Earphone and video auditory and somatosensory training linkage control system

Abstract

The invention relates to the technical field of auditory body sense training, and discloses an earphone and video auditory body sense training linkage control system. The system comprises a multi-modal data acquisition module, a data fusion processing module, a dynamic feature extraction module, an adaptive weight distribution module, an anomaly detection engine module and a real-time feedback adjustment module. The multi-modal data acquisition module synchronously acquires earphone real-time audio feature data and video training system visual motion feature data; the data fusion processing module aligns the two types of data according to timestamps to generate a space-time incidence matrix; a dynamic feature extraction module extracts auditory sense-somatosensory sense coupling feature vectors from the dynamic feature extraction module; a self-adaptive weight distribution module adjusts a coupling feature vector weighting coefficient according to the target parameter of the current training stage; the anomaly detection engine identifies an abnormal mode based on the deep residual network; and the real-time feedback adjustment module generates an audio and video parameter adjustment instruction according to the detection result. The system realizes linkage control of auditory sense and body sense training, and is suitable for scenes of rehabilitation training, exercise guidance and the like.

Description

Technical Field

[0001] This invention relates to the field of auditory-motor training technology, specifically to a linkage control system for auditory-motor training using headphones and video. Background Technology

[0002] In modern rehabilitation training, exercise guidance, and special skills development, the coordinated training of auditory and kinesthetic senses is gradually becoming an important direction for improving training effectiveness. Currently, various independent audio training devices and video motion training systems exist on the market. Audio training devices mostly focus on transmitting audio signals of specific frequencies and rhythms through headphones to help users improve their auditory perception or achieve emotional regulation. Video motion training systems, on the other hand, rely on image acquisition and analysis technology to capture the user's limb movement trajectory, posture angle, and other visual motion characteristics, providing a reference for the standardization of training movements. However, in existing technologies, audio training devices and video motion training systems often operate independently, lacking effective data interaction and collaborative control mechanisms. During actual training, there is a close physiological connection between auditory stimulation and somatosensory movement. For example, audio with a specific rhythm can guide users to adjust their movement frequency, and changes in the amplitude of user limb movements may require corresponding adjustments to audio parameters to achieve better training guidance. Due to the lack of data linkage, audio training devices cannot dynamically adjust audio output parameters based on the user's real-time movement status, and video motion training systems cannot combine audio signals to more accurately analyze and guide the user's movement rhythm and intensity control. This results in the synergistic effect of auditory and somatosensory stimulation being difficult to fully realize during training, significantly limiting the training effect. Existing training systems also have significant shortcomings in data processing and anomaly detection. Traditional data processing methods often analyze single types of data (such as audio data or motion data only), failing to accurately align and fuse audio and motion features over time, making it difficult to generate comprehensive feature data that reflects the state of auditory-kinesthetic coordination. In anomaly detection, existing technologies often employ simple threshold judgment methods, such as setting fixed motion amplitude or audio frequency thresholds, and classifying data as abnormal when it exceeds the threshold range. This approach cannot effectively identify complex anomaly patterns caused by auditory-kinesthetic coordination imbalances, such as a severe mismatch between the user's movement rhythm and the audio rhythm, or limb movement postures that meet standards but do not conform to the intensity control requirements of the audio guidance. This results in low accuracy of anomaly detection, failing to provide reliable evidence for timely training adjustments, and further affecting the safety and effectiveness of training. Different training stages (such as basic training, intensive training, and consolidation training) have different requirements for target parameters such as auditory guidance intensity and exercise difficulty. Existing training systems mostly rely on manual parameter adjustments, failing to automatically adjust the weighting of auditory and kinesthetic parameters according to the goals of the current training stage. For example, the basic training stage requires stronger audio guidance to help users establish a sense of movement rhythm, while the intensive training stage needs more focus on analyzing and guiding the details of movement posture. Manual adjustment is not only cumbersome but also difficult to accurately match the actual needs of different training stages, resulting in poor adaptability and flexibility in the training process, failing to meet diverse training scenarios and personalized training needs. Summary of the Invention

[0003] The purpose of this invention is to provide a linkage control system for headphones and video auditory and somatosensory training to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides a linkage control system for headphones and video auditory-kinesthetic training, the system comprising: The multimodal data acquisition module is used to simultaneously acquire real-time audio feature data transmitted from the headphones and visual motion feature data transmitted from the video training system. The data fusion processing module is used to align the real-time audio feature data and visual motion feature data according to timestamps to generate a spatiotemporal correlation matrix; The dynamic feature extraction module is used to extract auditory-kinesthetic coupling feature vectors from the spatiotemporal correlation matrix; An adaptive weight allocation module is used to dynamically adjust the weighting coefficients of the auditory-kinesthetic coupling feature vector according to the target parameters of the current training phase. An anomaly detection engine is used to perform anomaly pattern recognition on weighted auditory-somatosensory coupled feature vectors based on deep residual networks. The real-time feedback adjustment module is used to generate audio parameter adjustment instructions and video parameter adjustment instructions based on the anomaly detection results.

[0005] Preferably, the multimodal data acquisition module includes: The audio feature analysis unit is used to extract the sound pressure distribution spectrum, phase difference matrix, and harmonic component features from the headphone transmitted signal. The visual motion tracking unit is used to extract human joint motion trajectories, muscle contraction frequencies, and center of gravity offsets from the video training system. The time synchronization unit is used to attach timestamps accurate to the millisecond level to the audio feature data and visual motion feature data.

[0006] Preferably, the data fusion processing module includes: Matrix building unit, used to combine audio feature data and visual motion feature data with the same timestamp into a multidimensional feature tensor; A normalization processing unit is used to perform cross-modal data normalization on the multidimensional feature tensor; The correlation analysis unit is used to calculate the Pearson correlation coefficient between different modal features and generate the spatiotemporal correlation matrix.

