Deep Learning-based Human Gesture Channel Modeling for Integrated Sensing and Communication Scenarios

TL;DR

This paper introduces a deep learning framework for modeling human gesture channels in 6G ISAC systems, enabling accurate, interpretable, and generalizable simulation of gesture-induced wireless channel variations.

Contribution

It presents a novel deep learning approach combining Poisson neural networks and C-VAEs to model gesture-related multipath channels from real data, improving accuracy and interpretability.

Findings

High accuracy in predicting multipath components across gestures

Effective generation of scattering points for channel reconstruction

Good generalization to different subjects and gestures

Abstract

With the development of Integrated Sensing and Communication (ISAC) for Sixth-Generation (6G) wireless systems, contactless human recognition has emerged as one of the key application scenarios. Since human gesture motion induces subtle and random variations in wireless multipath propagation, how to accurately model human gesture channels has become a crucial issue for the design and validation of ISAC systems. To this end, this paper proposes a deep learning-based human gesture channel modeling framework for ISAC scenarios, in which the human body is decomposed into multiple body parts, and the mapping between human gestures and their corresponding multipath characteristics is learned from real-world measurements. Specifically, a Poisson neural network is employed to predict the number of Multi-Path Components (MPCs) for each human body part, while Conditional Variational Auto-Encoders (C-VAEs) are reused to generate the scattering points, which are further used to reconstruct continuous channel impulse responses and micro-Doppler signatures. Simulation results demonstrate that the proposed method achieves high accuracy and generalization across different gestures and subjects, providing an interpretable approach for data augmentation and the evaluation of gesture-based ISAC systems.