Robust Radar-driven Gesture Recognition for Contactless Human-computer Interaction Using Support Vector Machine and Signal Feature Optimization

Radar-based gesture recognition has emerged as a reliable alternative to vision-based systems for human-computer interaction, especially in environments with low illumination, occlusion, or privacy constraints. This study explores the implementation of a radar-based gesture recognition system using advanced signal processing and machine learning techniques to classify dynamic hand movements with high precision. The central challenge addressed involves extracting discriminative features from radar signals and developing robust classifiers capable of performing effectively under real-world conditions. The proposed approach includes preprocessing radar data through bandpass filtering (5-50 Hz) and normalization, followed by the extraction of key features such as signal energy, mean Doppler shift (7.6-7.9 Hz), and spectral centroid. A Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel is employed and optimized for gesture classification. Comparative analysis reveals that the SVM model outperforms the K-nearest neighbors (KNN) method, achieving a classification accuracy of 86% and an F1-score of 0.89, compared to 82% accuracy and a 0.84 F1-score obtained with KNN at. These results demonstrate the effectiveness of radar-based systems in detecting and classifying hand gestures accurately, achieving up to 97.3% accuracy in controlled environments. Unlike traditional camera-based systems, radar maintains functionality in poor lighting and occluded conditions while preserving user privacy by avoiding optical recordings. The system also offers low power consumption and real-time processing capabilities, making it suitable for deployment in privacy-sensitive and resource-constrained applications. This work confirms radar’s potential in fine-grained gesture interpretation and aligns with prior studies in crowd tracking and digit recognition, where similar performance metrics were observed. The integration of radar sensing with machine learning offers a promising path toward more secure, responsive, and environment-agnostic interaction systems.