[0007] Preferably, the dynamic feature extraction module includes: A convolutional feature extraction unit is used to extract joint spatiotemporal-frequency features from the spatiotemporal correlation matrix using a three-dimensional convolutional kernel; An attention mechanism unit is used to calculate the self-attention weights of the spatiotemporal-frequency joint features; The feature reduction unit is used to compress the weighted joint features into a low-dimensional auditory-somatosensory coupled feature vector by means of a variational autoencoder.

[0008] Preferably, the adaptive weight allocation module includes: The training phase identification unit is used to determine the current training phase based on the training duration and training intensity. The weight calculation unit is used to call the corresponding feature weighting scheme from the preset weight library based on the training stage; The dynamic adjustment unit is used to fine-tune the specific coefficients in the feature weighting scheme according to the real-time feature change rate.

[0009] Preferably, the anomaly detection engine includes: Residual network building units are used to construct deep convolutional neural networks containing skip connections. The feature comparison unit is used to calculate the similarity between the current auditory-somatosensory coupling feature vector and the reference vector in the standard feature library; Anomaly scoring unit, used to output anomaly probability value based on feature difference degree.

[0010] Preferably, the residual network construction unit specifically includes: Multi-scale convolutional layers are used to extract feature patterns at different time scales simultaneously. Gated recurrent layers are used to capture the temporal dependencies of feature sequences; The feature fusion layer is used to cross-fuse multi-scale features with time series features.

[0011] Preferably, the real-time feedback adjustment module includes: The instruction generation unit is used to map the anomaly detection results into specific audio gain parameters and video playback parameters; The parameter optimization unit is used to solve for the optimal combination of adjustment parameters using the gradient descent method. The device control unit is used to send the optimized parameters to the headphone driver system and the video rendering engine, respectively.

[0012] Preferably, the instruction generation unit specifically includes: An audio parameter mapping table is used to store equalizer adjustment schemes corresponding to different anomaly types; A video parameter mapping table is used to store frame rate compensation schemes corresponding to different anomaly types; A joint optimization strategy library is used to store optimization strategies for cross-device collaborative adjustment.

[0013] Preferably, the system further includes: The cloud collaboration module is used to upload key feature data and adjustment records during the training process to the cloud server; The incremental learning module is used to continuously optimize the detection model of the anomaly detection engine based on multi-user data collected in the cloud; The personalized adaptation module is used to generate customized feature weight schemes based on the user's historical training data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By employing multi-module collaborative operation, this technology effectively solves the problems of independent operation and data fragmentation between audio training equipment and video motion training systems in existing technologies, achieving deep linkage and coordinated control of auditory and kinesthetic training. The multimodal data acquisition module can simultaneously acquire real-time audio feature data transmitted from the headphones and visual motion feature data transmitted from the video training system, breaking down the data barriers between the two types of devices in traditional technologies and providing fundamental data support for subsequent collaborative analysis and control. By synchronously acquiring both types of data, it can accurately capture auditory stimuli and kinesthetic motion states at the same point in time during training, creating conditions for exploring the intrinsic relationship between the two. This allows the synergistic effect of auditory and kinesthetic training to be fully utilized, thereby optimizing the overall training experience. The data fusion processing module aligns real-time audio feature data and visual motion feature data according to timestamps to generate a spatiotemporal correlation matrix. This data processing method enables precise matching of the two types of data in the time dimension, eliminating the limitations of single-data type analysis. The spatiotemporal correlation matrix not only integrates features such as frequency, rhythm, and intensity of audio signals, but also incorporates information such as limb trajectory, posture angle, and movement speed from motion data, forming a comprehensive data carrier that fully reflects the auditory-kinesthetic coordination state. Based on this matrix, the dynamic feature extraction module can further extract auditory-kinesthetic coupling feature vectors. These vectors can accurately characterize the synergistic relationship between auditory stimuli and somatosensory movement, such as the matching degree between audio rhythm and movement frequency, and the correlation between changes in audio intensity and adjustments in limb movement amplitude, providing a more targeted analytical basis for subsequent parameter adjustment and anomaly detection. The adaptive weight allocation module dynamically adjusts the weighting coefficients of the auditory-kinesthetic coupling feature vectors based on the target parameters of the current training stage, enabling the system to flexibly adapt to the needs of different training stages. In different training stages, the system can dynamically switch the focus between auditory guidance and kinesthetic training by adjusting the weighting coefficients. For example, in the basic training stage, the weight of the audio feature correlation coefficient can be appropriately increased to strengthen the guiding role of audio on movement rhythm; in the advanced training stage, the weight of the motion feature correlation coefficient can be increased to focus more on optimizing the details of movement posture. This dynamic adjustment mechanism requires no manual intervention and can automatically match the training progress, improving the system's adaptability to different training scenarios while meeting personalized training needs, making the training process more aligned with the user's actual abilities and improvement goals. The anomaly detection engine uses a deep residual network to identify abnormal patterns in the weighted auditory-kinesthetic coupled feature vectors. Compared to traditional threshold-based methods, deep residual networks possess stronger capabilities for learning complex features and recognizing patterns. This engine can uncover potential abnormal correlation patterns from the coupled feature vectors, such as subtle imbalances between the user's movement rhythm and the audio rhythm, or abnormal situations where limb movements conform to standards but do not match the audio guidance intensity—situations that are difficult to identify using traditional methods. By accurately identifying these abnormal patterns, the system can promptly detect problems during training, preventing abnormal states from continuously affecting training effectiveness or causing safety risks, and providing a reliable basis for training adjustments.

