A study on light weight of radar-based human activity recognition model using self-attention mechanism

다국어 초록 (Multilingual Abstract)

Recently, radar-based human activity recognition (HAR) technology has been actively studied in the field of artificial intel-ligence by applying radar datasets to deep learning (DL) models to automatically learn and classification. This is one of the important applications in the field of activity recognition and can be used in various fields, such as exercise tracking, smart homes, self-driving cars, and health status monitoring, by recognizing human daily activity patterns. However, DL models are complex and have significant computational costs and numerous parameters to process and classify high-dimensional radar datasets. Therefore, their implementation on commercial mobile devices is limited by their computational complexity. Therefore, in this study, we propose a lightweight HAR DL model using Self-Attention technology to solve the complexity and computational costs of these DL models. Experimental results demonstrate that this model can maintain its performance while reducing the number of parameters required. In the future, these light-weight models will not only be usable on mobile devices but will also require lower computing power and memory capacity; therefore, they are expected to be used in various fields as time series-based DL models.