A multi-point haptic intelligent encoding and decoding device, system and method

By optimizing the encoding and decoding process of tactile signals through a neural network autoencoder and a multi-scale perception calibration module, the problems of signal distortion and long latency in existing technologies are solved, achieving efficient, low-latency transmission and high-quality reconstruction of multi-point tactile signals, which is suitable for scenarios such as virtual reality and medical robots.

CN120786067BActive Publication Date: 2026-07-03HUAZHONG UNIV OF SCI & TECH
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Patent Information

Application Number
CN202511025744.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-24
Publication Date
2026-07-03
Estimated Expiration
2045-07-24

AI Technical Summary

Technical Problem

Existing tactile encoding and decoding methods are unable to fully characterize the overall characteristics of tactile signals, resulting in signal distortion during recovery, long encoding delays, and difficulty in achieving synchronous transmission of multi-point tactile signals. They also fail to fully consider the nonlinear perception characteristics of human tactile receptors and have insufficient optimization metrics.

Method used

A neural network autoencoder is used for feature extraction and compression, a channel attention prediction is performed by combining a channel sensing module and a weighted fusion module, a dynamic grouped residual quantizer is used for quantization, and a multi-scale sensing calibration module is used to optimize the reconstructed signal. A hybrid loss function is constructed for training to achieve end-to-end transmission of multi-channel vibration tactile signals.

Benefits of technology

It improves the reconstruction quality of tactile signals, reduces transmission latency, and achieves low-latency synchronous transmission and high-quality reconstruction of multi-channel signals, making it suitable for high-precision tactile interaction scenarios such as virtual reality, remote operation, and medical robots.

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Abstract

The application belongs to the technical field of tactile communication and intelligent human-computer interaction, and discloses a multi-point tactile intelligent encoding and decoding device, system and method. An acquisition end obtains original tactile signals, and extracts features through a neural network self-encoder. A weighted fusion module based on an attention mechanism dynamically groups and quantizes residual errors of the encoded features in combination with channel state information (CSI), so as to realize low-code-rate compression transmission. A perception end inversely quantizes received data and reconstructs signals, and optimizes output tactile signals in combination with a multi-scale perception calibration mechanism, so that the output tactile signals are closer to original inputs in the human perception dimension. A multi-point tactile feedback device obtains vibration feedback corresponding to real-time multi-channel vibration tactile signals based on the reconstructed signals. The application realizes efficient compression, low-delay transmission and high-quality reconstruction of tactile signals by fusing a deep learning architecture and a psychological tactile perception model, and is suitable for virtual reality, remote operation, medical robots and other high-precision tactile interaction scenes.
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