Continual Learning of Micro-Doppler Signature-Based Human Activity Classification

Abstract

Human activity classification based on micro-Doppler signatures measured by radar recently has been successfully applied in diverse applications due to the improvement of machine learning methods, e.g., deep convolutional neural networks (DCNNs). Despite the success, those methods encounter a common practical problem when all the radar data are not readily available before training a model but sequentially arrive as the learning continues, e.g., during surveillance or search-and-rescue operations. That is, when DCNN is naively utilized in such settings, it is well-known that catastrophic forgetting of past learned tasks occurs; hence, more robust continual learning methods should be developed. To that end, we apply several state-of-the-art methods for two practical continual learning scenarios in activity classification, i.e., when the data for a subject and an activity class arrive incrementally, respectively, and compare the competitiveness of those methods. To the best of our knowledge, this is the first comparative study of continual learning methods for the classification based on micro-Doppler signatures—we find that exemplar memory-based methods particularly become very effective for both scenarios, not only for the performance but also for the memory usage.