[0015] The real-time feedback adjustment module generates audio and video parameter adjustment commands based on anomaly detection results, achieving dynamic closed-loop adjustment of training parameters. When an anomaly is detected, the module can specifically adjust parameters such as the audio frequency, rhythm, and intensity of the headphones, or adjust parameters such as the motion guidance speed and posture standard threshold of the video training system, enabling auditory stimulation and somatosensory training to quickly regain a coordinated state. For example, when it detects that the user's movement rhythm is slower than the audio rhythm, the module can generate a command to lower the audio rhythm or increase the frequency of video motion guidance prompts to help the user rematch the rhythm. This real-time feedback adjustment mechanism avoids the lag and tediousness of manual adjustment, ensuring that the training process is always in an optimal coordinated state, further improving the effectiveness and stability of training. Attached Figure Description

[0016] Figure 1 This is a timing diagram of the headphone and video auditory-somatosensory training linkage control system described in this invention; Figure 2 A flowchart illustrating the operation of the dynamic feature extraction module; Figure 3 A flowchart illustrating how the anomaly detection engine works. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a linkage control system for audio and video auditory-kinesthetic training. The system includes: intelligent collaborative control of audio and video training by integrating multimodal data acquisition, fusion processing, feature extraction, weight allocation, anomaly detection, and real-time feedback adjustment. The system first synchronously acquires real-time audio feature data transmitted from the headphones and visual motion feature data transmitted from the video training system through a multimodal data acquisition module, ensuring the timeliness and integrity of the data sources. The data fusion processing module aligns the acquired audio and visual data according to precise timestamps, generating a unified spatiotemporal correlation matrix to provide structured input for subsequent feature analysis. The dynamic feature extraction module extracts auditory-kinesthetic coupling feature vectors from the spatiotemporal correlation matrix, capturing the intrinsic correlation between audio and motion data. The adaptive weight allocation module dynamically adjusts the weighting coefficients of the feature vectors according to the target parameters of the training phase to optimize the feature contribution under different training scenarios. The anomaly detection engine performs pattern recognition on the weighted feature vectors based on a deep residual network to detect abnormal states deviating from the normal training mode. The real-time feedback adjustment module generates audio parameter adjustment instructions and video parameter adjustment instructions based on the anomaly detection results, enabling instant adaptive adjustment of the headphones and video system.

[0019] Example 1: The implementation of the multimodal data acquisition module involves the collaborative work of multiple components. The audio feature analysis unit is responsible for processing the raw audio signal transmitted from the headphones. This unit uses signal processing technology to analyze the deep features of the audio. The sound pressure distribution spectrum is constructed by analyzing the energy intensity of the audio signal at different frequency bands, which helps to understand the spatial distribution and intensity changes of the sound. The phase difference matrix is ​​generated by comparing the phase differences between different channels in the multi-channel audio signal. This difference can reflect the directionality and spatial positioning information of the sound source. The harmonic component features are extracted by identifying the fundamental frequency and its harmonic components in the audio signal, and the amplitude and phase relationship of each harmonic is analyzed to reveal the timbre characteristics. The visual motion tracking unit acquires and processes image sequences from the video training system. Human joint motion trajectories are obtained by tracking the positional changes of key joints in continuous video frames using computer vision algorithms. This joint motion data describes the overall posture and local movements of the body. Muscle contraction frequency is estimated by analyzing the periodic movements of specific body regions or by using auxiliary sensor data, reflecting the rhythm and intensity of muscle activity. Center of gravity shift is inferred by calculating the change in the geometric center of the human posture projection, used to assess the dynamic changes in body balance. The time synchronization unit adds precise time stamps to all acquired data, using a high-precision clock source to mark each data sample with a millisecond-level timestamp, ensuring complete alignment of audio and visual data in the time dimension. The synchronization mechanism is achieved through a combination of hardware clock signals and software timestamps, enabling data from different sources to be compared and fused on a unified timeline.

[0020] The data fusion processing module integrates the collected multimodal data. The matrix construction unit combines audio features and visual motion features with the same timestamp into a multidimensional data structure. This data structure remains continuous in the time dimension and contains information from different modalities in the feature dimension. For example, sound pressure distribution spectrum and joint motion trajectory data are aligned by time frame and organized into a three-dimensional tensor. The normalization processing unit standardizes the multidimensional feature tensor, using statistical methods to transform feature values ​​of different dimensions and ranges into a unified numerical range, eliminating data bias caused by modal differences and making features from different sources comparable. The correlation analysis unit calculates the statistical correlation between different modal features, using the Pearson correlation coefficient to measure the degree of linear correlation between audio features and visual motion features. The correlation coefficient calculation is based on the analysis of covariance and standard deviation, ultimately generating a spatiotemporal correlation matrix to record the correlation strength of different features at different times and spatial locations.

[0021] The entire implementation process emphasizes the coherence and accuracy of data processing. The audio feature analysis unit uses digital signal processing algorithms to analyze the audio stream in real time, employing a combination of frequency domain analysis and time domain analysis to ensure comprehensive feature extraction. The visual motion tracking unit utilizes deep learning models for pose estimation and motion analysis, extracting spatial features from video frames through convolutional neural networks and then analyzing motion patterns between consecutive frames using a temporal model. The time synchronization unit combines hardware synchronization signals with software time management to ensure millisecond-level time accuracy even under high-speed data acquisition. The matrix construction unit designs efficient data structures to store and organize multimodal data, employing memory mapping and caching techniques to improve the processing efficiency of large datasets. The normalization processing unit dynamically adjusts normalization parameters based on the statistical distribution characteristics of feature values ​​to adapt to changes in data range under different training scenarios. The correlation analysis unit uses parallel computing technology to accelerate the correlation calculation of large-scale feature data, improving processing speed through matrix operation optimization.

