Advanced Gesture Recognition Method Based on Fractional Fourier Transform and Relevance Vector Machine for Smart Home Appliances

Abstract

Addressing the challenges of low feature extraction dimensions and insufficient distinct information for gesture differentiation for smart home appliances, this article proposed an innovative gesture recognition algorithm, integrating fractional Fourier transform (FrFT) with relevance vector machine (RVM). The process involves using FrFT to transform raw gesture data into the fractional domain, thereby expanding the dimensions of information extraction. Subsequently, high-dimensional feature vectors are created from fractional domain, and RVM classifiers are employed for joint optimization of feature selection and classification decision functions, achieving optimal classification performance. A dataset was constructed using five different types of gestures recorded on the TI millimeter-wave radar platform to validate the effectiveness of this method. The experimental results demonstrate that the RVM selected the optimal FrFT order of 0.6, with the best feature set comprising fractional spectral entropy, peak factor, and second-order central moment. Recognition rates for each gesture exceeded 96.2%, with an average rate of 98.5%. This performance surpasses three comparative methods in both recognition accuracy and real-time processing, indicating high potential for future applications.