Study on feature processing schemes for deep‐learning‐based human activity classification using frequency‐modulated continuous‐wave radar

Abstract

Radar is an attractive sensor for classifying human activity because of its invariance to the environment and its ability to operate under low lighting conditions and through obstacles. Classification for human activity finds applications in human-computer interfaces, user-intent understanding and contextual-aware smart homes. Moreover, frequency-modulated continuous-wave technology enables radar systems to retrieve both micro-Doppler and range profiles from humans, which can then be used to recognize target movements. Several feature processing schemes are studied on signatures obtained from a compact, short-range, 60-GHz, frequency-modulated continuous-wave radar for various human activities. The contribution of distinct features to the performance of classification is analysed and novel convolutional-and recurrent-neural-network architectures are consequently presented that efficiently combine range and Doppler information for classifying human activity. In particular, applying multibranch deep learning techniques to high-resolution range profiles and Doppler spectrograms achieves performance similar to that using range-Doppler map videos although it requires less computational and memory resources.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation

“…Figure 9 shows that RACPIT improves the accuracy for all configurations by 10% to 20%. Despite this improvement, the accuracy does not reach the 90% figures of previous works [9,12,13]. A reason for this can indeed be found in the denoising behavior of the ITNs, which not only filter out background noise but also some micro-Doppler features, as it can be seen in Figure 8.…”

“…Among ML algorithms, deep learning has gained popularity over the years as a technique to classify human activities using radar features [5,6,9,[12][13][14]. High-quality public radar data is still hardly available, however, despite the great efforts that exist in this regard [15].…”