[0022] In the specific implementation of multimodal data acquisition, the audio feature analysis unit continuously monitors the audio input stream. Real-time calculation of the sound pressure distribution spectrum requires the application of short-time Fourier transform to obtain time-frequency information. The calculation of the phase difference matrix requires comparing the phase relationships between multiple audio channels, and harmonic component analysis requires the use of a spectrum peak-valley detection algorithm to identify the harmonic structure. When the visual motion tracking unit processes video input, it first performs human detection and joint localization, and then establishes the motion correspondence between consecutive frames through optical flow or feature point tracking technology. The estimation of muscle contraction frequency requires combining regional motion analysis and physiological signal characteristics, and the calculation of center of gravity offset is based on the geometric distribution and weight relationship of posture points. The time synchronization unit adopts a master-slave clock architecture, using the system master clock as a reference to provide synchronization signals to all data acquisition devices, and records the acquisition time of each data point through a timestamp counter. During the data fusion processing, the matrix construction unit creates a data packet for each time point, combines the corresponding audio feature vector and visual feature vector into row vectors, and then arranges them into a feature matrix in chronological order. The normalization unit uses a sliding window approach to calculate statistical parameters and dynamically adjusts the normalization coefficients based on recent data distribution to avoid normalization bias caused by changes in data distribution. The correlation analysis unit not only calculates simple correlation coefficients between features but also analyzes cross-correlation under different time delays to identify potential time-delayed modal relationships. The entire processing flow employs a pipelined architecture, enabling data acquisition, feature extraction, and fusion processing to be performed in parallel, ensuring the system's real-time performance.

[0023] The system implementation also considers the needs of various practical application scenarios. A noise suppression algorithm is added to audio feature parsing to improve feature extraction quality in noisy environments; an occlusion handling mechanism is added to visual motion tracking to ensure tracking stability even under partial occlusion; and a clock drift compensation algorithm is added to the time synchronization mechanism to eliminate potential clock deviations during long-term operation. The data fusion processing module also includes a data quality check to identify and filter abnormal data points, ensuring the reliability of the fused data. A significance test mechanism is retained during association analysis, retaining only statistically significant associations to avoid random correlations interfering with subsequent processing. Data transmission between the multimodal data acquisition module and the data fusion processing module uses an efficient encoding format to reduce data transmission latency and storage overhead. Time synchronization information is embedded in the header of each data packet to ensure timing consistency even under high load. All processing units are designed with status monitoring and error recovery mechanisms to ensure long-term stable operation of the system. Feature data storage uses a circular buffer structure, meeting real-time processing requirements while retaining sufficient historical data for backtracking analysis. The entire implementation emphasizes the system's practicality and robustness, ensuring reliable operation in various training environments.

[0024] Example 2: See Figure 2The implementation of the dynamic feature extraction module involves extracting key information from the spatiotemporal correlation matrix. The convolutional feature extraction unit uses a three-dimensional convolutional neural network to process the input matrix data. These convolutional kernels slide across the time, space, and frequency dimensions to capture local feature patterns. By setting convolutional kernels of different sizes, both short-term transient features and long-term trend features can be captured simultaneously. The attention mechanism unit applies self-attention weights to the extracted joint features. This mechanism compares the importance relationships between feature elements through query-key pairs, enabling the model to focus on the feature regions most relevant to the current training state. The feature dimensionality reduction unit uses a variational autoencoder to compress and encode the weighted joint features. The encoder maps high-dimensional features to a low-dimensional latent space while retaining the main information. The decoder ensures the effectiveness of the feature representation through reconstruction loss, ultimately outputting a dimension-reduced auditory-kinesthetic coupled feature vector. The adaptive weight allocation module dynamically adjusts the importance of features according to the training state. The training phase identification unit determines the current phase by analyzing the continuous training time and exercise intensity indicators. The training duration is accumulated from system startup, and the exercise intensity is quantitatively evaluated through heart rate monitoring or energy consumption algorithms. The weight calculation unit retrieves feature weighting schemes matching the training phase from a pre-set weight library. This library contains pre-defined coefficient combinations for different phases such as warm-up, reinforcement, and recovery. These coefficients are designed based on training physiology principles to optimize training effectiveness. The dynamic adjustment unit monitors the rate of change of the feature vector in real time and fine-tunes the weight coefficients accordingly. The rate of change is obtained by calculating the gradient change of the feature vector within a sliding time window. When an abnormal change pattern is detected, the weight adjustment mechanism is immediately triggered.

[0025] The convolutional feature extraction unit is configured with multiple convolutional layers operating in parallel. Each layer uses convolutional kernels of different sizes to capture multi-scale feature patterns. After the convolution operation, an activation function is used to introduce non-linear transformation capabilities. The attention mechanism unit is implemented using a scaled dot product attention calculation method. First, the input features are converted into three matrices: query, key, and value. Then, the attention weight distribution is calculated through matrix multiplication and softmax normalization. The variational autoencoder in the feature dimensionality reduction unit is specially designed. The encoder consists of multiple fully connected layers and outputs the mean and variance parameters of the latent space. The sampling layer randomly extracts latent representations from this distribution, and the decoder attempts to reconstruct the original features from the latent representations. The training phase identification unit continuously monitors the real-time data stream from the multimodal data acquisition module and divides the training process into different phases through set threshold logic. For example, when the training time is below a certain threshold and the exercise intensity is low, it is determined to be a warm-up phase. The weight calculation unit maintains a queryable weight configuration database, which stores the optimal weight combinations for various training scenarios. These weight values ​​are determined through extensive experimental data analysis and expert knowledge. The dynamic adjustment unit uses an adaptive filtering algorithm to track the trend of feature changes. It evaluates the rate of change by comparing the difference between the current feature vector and historical data, and the weight fine-tuning amplitude is proportional to the rate of change.

