the line of sight. The spatial ray vector takes the starting position of the line of sight as the ray starting point and the normalized direction of the line of sight as the ray direction.

[0023] S122. Set a spatial reference plane in three-dimensional space for simulating the observation target. The reference plane is defined by a preset normal vector and an initial plane point, representing the standard view or content plane that the user is looking at.

[0024] S123. Perform spatial geometric intersection calculation on the spatial ray vector and the spatial reference plane, calculate the line-plane intersection point of the current time step, and mark the time step as a valid time step;

[0025] S124. When the dot product of the spatial ray vector and the reference plane normal vector is lower than a set threshold, mark the time step as an invalid time step.

[0026] S125. Use the positions of all valid time step line-plane intersections as the initial intersection positions.

[0027] Optionally, S2 specifically includes:

[0028] S21. Construct a fixed-length sliding time window on the eye-tracking trajectory sequence and extract multiple trajectory segments, wherein the trajectory segments contain multiple consecutive fixation point positions;

[0029] S22. Calculate the Euclidean distance between adjacent gaze points in each trajectory segment and divide it by the corresponding time step difference to obtain a velocity value sequence. Calculate the velocity change rate based on the difference between adjacent velocity values ​​in the velocity value sequence.

[0030] S23. Calculate the gaze direction vector formed by the positions of adjacent gaze points in each trajectory segment, and generate an angle sequence based on the change in the angle between gaze directions between consecutive time steps. Calculate the angle change rate based on the difference between adjacent angles in the angle sequence.

[0031] S24. For each trajectory segment, calculate the difference in three-dimensional coordinates between the first and last gaze points as the position offset.

[0032] S25. The velocity change rate, angle change rate and position offset corresponding to each trajectory segment are spliced ​​and fused into a behavior feature vector according to the preset structure template, and all behavior feature vectors are arranged in chronological order to form a behavior feature sequence.

[0033] Optionally, S3 specifically includes:

[0034] S31. Input the behavioral feature sequence into the convolutional neural network, set a fixed-length one-dimensional convolutional kernel and slide it along the time direction to extract the local temporal features of each time step and generate the convolutional response sequence.

[0035] S32. A sparse autoencoder is used to perform dimensionality compression on the convolutional response sequence, mapping the convolutional response at each time step to a fixed-length state vector, specifically including:

[0036] The convolutional response vector at each time step is input into multiple fully connected layers, and linear transformation and LeakyReLU activation operations are performed sequentially to generate intermediate feature vectors.

[0037] After each fully connected layer is executed, a sparsity constraint is added to limit the proportion of non-zero values ​​in the activation results.

[0038] Extract the activation result after the constraints of the last fully connected layer as the state vector of the current time step;

[0039] The state vector is input into multiple fully connected layers of a symmetric structure to generate a reconstruction vector. The numerical difference between the corresponding convolutional response and the reconstruction vector is calculated. The parameters of the sparse autoencoder are dynamically optimized based on the numerical difference at each time step.

[0040] Arrange all state vectors in chronological order to form a state vector sequence;

[0041] S33. The gating loop unit performs gating calculation and state update operation, concatenates the state vector of each time step with the hidden state vector of the previous time step, and performs update gate and reset gate calculation operation on the concatenation result. In the first time step, the preset zero vector is used as the hidden state vector of the previous time step.

[0042] S34. Based on the update gate and the reset gate, perform a fusion operation on the current state vector and the hidden state vector of the previous time step to generate the hidden state vector of the current time step as the potential state vector.

[0043] S35. Connect all the potential state vectors in chronological order to construct a potential state sequence.

[0044] Optionally, the process of generating the potential state vector through the gated recurrent unit specifically includes:

[0045] The state vector of each time step is concatenated with the hidden state vector of the previous time step. In the first time step, a preset zero vector is used as the hidden state vector of the previous time step.

[0046] Based on the preset update weight matrix and reset weight matrix, the splicing result is subjected to two weighted linear transformations and Sigmoid function activation operations to generate update gate and reset gate.

[0047] Based on the reset threshold, perform an element-wise scaling operation on the hidden state vector of the previous time step to generate a memory vector;

[0048] The state vector at the current time step and the memory vector are weighted and fused according to the update threshold to generate the hidden state vector at the current time step as the potential state vector.

[0049] Optionally, S4 specifically includes:

[0050] S41. The SimSiam method is used to perform feature masking and sequential perturbation operations on the latent state vectors at each time step in the latent state sequence, generating masked feature sequences and perturbation feature sequences respectively.

[0051] S42. Perform residual mapping operation on the masked feature sequence and the perturbation feature sequence through a residual network to generate a masked coding sequence and a perturbation coding sequence, specifically including:

[0052] The occlusion feature vectors at each time step in the occlusion feature sequence are sequentially input into the residual network. A linear transformation operation is performed on the occlusion feature vectors using preset dimension parameters, and the Leaky ReLU function is used to map the linear transformation result into an intermediate feature vector.

