Classification of human body motions using an ultra-wideband pulse radar

BACKGROUND: The motion or gestures of a person are primarily recognized by detecting a specific object and the change in its position from image information obtained via an image sensor. However, the use of such systems is limited due to privacy concerns. OBJECTIVE: To overcome these concerns, this study proposes a radar-based motion recognition method. METHODS: Detailed human body movement data were generated using ultra-wideband (UWB) radar pulses, which provide precise spatial resolution. The pulses reflected from the body were stacked to reveal the body's movements and these movements were expressed in detail in the micro-range components. The collected radar data with emphasized micro-ranges were converted into an image. Convolutional neural networks (CNN) trained on radar images for various motions were used to classify specific motions. Instead of training the CNNs from scratch, transfer learning is performed by importing pretrained CNNs and fine-tuning their parameters with the radar images. Three pretrained CNNs, Resnet18, Resnet101, and Inception-Resnet-V2, were retrained under various training conditions and their performance was experimentally verified. RESULTS: As a result of various experiments, we conclude that detailed motions of subjects can be accurately classified by utilizing CNNs that were retrained with images obtained from the UWB pulse radar.