R-ATCN: continuous human activity recognition using FMCW radar with temporal convolutional networks

Abstract The utilization of millimeter-wave radar sensors for continuous human activity recognition technology has garnered significant interest. Prior research predominantly concentrated on recursive neural networks, which often incorporate numerous extraneous information features, hindering the ability to make precise and effective predictions for ongoing activities. In response to this challenge, this paper introduces a dual-dilated one-dimensional temporal convolutional network model with an attention mechanism (R-ATCN). By stacking temporal convolutions to enhance the receptive field without compromising temporal resolution, the R-ATCN effectively captures features. Additionally, the attention mechanism is employed to capture crucial frame information related to activity transitions and overall features. The study gathered 60 data sets from 5 participants utilizing frequency modulated continuous wave radar. It encompassed 8 various activities lasting a total of 52.5 min, with randomized durations and transition times for each activity. To evaluate the performance of the model, this paper also introduces evaluation metrics such as short-time tolerance (STT) score. Experimental results show that the R-ATCN model outperforms other contrastive models in terms of segmental F 1-score and STT scores. The effectiveness of the proposed model lies in its ability to accurately identify ongoing human activities within indoor environments.