[0053] Perform a linear transformation operation on the intermediate feature vector to obtain the projection vector;

[0054] The projection vector and the occlusion feature vector are added according to their element positions to generate the occlusion encoding vector for the current time step.

[0055] The perturbation feature vector at each time step in the perturbation feature sequence is sequentially input into the residual network to generate the perturbation encoding vector at each time step.

[0056] All masking and perturbation coding vectors are arranged in chronological order to form masking coding sequence and perturbation coding sequence, respectively;

[0057] S43. Perform Manhattan distance and cosine similarity calculation operations on the masking encoding vector and perturbation encoding vector at each time step, and perform weighted fusion operation on the Manhattan distance and cosine similarity according to the preset weight coefficient to obtain the difference calculation result.

[0058] Optionally, S41 specifically includes:

[0059] S411. Based on the potential state vector at each time step, a pseudo-random number generator is used to generate a Boolean mask with the same dimension as the potential state vector according to a preset masking ratio.

[0060] S412. Set the dimension values ​​corresponding to the zero masking code values ​​in the latent state vector to zero to generate the masking feature sequence.

[0061] S413. Based on the potential state vectors at each time step, call the preset order perturbation function to generate a shuffled list of feature dimensions;

[0062] S414. Keeping the values ​​of each feature dimension in the latent state vector unchanged, rearrange the positions of each feature dimension in the latent state vector according to the shuffled list to generate a perturbation feature sequence.

[0063] Optionally, S5 specifically includes:

[0064] S51. A similarity measurement operation is performed on the difference calculation results at each time step using a variational autoencoder, and state clustering is performed on the difference calculation results based on the similarity measurement results to generate multiple cluster segments, specifically including:

[0065] The difference calculation results arranged by time step are input into the variational autoencoder. Linear transformation and Leaky ReLU function activation operations are performed sequentially in the coding structure to extract the coding vector of each time step.

[0066] In the latent variable space of the variational autoencoder, reparameterization sampling based on a normal distribution is performed on all encoded vectors to generate a latent space feature set;

[0067] A similarity metric is calculated on the latent space feature set, and a similarity matrix is ​​constructed based on the Euclidean distance between the features;

[0068] Unsupervised clustering is performed on the similarity matrix to divide latent space features with Euclidean distances below a preset distance threshold into the same cluster, generating multiple cluster segments;

[0069] S52. Perform time continuity constraint analysis on the difference calculation results within each cluster segment, merge time steps that are continuous in time and in the same cluster segment to form emotional change segments;

[0070] S53. Determine the start and end time steps of each emotional change segment on the time axis, and calculate the magnitude of change based on the numerical range of the difference calculation results within the corresponding time range.

[0071] S54. Arrange the start time step, end time step, and change amplitude corresponding to each emotional change segment in chronological order to construct a change segment sequence.

[0072] The beneficial effects of this invention are:

[0073] First, by performing sliding modeling and trajectory segment extraction on three-dimensional eye-tracking data, this invention can effectively capture the subtle gaze shifts and gaze pattern changes of users during interaction. Combined with the constructed behavioral feature fusion mechanism and latent state extraction structure, it realizes deep expression modeling of the implicit emotional change signals in eye-tracking trajectories, significantly improving the ability to represent emotions under complex eye-tracking sequences.

[0074] Secondly, this invention introduces the SimSiam unsupervised contrastive learning method. By constructing a feature difference learning mechanism between the occlusion feature vector and the sequence transformation vector, it achieves highly robust extraction of emotion change features without relying on manual annotation. This effectively enhances the model's adaptability to temporal perturbations, individual behavioral differences, and modal shifts in eye-tracking data, and improves the generalization ability and continuity recognition accuracy of emotion state change recognition.

[0075] Finally, based on the clustering and continuity analysis of emotional change segments, this invention constructs emotional fluctuation features and introduces a multilayer perceptron structure for emotional state discrimination, realizing fully automated processing from raw eye-tracking data to the final emotional change report. It also has good scalability and deployment capabilities, and can be widely applied to application scenarios such as emotion perception, human-computer interaction evaluation, behavior understanding and psychological state monitoring, providing stable and reliable emotion recognition support for related intelligent systems. Attached Figure Description

[0076] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0077] Figure 1 This is a flowchart of a deep learning-based method for analyzing emotions from three-dimensional eye-tracking data, as proposed in this invention.

[0078] Figure 2 This is a flowchart illustrating the extraction and continuity analysis of emotion change segments in a deep learning-based three-dimensional eye-tracking data emotion change analysis method proposed in this invention.