[0026] The system's real-time requirements necessitate highly optimized computational processes. Convolutional feature extraction employs separable convolution to reduce computational complexity, while the attention mechanism utilizes multi-head attention to process different feature subspaces in parallel. The variational autoencoder training process employs reparameterization techniques to make the model differentiable, facilitating end-to-end optimization through backpropagation. The training-phase recognition algorithm is designed as a lightweight decision tree model, ensuring the real-time performance and accuracy of phase judgments. The weight database uses in-memory caching to accelerate query response, and the dynamic adjustment algorithm uses exponentially weighted moving averages to smooth the rate of change calculation. Various boundary conditions are considered during implementation; interpolation techniques are used to supplement feature values ​​when convolutional feature extraction encounters missing data, and the attention mechanism is robust to outliers to prevent weight allocation distortion. Regularization constraints are incorporated during variational autoencoder training to prevent overfitting and ensure that the dimensionality-reduced features retain discriminative power. The training-phase recognition includes a smooth transition mechanism to avoid excessively frequent phase switching, and a default fallback scheme is implemented for weight calculation, using conservative weight values ​​when database queries fail. The dynamic adjustment algorithm includes a rate of change threshold mechanism; only significant changes exceeding the threshold trigger weight adjustments. Data exchange between modules adopts a unified interface specification. The convolutional feature extraction unit outputs feature maps in a standardized format, and the attention mechanism unit generates a weight matrix of the same dimension as the input. The variational autoencoder is designed with a symmetric structure to ensure consistency between encoding and decoding. During the training phase, the recognition unit outputs stage labels and confidence scores. The weight calculation unit returns weight vectors and version information, and the dynamic adjustment unit outputs the final adjusted weight coefficients. All data processing steps include integrity checks to ensure the reliability of data transmission between modules.

[0027] The system implementation also emphasizes computational efficiency optimization. Convolutional operations utilize the `im2col` technique to accelerate matrix multiplication, and attention computation is reduced in memory usage through block processing. The variational autoencoder employs a distributed training strategy to accelerate model convergence, and incremental update algorithms are used during the training phase to reduce computational overhead. An indexed structure is established in the weight database to improve query efficiency, and the dynamic adjustment algorithm uses numerical approximation methods to simplify the calculation process. The entire module design supports parallel pipelined processing, ensuring real-time performance requirements are met with limited hardware resources. Error handling and recovery mechanisms are also crucial components of the implementation. Convolutional feature extraction incorporates gradient clipping to prevent numerical overflow, and the attention mechanism includes normalization protection to avoid division-by-zero errors. The variational autoencoder monitors KL divergence during training to prevent posterior collapse, and the training phase includes a timeout reset mechanism to handle abnormally long training periods. The weight database undergoes regular backups and version management, and the dynamic adjustment algorithm incorporates boundary checks to prevent weight coefficients from exceeding limits. All modules include health status monitoring capabilities, enabling automatic anomaly detection and initiation of recovery processes.

[0028] Example 3: See Figure 3The anomaly detection engine is built upon a deep residual network architecture. The residual network building blocks are designed with a deep convolutional neural network structure containing skip connections. This design directly transmits low-level features to high-level features through identity mapping, effectively mitigating the vanishing gradient problem in deep networks. The network consists of multiple stacked residual blocks, each containing a convolutional layer, a normalization layer, and an activation function. The feature comparison unit measures the similarity between the real-time generated auditory-kinesthetic coupled feature vectors and reference templates in the standard feature library. The similarity calculation uses the cosine similarity method to evaluate the directional consistency between the two vectors. The standard feature library stores a large number of feature samples collected under normal training mode as comparison benchmarks. The anomaly scoring unit outputs anomaly probability values ​​based on the degree of feature difference. The difference is converted into a probability score between 0 and 1 after normalization of the similarity calculation results. A higher probability value indicates a greater likelihood that the current training state deviates from the normal mode.

[0029] The residual network building blocks are implemented using multi-scale convolutional layers, gated recurrent layers, and feature fusion layers. The multi-scale convolutional layers use kernels of different sizes to process input features in parallel, simultaneously capturing short-term transient patterns and long-term trend features. The gated recurrent layers model the temporal dependencies of feature sequences using a gated recurrent unit structure, controlling the retention and forgetting of historical information through update and reset gate mechanisms. The feature fusion layer cross-integrates multi-scale features with time-series features, achieving information fusion through a combination of feature concatenation and weighted summation. The entire residual network is trained end-to-end, with network parameters optimized using the backpropagation algorithm.

[0030] During the forward propagation of the residual network, the computation of each residual block can be represented as:

[0031] in: Indicates the first disability Input features of the difference block, This represents the residual function (which typically contains multiple convolution and activation operations). This represents the learnable parameters of the residual block. This represents the output characteristics of the residual block. This design allows the network to learn the residual mapping between the input and output, rather than directly learning the unreferenced mapping.

[0032] The feature comparison unit employs an approximate nearest neighbor search algorithm to accelerate the querying of a large-scale feature library, and improves the efficiency of similarity calculation by constructing a feature index structure. The standard feature library is stored and categorized by training stage and motion type, with each category containing multiple validated normal training feature samples. Similarity calculation uses a batch processing approach, comparing simultaneously with multiple reference templates, and taking the highest similarity as the final result. The comparison process also includes a consistency check mechanism to exclude reference templates that are clearly inconsistent with the current training scenario. The anomaly scoring unit maps similarity values ​​to anomaly probabilities, and the mapping function uses a non-linear transformation to enhance sensitivity to anomalies. Probability calculation incorporates time smoothing, considering historical anomaly scoring trends to avoid instantaneous misjudgments. The scoring output includes a confidence assessment, reflecting the reliability of the current score. The anomaly threshold is dynamically adjusted according to the training stage, with different sensitivity requirements set for different stages.

