# A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar

## TL;DR

This paper introduces a new deep learning model that improves human skeleton estimation from FMCW radar data, achieving higher accuracy and efficiency.

## Contribution

A novel deep learning framework combining CNNs, transformers, and Bi-LSTM for enhanced skeleton estimation from sparse radar data.

## Key findings

- The model achieves a mean absolute error of 1.77 cm in joint localization.
- It reduces estimation errors significantly compared to conventional methods.
- The approach maintains computational efficiency while improving accuracy.

## Abstract

Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. Our approach introduces a frame concatenation strategy that improves data quality before processing through the neural network pipeline. Experimental evaluations on the MARS dataset demonstrate that our model outperforms conventional methods by significantly reducing estimation errors, achieving a mean absolute error (MAE) of 1.77 cm and a root mean squared error (RMSE) of 2.92 cm while maintaining computational efficiency.