[0079] Figure 3 This is a flowchart illustrating the latent state construction and comparative learning process of a deep learning-based three-dimensional eye-tracking data emotion change analysis method proposed in this invention. Detailed Implementation

[0080] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0081] refer to Figure 1-3 A deep learning-based method for analyzing emotion changes in 3D eye-tracking data includes the following steps:

[0082] S1. Collect the user's three-dimensional eye movement data, identify the position of the gaze point by calculating the gaze origin and gaze direction, and form an eye movement trajectory sequence;

[0083] S2. Construct a fixed-length sliding time window for the eye-tracking trajectory sequence, extract multiple trajectory segments, calculate the velocity change rate, angle change rate, and position offset of each trajectory segment, fuse them, and arrange the fusion results into a behavioral feature sequence.

[0084] S3. Perform a one-dimensional convolution operation on the behavioral feature sequence, and perform gating computation and state update operations through a gated recurrent unit to extract the latent state vector of each time step of the convolution result and form a latent state sequence.

[0085] S4. The SimSiam method is used to perform unsupervised contrastive learning on the latent state sequence. Masking and perturbation operations are performed on each latent state vector, and the difference between the masking and perturbation results is calculated.

[0086] S5. Perform clustering and change analysis on the difference calculation results to identify emotional change segments and extract the start time step, end time step and change amplitude to form a change segment sequence.

[0087] S6. Perform continuity analysis and mutation detection on the changed segment sequence, calculate the emotion change rate and trend slope, and construct the fluctuation characteristic sequence;

[0088] S7. Use a multilayer perceptron to extract the emotion classification results at each time step of the fluctuation feature sequence and generate an emotion change analysis report.

[0089] In this embodiment, the three-dimensional eye-tracking data represents a set of three-dimensional coordinate data collected in a spatial coordinate system showing the changes in the user's gaze direction and eye position over time. The fixation point position represents the coordinate position of the user's gaze point in three-dimensional space. The velocity change rate represents the degree of change in the spatial movement velocity between adjacent fixation points in the trajectory segment over time. The angle change rate represents the magnitude of the deflection of the gaze direction formed by adjacent fixation points in the trajectory segment over time. The position offset represents the distance difference between the starting and ending positions in the trajectory segment in three-dimensional coordinate space. The emotion change rate represents the rate of change of the change amplitude over time within the change segment. The trend slope represents the overall evolution trend of the change amplitude within the change segment.

[0090] In this embodiment, S1 specifically includes:

[0091] S11. Collect the user's three-dimensional eye-tracking data during the interaction process and record the starting position and direction of the gaze at each time step.

[0092] S12. Calculate the spatial intersection point based on the starting position and direction information of the line of sight at each time step to construct the line of sight ray, and extract the initial intersection point position between the line of sight ray and the set spatial reference plane;

[0093] S13. Perform a least-squares fitting operation on the initial intersection positions of multiple consecutive time steps to construct a smoothed gaze point trajectory, and extract the gaze point position corresponding to the current time step. The least-squares fitting operation specifically includes:

[0094] Set a sliding time interval, expand a fixed length of time step forward and backward with the current time step as the center, extract all the initial intersection points within the sliding time interval, and organize them into a coordinate sequence in chronological order;

[0095] The coordinate sequence is split according to the dimension, and the X, Y and Z coordinates of each coordinate point in the coordinate point set are extracted respectively. The three types of coordinates are then paired with time indices to form three sets of coordinate-time pairs.

[0096] For each coordinate time pair set, perform a least squares fitting operation. By traversing all time steps in the set, calculate the overall trend of the coordinates at each time step and generate a set of trend parameters.

[0097] The fitting values ​​of the three-dimensional coordinates corresponding to each time step are calculated based on the trend parameters to generate a smoothed gaze point trajectory.

[0098] S14. Arrange all fixation point positions in chronological order to construct an eye movement trajectory sequence containing a time index. The eye movement trajectory sequence represents the user's gaze position change process within a continuous time range.

[0099] In this embodiment, S12 specifically includes:

[0100] S121. Construct a spatial ray vector based on the starting position of the line of sight and the direction of the line of sight. The spatial ray vector takes the starting position of the line of sight as the ray starting point and the normalized direction of the line of sight as the ray direction.

[0101] S122. Set a spatial reference plane in three-dimensional space for simulating the observation target. The reference plane is defined by a preset normal vector and an initial plane point, representing the standard view or content plane that the user is looking at.

[0102] S123. Perform spatial geometric intersection calculation on the spatial ray vector and the spatial reference plane, calculate the line-plane intersection point of the current time step, and mark the time step as a valid time step;

[0103] S124. When the dot product of the spatial ray vector and the reference plane normal vector is lower than a set threshold, mark the time step as an invalid time step.

[0104] S125. Use the positions of all valid time step line-plane intersections as the initial intersection positions.

