Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network
Drew G. Bresnahan, Yang Li
Abstract
Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique time-frequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.
Topics & Concepts
Computer scienceConvolutional neural networkArtificial intelligenceSpectrogramRadarComputer visionHead (geology)Artificial neural networkPattern recognition (psychology)Deep learningExtremely high frequencyRadar imagingContinuous-wave radarRemote sensingGeologyTelecommunicationsGeomorphologyNon-Invasive Vital Sign MonitoringGaze Tracking and Assistive TechnologySleep and Work-Related Fatigue