Transformer-Based Heartbeat Monitoring with FMCW Radar Under Random Body Motion

Millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar enables contactless cardiac monitoring, but heartbeat estimation becomes challenging when respiration and random body motion (RBM) distort the radar signal. In this paper, we propose a hybrid framework for 77 GHz FMCW radar that combines model-based signal processing with a Convolutional Neural Network (CNN)-Transformer network. The first block extracts chest displacement and constructs meaningful high-level motion features from raw radar data, while the second block reconstructs a photoplethysmography (PPG)-like signal from the extracted features. In this study, a synchronized PPG signal is used as the ground truth for heartbeat monitoring in supervised training. The method is evaluated following the IEEE AESS Radar Challenge Problem I protocol using the official datasets and figures of merit across three motion scenarios: stationary, deep breathing, and RBM. Results show that the proposed architecture reliably reconstructs the PPG signal in all scenarios, achieving high fidelity in controlled conditions and maintaining robust performance under motion. This enables reliable average heart rate (AHR) and heart rate variability (HRV) estimation even where benchmark methods fail, and leads to the highest total score among the compared approaches.

Analysis of Model-Free Reinforcement Learning Control Schemes on self-balancing Wheeled Extendible System

Traditional linear control strategies have been extensively researched and utilized in many robotic and industrial applications and yet they don't respond to the total dynamics of the systems. To avoid tedious calculations for nonlinear control schemes like H infinity control and Predictive Control, the application of Reinforcement Learning can provide alternative solutions. This article presents the implementation of RL control with Deep Deterministic Policy Gradient and Proximal Policy Optimization on a mobile self-balancing Extendible Wheeled Inverted Pendulum (E-WIP) system. Such RL models make the task of finding a satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control. In this article, two RL-based controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory.