Fall Detection Related Human Activity Recognition by Millimeter-Wave Radar

Fall detection holds significant value in healthcare of elderly. However, the current fall detection algorithms based on millimeter-wave radar only detect whether an individual is fallen or not. This paper proposes a novel approach, utilizing the duration that an individual lies on the ground, to dynamically assess their physical posture. The fall detection problem is redefined as a human activity recognition task for further exploration. Nevertheless, current public scene-related datasets lack relevant activity data, such as falling and getting up. Therefore, in this paper we describe the approach of building a millimeter-wave radar point cloud dataset, encompassing nine activities for fall detection scene. Additionally, two methods for aligning data frame length are proposed during the data preprocessing stage. A human activity recognition model, Spatial Attention Temporal Attention Human Activity Recognition (SATA-HAR), is designed based on graph attention and transformer encoder. This model captures features in both spatial and temporal dimensions of activity data, and then utilizes a classifier for classification. The experimental results demonstrate that the methods and model proposed in this paper are highly effective. SATA-HAR achieves a recognition accuracy of 90.28%, surpassing other baseline models. It also shows strong training stability.