[0105] In this embodiment, S2 specifically includes:

[0106] S21. Construct a fixed-length sliding time window on the eye-tracking trajectory sequence and extract multiple trajectory segments, wherein the trajectory segments contain multiple consecutive fixation point positions;

[0107] S22. Calculate the Euclidean distance between adjacent gaze points in each trajectory segment and divide it by the corresponding time step difference to obtain a velocity value sequence. Calculate the velocity change rate based on the difference between adjacent velocity values ​​in the velocity value sequence.

[0108] S23. Calculate the gaze direction vector formed by the positions of adjacent gaze points in each trajectory segment, and generate an angle sequence based on the change in the angle between gaze directions between consecutive time steps. Calculate the angle change rate based on the difference between adjacent angles in the angle sequence.

[0109] S24. For each trajectory segment, calculate the difference in three-dimensional coordinates between the first and last gaze points as the position offset.

[0110] S25. The velocity change rate, angle change rate and position offset corresponding to each trajectory segment are spliced ​​and fused into a behavior feature vector according to the preset structure template, and all behavior feature vectors are arranged in chronological order to form a behavior feature sequence.

[0111] In this embodiment, S3 specifically includes:

[0112] S31. Input the behavioral feature sequence into the convolutional neural network, set a fixed-length one-dimensional convolutional kernel and slide it along the time direction to extract the local temporal features of each time step and generate the convolutional response sequence.

[0113] S32. A sparse autoencoder is used to perform dimensionality compression on the convolutional response sequence, mapping the convolutional response at each time step to a fixed-length state vector, specifically including:

[0114] The convolutional response vector at each time step is input into multiple fully connected layers, and linear transformation and LeakyReLU activation operations are performed sequentially to generate intermediate feature vectors.

[0115] After each fully connected layer is executed, a sparsity constraint is added to limit the proportion of non-zero values ​​in the activation results.

[0116] Extract the activation result after the constraints of the last fully connected layer as the state vector of the current time step;

[0117] The state vector is input into multiple fully connected layers of a symmetric structure to generate a reconstruction vector. The numerical difference between the corresponding convolutional response and the reconstruction vector is calculated. The parameters of the sparse autoencoder are dynamically optimized based on the numerical difference at each time step.

[0118] Arrange all state vectors in chronological order to form a state vector sequence;

[0119] S33. The gating loop unit performs gating calculation and state update operation, concatenates the state vector of each time step with the hidden state vector of the previous time step, and performs update gate and reset gate calculation operation on the concatenation result. In the first time step, the preset zero vector is used as the hidden state vector of the previous time step.

[0120] S34. Based on the update gate and the reset gate, perform a fusion operation on the current state vector and the hidden state vector of the previous time step to generate the hidden state vector of the current time step as the potential state vector.

[0121] S35. Connect all the potential state vectors in chronological order to construct a potential state sequence.

[0122] In this embodiment, the process of generating the potential state vector through the gated loop unit specifically includes:

[0123] The state vector of each time step is concatenated with the hidden state vector of the previous time step. In the first time step, a preset zero vector is used as the hidden state vector of the previous time step.

[0124] Based on the preset update weight matrix and reset weight matrix, the splicing result is subjected to two weighted linear transformations and Sigmoid function activation operations to generate update gate and reset gate.

[0125] Based on the reset threshold, perform an element-wise scaling operation on the hidden state vector of the previous time step to generate a memory vector;

[0126] The state vector at the current time step and the memory vector are weighted and fused according to the update threshold to generate the hidden state vector at the current time step as the potential state vector.

[0127] In this embodiment, S4 specifically includes:

[0128] S41. The SimSiam method is used to perform feature masking and sequential perturbation operations on the latent state vectors at each time step in the latent state sequence, generating masked feature sequences and perturbation feature sequences respectively.

[0129] S42. Perform residual mapping operation on the masked feature sequence and the perturbation feature sequence through a residual network to generate a masked coding sequence and a perturbation coding sequence, specifically including:

[0130] The occlusion feature vectors at each time step in the occlusion feature sequence are sequentially input into the residual network. A linear transformation operation is performed on the occlusion feature vectors using preset dimension parameters, and the Leaky ReLU function is used to map the linear transformation result into an intermediate feature vector.

[0131] Perform a linear transformation operation on the intermediate feature vector to obtain the projection vector;

[0132] The projection vector and the occlusion feature vector are added according to their element positions to generate the occlusion encoding vector for the current time step.

[0133] The perturbation feature vector at each time step in the perturbation feature sequence is sequentially input into the residual network to generate the perturbation encoding vector at each time step.

[0134] All masking and perturbation coding vectors are arranged in chronological order to form masking coding sequence and perturbation coding sequence, respectively;

[0135] S43. Perform Manhattan distance and cosine similarity calculation operations on the masking encoding vector and perturbation encoding vector at each time step, and perform weighted fusion operation on the Manhattan distance and cosine similarity according to the preset weight coefficient to obtain the difference calculation result.

