Point Transformer-Based Human Activity Recognition Using High-Dimensional Radar Point Clouds

Abstract

Radar-based Human Activity Recognition(HAR) is considered by using snapshots of point clouds. Such point cloudsinterpret 2D images generated by an mm-wave FMCW MIMO radar enriched byincluding Doppler and temporal information. We use the similarity between suchradar data representation and the core of the self-attention concept inartificial intelligence. Three self-attention models (Point Transformer) areinvestigated to classify Activities of Daily Living (ADL). An experimentaldataset collected at TU Delft is used to explore the best combination ofdifferent input features, the effect of a proposed Adaptive ClutterCancellation (ACC) method, and the robustness in a leave-one-subject-outscenario. Results with a macro F1 score in the order of 90% are demonstratedwith the proposed method, including activities that are static postures withlittle associated Doppler.