End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

2025-03-06

AI Summary

This work addresses the challenge of high-fidelity 3D human pose reconstruction from wearable sensor data under 6G extended reality (XR) constraints—namely, high noise, severe channel distortion, and low-bit quantization. Method: We propose a novel two-stage deep neural receiver: Stage I jointly performs OFDM channel estimation and symbol decoding; Stage II directly maps the decoded symbols to 3D joint poses—bypassing conventional decoupled signal processing pipelines. Contribution/Results: Theoretical analysis and experiments demonstrate that 8-bit quantization suffices for high-accuracy reconstruction. Compared to the LS channel estimation + LMMSE equalization baseline, our method achieves a 5 dB BER improvement (reaching ~10⁻⁴), reduces joint angle error by 37%, and attains a sensor signal reconstruction MSE as low as 5×10⁻⁴. This establishes a new end-to-end paradigm unifying communication and sensing for pose estimation.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at $10^{-4}$ BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of $5 imes10^{-4}$ for reconstructed sensor signals, and reducing joint angular error by 37% for the reconstructed human poses compared to the baseline.

Problem

• Accurate 3D human pose reconstruction in 6G networks
• Overcoming wireless channel impairments and quantization effects
• Deep learning-based framework for improved pose estimation accuracy

Innovation

• Deep learning-based framework for pose reconstruction
• Two-stage neural receiver for OFDM systems
• 8-bit quantization for accurate sensor signal reconstruction