[0136] In this embodiment, S41 specifically includes:

[0137] S411. Based on the potential state vector at each time step, a pseudo-random number generator is used to generate a Boolean mask with the same dimension as the potential state vector according to a preset masking ratio.

[0138] S412. Set the dimension values ​​corresponding to the zero masking code values ​​in the latent state vector to zero to generate the masking feature sequence.

[0139] S413. Based on the potential state vectors at each time step, call the preset order perturbation function to generate a shuffled list of feature dimensions;

[0140] S414. Keeping the values ​​of each feature dimension in the latent state vector unchanged, rearrange the positions of each feature dimension in the latent state vector according to the shuffled list to generate a perturbation feature sequence.

[0141] In this embodiment, S43 specifically includes:

[0142] S431. Perform difference calculation on the masking coding vector and perturbation coding vector of each time step according to the dimensional position, calculate the absolute difference between the values ​​of each dimension and sum them to obtain the Manhattan distance of the current time step.

[0143] S432. Perform cosine similarity calculation on the masking encoding vector and the perturbation encoding vector at each time step, specifically including:

[0144] Calculate the product of the corresponding dimensions of two vectors and add them together along each dimension to obtain the dot product value;

[0145] Calculate the sum of squares of the two vectors separately, and take the square root of the sum to obtain the Euclidean norm values ​​of the two vectors.

[0146] Divide the dot product by the product of the two Euclidean norm values ​​to obtain the cosine similarity at the current time step.

[0147] S433. Perform a weighted fusion operation on the Manhattan distance and cosine similarity according to the preset weight coefficients, and use the fusion result as the difference calculation result of the current time step.

[0148] In this embodiment, S5 specifically includes:

[0149] S51. A similarity measurement operation is performed on the difference calculation results at each time step using a variational autoencoder, and state clustering is performed on the difference calculation results based on the similarity measurement results to generate multiple cluster segments, specifically including:

[0150] The difference calculation results arranged by time step are input into the variational autoencoder. Linear transformation and Leaky ReLU function activation operations are performed sequentially in the coding structure to extract the coding vector of each time step.

[0151] In the latent variable space of the variational autoencoder, reparameterization sampling based on a normal distribution is performed on all encoded vectors to generate a latent space feature set;

[0152] A similarity metric is calculated on the latent space feature set, and a similarity matrix is ​​constructed based on the Euclidean distance between the features;

[0153] Unsupervised clustering is performed on the similarity matrix to divide latent space features with Euclidean distances below a preset distance threshold into the same cluster, generating multiple cluster segments;

[0154] S52. Perform time continuity constraint analysis on the difference calculation results within each cluster segment, merge time steps that are continuous in time and in the same cluster segment to form emotional change segments;

[0155] S53. Determine the start and end time steps of each emotional change segment on the time axis, and calculate the magnitude of change based on the numerical range of the difference calculation results within the corresponding time range.

[0156] S54. Arrange the start time step, end time step, and change amplitude corresponding to each emotional change segment in chronological order to construct a change segment sequence.

[0157] In this embodiment, S6 specifically includes:

[0158] S61. Construct equally spaced sliding time windows for the sequence of changing segments, perform continuous identification operation on the continuous changing segments in each window, and extract the number of changing segments in each window and the time interval between adjacent changing segments.

[0159] S62. Based on the time interval distribution of adjacent change segments and the changing trend of the number of change segments, perform segment continuity analysis and mark multiple change segments that appear consecutively within a set time threshold as continuous change areas.

[0160] S63. Perform differential calculation on the change amplitude corresponding to each continuous change region, and extract the emotional change rate in combination with the change direction, wherein the change direction represents the trend of the change amplitude increasing or decreasing over time in the continuous change region.

[0161] S64. Calculate the trend slope based on the fitting results between the time step and the change amplitude;

[0162] S65. Arrange all the rates of change of emotion and the slope of the trend in chronological order to construct a fluctuation characteristic sequence.

[0163] In this embodiment, S7 specifically includes:

[0164] S71. Input the fluctuation feature sequence into a multilayer perceptron, pass it through multiple fully connected layers in sequence, perform linear transformation and Tanh function mapping operations to generate a hidden representation sequence;

[0165] S72. According to the preset classification rules, perform classification and discrimination operations on the hidden representation vectors and assign emotion category labels to them, and construct a sequence of emotion classification results arranged by time step;

[0166] S73. Based on the emotion classification result sequence, and combined with the rate of change and trend slope of each time step in the fluctuation characteristic sequence, generate an emotion change analysis report. Example

[0167] To verify the feasibility of this invention in practice, it was applied to a multimodal interactive environment for real-time analysis of users' emotional changes during interactions with different content. This environment includes a terminal system with integrated eye-tracking equipment. When users watch videos, browse text and images, or perform tasks, the system synchronously collects their three-dimensional eye-tracking data and identifies and analyzes their emotional state based on the method proposed in this invention.

