Comparison of Fundamental Radar Features for Differentiating Between Walking and Standing in Horizontal and Vertical Movement Directions

This study investigates the feasibility of using a basic 24 GHz one-dimensional (1D) radar for Human Activity Recognition (HAR), focusing on differentiating between walking and standing movements.We evaluate the radar's performance using various machine learning models, including K-means, GMM, SVM, and LSTM.Using the silhouette score and the Davies-Bouldin index, we evaluate the intra-and interclass results of K-means and GMM, while SVM and LSTM are used to analyze their performance.The results indicate that the LSTM model achieves high accuracy in both vertical and horizontal dimensions, with precision, recall, and F1-scores all above 98% for both standing and walking movements.However, the SVM model faces challenges in horizontal movement detection, consistent with the unsupervised learning results where the inter-class and intra-class distances for the horizontal dimension are not significant, making differentiation difficult.These findings delineate the boundaries and capabilities of a lower-specification radar for HAR, providing insights into its practical applications and limitations.