[0033] The multi-scale convolutional layers are implemented using grouped convolution techniques, with convolutional kernels of different scales processing input features in parallel. The output feature maps are then concatenated and fed into subsequent layers. Gated recurrent layers maintain hidden states and remember historical information, controlling information flow through a gating mechanism. The feature fusion layer employs an adaptive weighting mechanism to dynamically adjust the contribution ratio of different feature sources. Network training uses a weighted loss function to improve the detection capability for rare and anomalous patterns. The standard feature library is implemented using a distributed storage architecture, supporting fast querying and dynamic updates. Feature samples undergo rigorous screening and labeling to ensure the quality and representativeness of the reference templates. Each feature vector in the library comes with rich metadata information, including acquisition time, training type, and user characteristics. The feature comparison algorithm supports online learning, gradually updating the reference template set based on newly acquired normal samples.

[0034] The anomaly scoring algorithm incorporates a time window mechanism, comprehensively considering anomalies at multiple recent time points. The scoring output includes trend analysis, indicating the development direction of the anomaly. Probability calculation employs calibration techniques to ensure that the output probability values ​​are consistent with the actual anomaly frequency. The scoring system also includes a feedback learning mechanism, adjusting scoring parameters based on manual confirmation. The entire anomaly detection engine adopts a modular design, facilitating individual optimization and updates. The residual network supports online fine-tuning, adapting to the feature distributions of different users. The feature comparison unit supports multiple similarity measurement methods, allowing selection of the most suitable algorithm based on specific scenarios. The anomaly scoring unit provides configurable sensitivity parameters to meet different levels of detection stringency. The system implementation also includes complete logging and debugging interfaces for performance analysis and troubleshooting. Network training uses a large-scale labeled dataset, employing data augmentation techniques to improve model generalization ability. The training process includes regularization constraints to prevent overfitting. Model evaluation uses cross-validation to ensure the stability of detection performance. The inference process optimizes the computational graph, improving real-time processing efficiency. The entire system design considers resource constraints, optimizing computational and storage overhead while ensuring detection accuracy.

[0035] Example 4: The implementation of the real-time feedback adjustment module achieves dynamic adjustment of training parameters through the collaborative work of multiple components. The instruction generation unit receives the anomaly probability value and type identifier from the anomaly detection engine and maps them to specific device adjustment instructions. This unit internally maintains audio parameter mapping tables and video parameter mapping tables. The audio parameter mapping table defines the audio gain, equalizer settings, and dynamic range control parameters corresponding to different anomaly types, while the video parameter mapping table specifies the adjustment schemes for video frame rate, resolution, and rendering quality under various anomaly conditions. The parameter optimization unit uses numerical optimization methods to find the optimal parameter combination. It constructs an objective function to measure the degree of matching between the parameter adjustment effect and the training objective, and uses an iterative algorithm to solve for the parameter values ​​that optimize the objective function. The device control unit converts the optimized parameters into control signals recognizable by the device, sends the audio parameters to the headphone driver system via a standard communication protocol, and transmits the video parameters to the video rendering engine. Refer to Table 1, which shows the mapping relationship between anomaly types and adjustment parameters.

[0036] Table 1: Mapping Table of Anomaly Types and Parameter Adjustment Exception type identifier Exception Description Audio gain adjustment (dB) Equalizer band adjustment Video frame rate compensation (%) Rendering quality level A001 rhythm out of sync +3.0 Low frequency enhancement +2dB -5 high A002 Distraction +1.5 Mid-frequency boost +3dB +0 middle A003 Inadequate motor coordination -2.0 Uniform enhancement across the entire frequency band +1dB +10 high A004 Physiological overload -1.0 High frequency attenuation -4dB -15 Low A005 Spatial perception bias +0.5 Stereo field expansion +20% +5 middle The parameter optimization unit is implemented based on the gradient descent algorithm to find the optimal parameter configuration. This algorithm determines the direction of parameter adjustment by calculating the partial derivatives of the objective function with respect to each parameter. The objective function comprehensively considers multiple factors such as anomaly scores, user comfort, and training effectiveness. During each iteration, the algorithm fine-tunes the parameter values ​​and evaluates the improvement of the objective function. Optimization stops when the improvement after multiple consecutive iterations is less than a set threshold. The optimization process also includes a constraint handling mechanism to ensure that parameter values ​​are always within a safe range acceptable to the device. The device control unit uses a bidirectional communication architecture to interact with the terminal device, sending digital audio processing parameters to the headphone driver system, including multi-band equalizer settings, dynamic range compression threshold, and limiter parameters. The video rendering engine receives video parameters including frame generation strategy, resolution scaling ratio, and texture detail level. These parameters collectively affect the smoothness and clarity of visual training. The control command transmission employs a redundancy check mechanism to ensure the accuracy and reliability of parameter settings. In actual operation, the command generation unit scales the parameter adjustment amount according to the severity of the anomaly. Higher anomaly scores result in larger parameter adjustments, while lower anomaly scores correspond to smaller parameter adjustments. The mapping table lookup process includes fuzzy matching capabilities, enabling it to handle undefined anomaly types. The parameter optimization unit maintains a history of parameter adjustments to prevent frequent oscillations in parameter settings. The device control unit implements a heartbeat detection mechanism to monitor device status in real time and ensure parameters take effect.

