# Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning

arXiv: https://arxiv.org/abs/2410.07542 · HTML: https://mlsanity.com/paper/2410.07542/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition](https://mlsanity.com/paper/2301.02488/) (2023)
- [Through-the-Wall Radar Human Activity Micro-Doppler Signature Representation Method Based on Joint Boulic-Sinusoidal Pendulum Model](https://mlsanity.com/paper/2408.12077/) (2024)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)

## Built on by (most cited first, 4 of 4)

- [RadHARSimulator V2: Video to Doppler Generator](https://mlsanity.com/paper/2511.09022/) (2025)
- [Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks](https://mlsanity.com/paper/2506.05169/) (2025)
- [Represent Micro-Doppler Signature in Orders](https://mlsanity.com/paper/2602.12985/) (2026)
- [DiFF: Doppler-informed Flow Matching for Human Motion Flow](https://mlsanity.com/paper/2609.39098/) (2026)

## Abstract (arXiv)

Through-the-wall radar (TWR) human activity recognition can be achieved by fusing micro-Doppler signature extraction and intelligent decision-making algorithms. However, limited by the insufficient priori of tester in practical indoor scenarios, the trained models on one tester are commonly difficult to inference well on other testers, which causes poor generalization ability. To solve this problem, this paper proposes a generalizable indoor human activity recognition method based on micro-Doppler corner point cloud and dynamic graph learning. In the proposed method, DoG-μD-CornerDet is used for micro-Doppler corner extraction on two types of radar profiles. Then, a micro-Doppler corner filtering method based on polynomial fitting smoothing is proposed to maximize the feature distance under the constraints of the kinematic model. The extracted corners from the two types of radar profiles are concatenated together into three-dimensional point cloud. Finally, the paper proposes a dynamic graph neural network (DGNN)-based recognition method for data-to-activity label mapping. Visualization, comparison and ablation experiments are carried out to verify the effectiveness of the proposed method. The results prove that the proposed method has strong generalization ability on radar data collected from different testers.