[0168] In practical applications, users do not need to wear additional devices. They only need to look directly at the terminal screen equipped with a high-precision eye-tracking module, and the system will automatically record their eye movement trajectory information, including the spatial displacement of the eyeball in the horizontal and vertical directions, as well as the three-dimensional coordinate sequence formed by changes in eye movement depth. Each eye movement trajectory is divided into multiple sliding trajectory segments, and each segment corresponds to a stable fixation or saccade process. The system further constructs a behavioral feature set for each segment and extracts the latent state sequence through a one-dimensional convolutional network and a gated recurrent structure.

[0169] To avoid label dependence in emotion feature extraction, this invention introduces a SimSiam structure to perform unsupervised contrastive learning after state sequence extraction. Positive and negative sample pairs are generated through pseudo-random masking and dimensionality perturbation techniques, and a self-supervised loss function is constructed. This allows the model to aggregate similar states without relying on any manual annotation and enhances the ability to distinguish between different states. During training, the system utilizes a large amount of historical eye-tracking behavior data as a pre-training data source, quickly converging to a stable feature encoding pattern.

[0170] After the user's eye-tracking data sequence passes through the aforementioned feature extraction and comparative learning modules, the system automatically segments emotional change fragments and constructs an emotional fluctuation feature set based on state amplitude, fluctuation frequency, and duration of change. Subsequently, a multilayer perceptron structure is used to map the emotional fluctuation features to standard emotion type labels, and a complete emotion analysis report is output. To improve model adaptability, a sliding update mechanism is introduced during continuous operation, constantly fine-tuning based on new data to ensure the model's stability and generalization ability in long-term operation.

[0171] In this experiment, 30 users were randomly selected to participate in the test, completing five different interactive tasks: browsing static images, watching dynamic videos, inputting operation commands, reading text, and switching between multiple windows. Each task lasted approximately 10 minutes. After data collection and model training, we used traditional emotion recognition methods as a control group and compared them with a benchmark neural network emotion recognition model with manual annotations. During the test, all users' emotional fluctuations were recorded and manually verified to evaluate the recognition accuracy, stability, and consistency.

[0172] The table below shows key metrics for some users, including recognition accuracy, recognition continuity score, response time stability, false positive rate, and model convergence time, under the method of this invention and the control method:

[0173] Table 1. Comparison of experimental data between the method of the present invention and the control method.

[0174] User ID The accuracy rate (%) of this invention Accuracy rate of identification by comparison method (%) Emotional continuity score (0-1) Response time stability (ms) False positive rate (%) Model convergence time (epochs) U01 92.5 84.3 0.91 37.5 3.1 42 U02 90.2 81.6 0.89 40.2 3.6 41 U03 94.1 86.8 0.93 36.1 2.7 43 U04 89.7 80.2 0.88 39.9 3.9 40 U05 91.6 82.4 0.90 38.3 3.3 41 U06 92.8 84.1 0.92 37.2 3.0 42 U07 93.3 83.5 0.94 35.8 2.6 43 U08 88.9 79.7 0.87 41.5 4.2 40 U09 90.7 82.1 0.89 39.0 3.4 41 U10 94.4 87.5 0.95 34.7 2.3 43

[0175] As shown in Table 1, the present invention generally outperforms the traditional control method in terms of recognition accuracy, with a maximum improvement of 7.1% and an average improvement of 6.4%. Particularly in terms of the continuity score of emotion changes, the average score is 0.91, significantly better than the traditional method's 0.83, indicating that the present invention performs better in terms of the continuity and stability of the emotion recognition process. Regarding response time stability, the system remains within 40ms in most cases, demonstrating high real-time performance. Furthermore, the false positive rate is significantly reduced, indicating that the present method can effectively suppress erroneous judgments when dealing with complex eye-movement behaviors, improving the reliability of the analysis results.

[0176] Meanwhile, the proposed method exhibits rapid model convergence, reaching a stable state after approximately 42 epochs, demonstrating excellent training efficiency and algorithm convergence. This makes it suitable for rapid deployment and online use in various scenarios. Overall, this invention not only solves the problem of traditional methods' dependence on labeled data but also demonstrates significant advantages in analytical stability, emotional fluctuation perception capabilities, and adaptability, making it highly valuable for practical application and promotion.

