Stability-Enhanced Human Activity Recognition With a Compact Few-Channel mm-Wave FMCW Radar

Abstract

This study explores the application of a 77 GHz mm-wave frequency-modulated continuous wave (FMCW) radar system for human activity recognition (HAR). We propose a novel density-aware convex hull (DACH) algorithm specifically designed to address the challenge of point cloud sparsity, which is particularly evident when using few-channel radar systems. This algorithm combines a triple-view convolutional neural network (CNN) and long short-term memory (LSTM) models for classification. Unlike traditional methods that often overlook the impact of sparse point cloud data, our approach emphasizes the importance of maintaining robust and dense data for precise activity recognition. Our experiments, which involved classifying nine human activities—standing, sitting, squatting, lying on the floor, transitions between these postures, and walking—demonstrate the method’s effectiveness. By using a compact 3Tx4Rx few-channel radar, we achieve a balance among cost, size, and performance, making it suitable for practical applications like indoor health monitoring and elderly care. The proposed method achieves an average classification accuracy of approximately 99.63% across all four scenarios, marking a significant improvement over existing approaches, and shows promise for real-time applications in various fields.