Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Abstract

We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.

Deep-Learning Methods for Hand-Gesture Recognition Using Ultra-Wideband Radar

“…The most common machine-learning approaches for radar-based hand-gesture recognition are CNN, SVM, k-NN, and LSTM. In order to classify hand-gestures using classifiers such as SVM and k-NN, one of the techniques is the manual extraction of handgesture features [1], [14], [15] from the range-Doppler or time-frequency (spectrogram) maps. Manual feature extraction requires predefined characteristic features of the gesture signatures, and therefore, the performance of the classifier varies significantly depending on the defined features.…”

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

“…Propelled by successes in discriminating between different human activities, radar has recently been employed for automatic hand gesture recognition for interactive intelligent devices [1][2][3][4][5][6]. This recognition proves important in contactless close-range hand-held or arm-worn devices, such as cell phones and watches.…”

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

“…In wireless communications, the compound Doppler effect caused by the moving objects or bodies opened up opportunities for many applications. These applications track the scattered wave components by the moving bodies for drone detection [1], gesture recognition [2], human gait assessment for diagnosis and rehabilitation [3], and tracking human activities using no n-wearable radio-frequency-based (RF-based) elder-care [4]. Such waves contain the micro-Doppler effects corresponding to the moving bodies.…”