[0177] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for analyzing emotion changes in 3D eye-tracking data based on deep learning, characterized in that, Includes the following steps: S1. Collect the user's three-dimensional eye movement data, identify the position of the gaze point by calculating the gaze origin and gaze direction, and form an eye movement trajectory sequence; S2. Construct a fixed-length sliding time window for the eye-tracking trajectory sequence, extract multiple trajectory segments, calculate the velocity change rate, angle change rate, and position offset of each trajectory segment, fuse them, and arrange the fusion results into a behavioral feature sequence. S3. Perform a one-dimensional convolution operation on the behavioral feature sequence, and perform gating computation and state update operations through a gated recurrent unit to extract the latent state vector of each time step of the convolution result and form a latent state sequence. S4. The SimSiam method is used to perform unsupervised contrastive learning on the latent state sequence. Masking and perturbation operations are performed on each latent state vector, and the difference between the masking and perturbation results is calculated. S5. Perform clustering and change analysis on the difference calculation results to identify emotional change segments and extract the start time step, end time step and change amplitude to form a change segment sequence. S6. Perform continuity analysis and mutation detection on the changed segment sequence, calculate the emotion change rate and trend slope, and construct the fluctuation characteristic sequence; S7. Use a multilayer perceptron to extract the emotion classification results at each time step of the fluctuation feature sequence and generate an emotion change analysis report.

2. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, The three-dimensional eye-tracking data represents a set of three-dimensional coordinate data collected in a spatial coordinate system, showing the changes in the user's gaze direction and eye position over time. The fixation point position represents the coordinate position of the user's gaze point in three-dimensional space. The velocity change rate represents the degree of change in the spatial movement velocity between adjacent fixation points in the trajectory segment over time. The angle change rate represents the magnitude of the deflection of the gaze direction formed by adjacent fixation points in the trajectory segment over time. The position offset represents the distance difference between the starting and ending positions in the trajectory segment in three-dimensional coordinate space. The emotion change rate represents the rate of change of the change amplitude within the change segment over time. The trend slope represents the overall evolution trend of the change amplitude within the change segment.

3. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, S1 specifically includes: S11. Collect the user's three-dimensional eye-tracking data during the interaction process and record the starting position and direction of the gaze at each time step. S12. Calculate the spatial intersection point based on the starting position and direction information of the line of sight at each time step to construct the line of sight ray, and extract the initial intersection point position between the line of sight ray and the set spatial reference plane; S13. Perform least squares fitting on the initial intersection positions of multiple consecutive time steps to construct a smoothed gaze point trajectory and extract the gaze point position corresponding to the current time step. S14. Arrange all fixation point positions in chronological order to construct an eye movement trajectory sequence containing a time index. The eye movement trajectory sequence represents the user's gaze position change process within a continuous time range.

4. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 3, characterized in that, S12 specifically includes: S121. Construct a spatial ray vector based on the starting position of the line of sight and the direction of the line of sight. The spatial ray vector takes the starting position of the line of sight as the ray starting point and the normalized direction of the line of sight as the ray direction. S122. Set a spatial reference plane in three-dimensional space for simulating the observation target. The reference plane is defined by a preset normal vector and an initial plane point, representing the standard view or content plane that the user is looking at. S123. Perform spatial geometric intersection calculation on the spatial ray vector and the spatial reference plane, calculate the line-plane intersection point of the current time step, and mark the time step as a valid time step; S124. When the dot product of the spatial ray vector and the reference plane normal vector is lower than a set threshold, mark the time step as an invalid time step. S125. Use the positions of all valid time step line-plane intersections as the initial intersection positions.

5. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, S2 specifically includes: S21. Construct a fixed-length sliding time window on the eye-tracking trajectory sequence and extract multiple trajectory segments, wherein the trajectory segments contain multiple consecutive fixation point positions; S22. Calculate the Euclidean distance between adjacent gaze points in each trajectory segment and divide it by the corresponding time step difference to obtain a velocity value sequence. Calculate the velocity change rate based on the difference between adjacent velocity values ​​in the velocity value sequence. S23. Calculate the gaze direction vector formed by the positions of adjacent gaze points in each trajectory segment, and generate an angle sequence based on the change in the angle between gaze directions between consecutive time steps. Calculate the angle change rate based on the difference between adjacent angles in the angle sequence. S24. For each trajectory segment, calculate the difference in three-dimensional coordinates between the first and last gaze points as the position offset. S25. The velocity change rate, angle change rate and position offset corresponding to each trajectory segment are spliced ​​and fused into a behavior feature vector according to the preset structure template, and all behavior feature vectors are arranged in chronological order to form a behavior feature sequence.

6. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, S3 specifically includes: S31. Input the behavioral feature sequence into the convolutional neural network, set a fixed-length one-dimensional convolutional kernel and slide it along the time direction to extract the local temporal features of each time step and generate the convolutional response sequence. S32. A sparse autoencoder is used to perform dimensionality compression on the convolutional response sequence, mapping the convolutional response at each time step to a fixed-length state vector, specifically including: The convolutional response vector at each time step is input into multiple fully connected layers, and linear transformation and Leaky ReLU activation operations are performed sequentially to generate intermediate feature vectors. After each fully connected layer is executed, a sparsity constraint is added to limit the proportion of non-zero values ​​in the activation results. Extract the activation result after the constraints of the last fully connected layer as the state vector of the current time step; The state vector is input into multiple fully connected layers of a symmetric structure to generate a reconstruction vector. The numerical difference between the corresponding convolutional response and the reconstruction vector is calculated. The parameters of the sparse autoencoder are dynamically optimized based on the numerical difference at each time step. Arrange all state vectors in chronological order to form a state vector sequence; S33. The gating loop unit performs gating calculation and state update operation, concatenates the state vector of each time step with the hidden state vector of the previous time step, and performs update gate and reset gate calculation operation on the concatenation result. In the first time step, the preset zero vector is used as the hidden state vector of the previous time step. S34. Based on the update gate and the reset gate, perform a fusion operation on the current state vector and the hidden state vector of the previous time step to generate the hidden state vector of the current time step as the potential state vector. S35. Connect all the potential state vectors in chronological order to construct a potential state sequence.

7. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 6, characterized in that, The process of generating the potential state vector through the gated recurrent unit specifically includes: The state vector of each time step is concatenated with the hidden state vector of the previous time step. In the first time step, a preset zero vector is used as the hidden state vector of the previous time step. Based on the preset update weight matrix and reset weight matrix, the splicing result is subjected to two weighted linear transformations and Sigmoid function activation operations to generate update gate and reset gate. Based on the reset threshold, perform an element-wise scaling operation on the hidden state vector of the previous time step to generate a memory vector; The state vector at the current time step and the memory vector are weighted and fused according to the update threshold to generate the hidden state vector at the current time step as the potential state vector.

8. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, S4 specifically includes: S41. The SimSiam method is used to perform feature masking and sequential perturbation operations on the latent state vectors at each time step in the latent state sequence, generating masked feature sequences and perturbation feature sequences respectively. S42. Perform residual mapping operation on the masked feature sequence and the perturbation feature sequence through a residual network to generate a masked coding sequence and a perturbation coding sequence, specifically including: The occlusion feature vectors at each time step in the occlusion feature sequence are sequentially input into the residual network. A linear transformation operation is performed on the occlusion feature vectors using preset dimension parameters, and the Leaky ReLU function is used to map the linear transformation result into an intermediate feature vector. Perform a linear transformation operation on the intermediate feature vector to obtain the projection vector; The projection vector and the occlusion feature vector are added according to their element positions to generate the occlusion encoding vector for the current time step. The perturbation feature vector at each time step in the perturbation feature sequence is sequentially input into the residual network to generate the perturbation encoding vector at each time step. All masking and perturbation coding vectors are arranged in chronological order to form masking coding sequence and perturbation coding sequence, respectively; S43. Perform Manhattan distance and cosine similarity calculation operations on the masking encoding vector and perturbation encoding vector at each time step, and perform weighted fusion operation on the Manhattan distance and cosine similarity according to the preset weight coefficient to obtain the difference calculation result.

9. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 8, characterized in that, S41 specifically includes: S411. Based on the potential state vector at each time step, a pseudo-random number generator is used to generate a Boolean mask with the same dimension as the potential state vector according to a preset masking ratio. S412. Set the dimension values ​​corresponding to the zero masking code values ​​in the latent state vector to zero to generate the masking feature sequence. S413. Based on the potential state vectors at each time step, call the preset order perturbation function to generate a shuffled list of feature dimensions; S414. Keeping the values ​​of each feature dimension in the latent state vector unchanged, rearrange the positions of each feature dimension in the latent state vector according to the shuffled list to generate a perturbation feature sequence.

10. The method for analyzing emotion changes in three-dimensional eye-tracking data based on deep learning according to claim 1, characterized in that, S5 specifically includes: S51. A similarity measurement operation is performed on the difference calculation results at each time step using a variational autoencoder, and state clustering is performed on the difference calculation results based on the similarity measurement results to generate multiple cluster segments, specifically including: The difference calculation results arranged by time step are input into the variational autoencoder. Linear transformation and Leaky ReLU function activation operations are performed sequentially in the coding structure to extract the coding vector of each time step. In the latent variable space of the variational autoencoder, reparameterization sampling based on a normal distribution is performed on all encoded vectors to generate a latent space feature set; A similarity metric is calculated on the latent space feature set, and a similarity matrix is ​​constructed based on the Euclidean distance between the features; Unsupervised clustering is performed on the similarity matrix to divide latent space features with Euclidean distances below a preset distance threshold into the same cluster, generating multiple cluster segments; S52. Perform time continuity constraint analysis on the difference calculation results within each cluster segment, merge time steps that are continuous in time and in the same cluster segment to form emotional change segments; S53. Determine the start and end time steps of each emotional change segment on the time axis, and calculate the magnitude of change based on the numerical range of the difference calculation results within the corresponding time range. S54. Arrange the start time step, end time step, and change amplitude corresponding to each emotional change segment in chronological order to construct a change segment sequence.

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