Cloud-Edge Haptic Signal Reconstruction via Audio-Visual Fusion
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Solution Overview
Problem
Existing haptic signal reconstruction methods face challenges in generating accurate haptic signals due to sensitivity to interference and noise in wireless communication, especially in remote operations, and rely heavily on large-scale training data, which is not always available, and fail to fully utilize multi-modal information.
Innovation Solution
An audio-visual-aided haptic signal reconstruction method based on cloud-edge collaboration using self-supervision learning and multi-modal feature fusion to extract semantic features from sparse data, combining pre-trained audio and video feature extraction networks with a haptic feature extraction network to generate complete haptic signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signal processing technology is used to reconstruct haptic signals from damaged signals, then some reconstruction can be achieved, but the reconstruction accuracy deteriorates when signals are seriously damaged or incomplete
Solution Approach 1:
The patent introduces cross-modal information (audio and video signals) as an intermediary to reconstruct the damaged haptic signal. Instead of relying solely on the damaged haptic signal itself, the system uses audio and video signals as intermediate representations that contain complementary information about the haptic characteristics, enabling accurate reconstruction even when the original haptic signal is seriously damaged or incomplete.
Solution Approach 2:
The patent transitions from single-modality reconstruction to multi-modality reconstruction by adding audio and video dimensions. The system reconstructs haptic signals by integrating information from multiple modalities (haptic, audio, video), effectively adding dimensional complexity to the reconstruction process and improving accuracy when traditional single-modality methods fail.
2Adaptability or versatility
If existing cross-modal generation methods are used to generate haptic signals from audio and video, then some haptic information can be obtained, but the generation accuracy deteriorates due to limited category information and incomplete data
Solution Approach 1:
The patent merges audio and video signal processing into a unified multi-modality reconstruction framework. Instead of treating audio and video separately or using only one modality, the system combines both audio and video features with haptic features through integrated neural networks, enabling more accurate and comprehensive haptic signal generation that leverages the complementary information from all modalities.
Solution Approach 2:
The patent employs deep neural networks with multiple layers to transform and process multi-modality data. The system changes the representation parameters of audio, video, and haptic signals through learned transformations, mapping them into a shared latent space where they can be effectively combined and used to generate accurate haptic signals that reflect the underlying physical characteristics.
3Quantity of substance
If multi-modal data is collected for training, then more information is available, but data collection complexity and storage requirements increase
Solution Approach 1:
The patent performs preliminary feature extraction and representation learning during the training phase using pre-collected multi-modal data. The system pre-processes audio, video, and haptic signals to extract meaningful features and builds neural network models that can efficiently process this information. This preliminary action allows the system to handle complex multi-modal data during actual application without requiring real-time complex processing, reducing operational complexity.
Data Source
AI summary
An audio visual haptic signal reconstruction method includes first utilizing a large-scale audio-visual database stored in a central cloud to learn knowledge, and transferring same to an edge node; then combining, by means of the edge node, a received audio-visual signal with knowledge in the central cloud, and fully mining semantic correlation and consistency between modals; and finally fusing the semantic features of the obtained audio and video signals and inputting the semantic features to a haptic generation network, thereby realizing the reconstruction of the haptic signal. The method effectively solves the problems that the number of audio and video signals of a multi-modal dataset is insufficient, and semantic tags cannot be added to all the audio-visual signals in a training dataset by means of manual annotation. Also, the semantic association between heterogeneous data of different modals are better mined, and the heterogeneity gap between modals are eliminated.