[0037] In specific application scenarios, when the system detects a rhythm desynchronization anomaly, the instruction generation unit queries the mapping table to obtain the corresponding parameter adjustment scheme. The parameter optimization unit fine-tunes the baseline parameters based on the current training intensity and environmental factors, ultimately generating adjustment instructions to increase audio gain by 3dB and enhance the low-frequency band, while simultaneously reducing the video frame rate by 5% to maintain audio-visual synchronization. The device control unit encapsulates these parameters into a device control protocol data packet and sends it to the headphones and video system via a wireless connection. The system implementation also considers the smooth transition of parameter adjustments; audio parameter changes use a fade-in / fade-out method to avoid abrupt auditory changes, and video parameter adjustments achieve gradual changes through interpolation algorithms. All parameter adjustments have been ergonomically tested to ensure that the adjusted parameters conform to human perception characteristics. The device control protocol includes version compatibility handling to adapt to the parameter range differences of different device models. Anomaly type identification and parameter mapping relationships are established based on actual experimental data, and the optimal adjustment strategy for different anomalies is determined through extensive training scenario testing. The parameter optimization algorithm includes adaptive learning capabilities, which can gradually adjust the parameter mapping relationship according to the user's personal preferences. The device control module supports multiple communication interfaces, including Bluetooth, Wi-Fi, and wired connections, ensuring compatibility with various training devices. The entire real-time feedback adjustment process employs a closed-loop control approach. After parameter adjustments, it monitors for abnormal score changes, triggering a new round of optimization calculations if the results are unsatisfactory. The system also includes a manual adjustment interface, allowing trainers to override automatic adjustment results based on actual conditions. All parameter adjustment operations are logged for easy later analysis and system optimization.

[0038] Example 5: Implementation of the Cloud Collaboration Module. Built upon a distributed system architecture, this module is responsible for uploading key feature data and adjustment records generated during training to a cloud server for centralized management. After each training session, the system automatically extracts statistical summaries of auditory-kinesthetic coupling feature vectors, time series of anomaly scores, and the final combination of adjustment parameters. This data, after anonymization, is transmitted to the cloud platform via an encrypted channel. The cloud server employs a sharded storage strategy to manage multi-user data, establishing an index structure based on timestamps and anonymous user identifiers to support efficient data retrieval and analysis queries. Differential privacy technology is used during data upload to protect sensitive user information, preserving statistical characteristics while preventing individual information leakage. The incremental learning module continuously optimizes the anomaly detection model based on massive amounts of multi-user data collected in the cloud. This module periodically downloads new training samples from the cloud and updates the weight parameters of the deep residual network through online learning. The model update process uses a mini-batch gradient descent algorithm, randomly sampling a small number of samples from the new dataset each time to calculate the gradient and update the network parameters. This progressive learning approach allows the model to gradually adapt to new data distributions without losing existing knowledge. The incremental learning process incorporates a catastrophic forgetting prevention mechanism, using elastic weight consolidation techniques to protect critical connection weights from excessive modification and maintain the model's ability to remember learned patterns. The personalized adaptation module generates customized feature weight schemes based on users' historical training data. This module analyzes a single user's long-term accumulated training records to identify their unique movement patterns, physiological response characteristics, and training preferences. The analysis process employs clustering algorithms to discover the feature distribution patterns of users under different training states, establishing a user-specific feature weight configuration file. This configuration file includes optimized auditory and somatosensory feature weight ratios for that user, as well as personalized settings for anomaly detection thresholds. These parameters will be used by the adaptive weight allocation module and the anomaly detection engine during subsequent training.

[0039] In a specific operational instance, after a user completes a high-intensity interval training session, the cloud-based collaborative module compresses and uploads a summary of the training data, including an average anomaly probability of 0.23, the occurrence of the main anomaly type A003 (insufficient motor coordination) 7 times, and a final audio gain of -1.8dB. This data is merged and stored with the user's historical records to form a continuous personal training profile. The incremental learning module initiates an update process during low-load periods at night, downloading newly generated training data from users worldwide over the past week and incorporating new anomaly pattern features while maintaining the original model performance. The personalized adaptation module recalculates the user's feature weight configuration weekly, identifying a decrease in the user's sensitivity to audio cues in the high heart rate zone and adjusting the auditory feature weights accordingly for this state. The cloud server implements a multi-tenant architecture, providing each training institution with independent data storage space and computing resources. The data upload interface supports breakpoint resumption and compressed transmission, adapting to stability requirements under different network environments. The incremental learning task employs a distributed computing framework, distributing model update tasks to multiple computing nodes for parallel processing. Personalized configuration generation uses privacy computing technology, performing sensitive data analysis locally before uploading anonymized results.

[0040] Data security management is implemented throughout the entire process. Uploaded data undergoes rigorous anonymization, removing all direct identifiers and employing generalization techniques to handle quasi-identifiers. End-to-end encryption is used in the transmission channel to prevent data theft or tampering during transmission. Cloud storage implements access control policies, allowing only authorized system components to access specific types of data. Model update packages are digitally signed to ensure model integrity and source credibility. The incremental learning algorithm design includes a model performance monitoring mechanism, testing model performance on a validation set after each update; if performance degradation exceeds a threshold, a rollback to the previous version is initiated. The personalized adaptation module includes change detection functionality, prompting for configuration regeneration when significant changes in the user's training mode are detected. The system also provides a manual intervention interface, allowing trainers to fine-tune automatically generated personalized parameters. The cloud collaboration module implements an intelligent scheduling strategy, determining data upload timing and compression ratios based on network conditions and device battery level. Higher compression ratios are used to reduce data volume when wireless network signals are weak, while more detailed data records are transmitted in Wi-Fi environments. The incremental learning process considers device resource limitations, performing large-scale model updates only when charging and idle. Personalized configuration updates are performed incrementally, recalculating only the changed parameters each time. The system implementation also includes a complete version management mechanism, storing all historical model and configuration versions in the cloud, supporting rapid rollback and comparative analysis. A data quality monitoring module checks the integrity and rationality of uploaded data, filtering out data records with obvious errors or anomalies. The model update process records detailed change logs, including performance changes and parameter adjustments. Personalized configuration generation retains the decision-making basis, facilitating understanding of the logic and reasons for parameter adjustments. Multiple modules collaborate through clearly defined interface protocols. The cloud collaboration module provides standardized data upload and query interfaces, the incremental learning module defines the model update package format and version compatibility rules, and the personalized adaptation module specifies the data structure and semantic standards for configuration files. This modular design gives the system good scalability and maintainability, facilitating subsequent feature enhancements and performance optimizations. The entire implementation prioritizes user experience and privacy protection, ensuring data security and compliance while providing personalized services.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An earphone and video audio-vestibular training linkage control system, characterized in that, The system comprises: A multi-modal data acquisition module for synchronously acquiring real-time audio feature data transmitted by earphones and visual motion feature data transmitted by a video training system; A data fusion processing module for generating a space-time correlation matrix after aligning the real-time audio feature data and visual motion feature data according to timestamps; A dynamic feature extraction module for extracting an auditory-somatosensory coupling feature vector from the space-time correlation matrix; An adaptive weight allocation module for dynamically adjusting weighting coefficients of the auditory-somatosensory coupling feature vector according to target parameters of a current training stage; An anomaly detection engine for performing anomaly pattern recognition on the weighted auditory-somatosensory coupling feature vector based on a deep residual network; A real-time feedback adjustment module for generating audio parameter adjustment instructions and video parameter adjustment instructions according to anomaly detection results.

2. The headphone and video-auditory-somatosensory training linkage control system according to claim 1, wherein, The multi-modal data acquisition module comprises: An audio feature analysis unit for extracting sound pressure distribution spectrum, phase difference matrix and harmonic component features from earphone transmission signals; A visual motion tracking unit for extracting human joint motion trajectories, muscle contraction frequencies and center of gravity offsets from a video training system; A time synchronization unit for attaching millisecond-level timestamps to the audio feature data and visual motion feature data.

3. The headphone and video-auditory-somatosensory training linkage control system according to claim 1, wherein, The data fusion processing module comprises: A matrix construction unit for combining audio feature data and visual motion feature data of the same timestamp into a multi-dimensional feature tensor; A normalization processing unit for performing cross-modal data standardization on the multi-dimensional feature tensor; A correlation analysis unit for calculating Pearson correlation coefficients between different modal features and generating the space-time correlation matrix.

4. The headphone and video-auditory-somatosensory training linkage control system according to claim 1, wherein, The dynamic feature extraction module comprises: A convolution feature extraction unit for extracting time-space-frequency joint features from the space-time correlation matrix through a three-dimensional convolution kernel; An attention mechanism unit for calculating self-attention weights of the time-space-frequency joint features; A feature dimension reduction unit for compressing weighted joint features into low-dimensional auditory-somatosensory coupling feature vectors through a variational autoencoder.

5. The headphone and video-auditory-somatosensory training linkage control system of claim 1, wherein, The adaptive weight allocation module comprises: A training stage identification unit for determining a current training stage according to training duration and training intensity; A weight calculation unit for calling a corresponding feature weighting scheme from a preset weight library based on the training stage; A dynamic adjustment unit for fine-tuning specific coefficients in the feature weighting scheme according to a real-time feature change rate.

6. The headphone and video-auditory-somatosensory training linkage control system of claim 1, wherein, The anomaly detection engine comprises: A residual network construction unit for constructing a deep convolutional neural network containing a skip connection; A feature comparison unit for performing similarity calculation on the current auditory-somatosensory coupling feature vector and a reference vector in a standard feature library; An anomaly scoring unit for outputting an anomaly probability value according to feature difference degree.

7. The headphone and video-auditory-somatosensory training linkage control system according to claim 6, wherein, The residual network construction unit specifically comprises: A multi-scale convolution layer for simultaneously extracting feature patterns of different time scales; A gated recurrent layer for capturing time dependence of feature sequences; A feature fusion layer for cross-fusing multi-scale features and time sequence features.

8. The headphone and video-auditory-somatosensory training linkage control system of claim 1, wherein, The real-time feedback adjustment module comprises: An instruction generating unit is configured to map the abnormality detection result to specific audio gain parameters and video playback parameters; A parameter optimization unit is configured to solve the optimal adjustment parameter combination by using a gradient descent method; A device control unit is configured to send the optimized parameters to an earphone driving system and a video rendering engine, respectively.

9. The headphone and video-auditory-somatosensory training linkage control system of claim 8, wherein, The instruction generating unit specifically includes: An audio parameter mapping table configured to store equalizer adjustment schemes corresponding to different abnormality types; A video parameter mapping table configured to store frame rate compensation schemes corresponding to different abnormality types; A joint optimization strategy library configured to store optimization strategies for cross-device collaborative adjustment.

10. The headphone and video-auditory-somatosensory training linkage control system of claim 1, wherein, The system further includes: A cloud collaboration module configured to upload key feature data and adjustment records in the training process to a cloud server; An incremental learning module configured to continuously optimize the detection model of the abnormality detection engine based on multi-user data collected by the cloud; A personalized adaptation module configured to generate a customized feature weight scheme according to historical training data of a user.

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