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.
Original language | English |
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Title of host publication | Proceedings of the 2023 IEEE Radar Conference (RadarConf23) |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-3669-4 |
ISBN (Print) | 978-1-6654-3670-0 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE Radar Conference (RadarConf23) - San Antonio, United States Duration: 1 May 2023 → 5 May 2023 |
Conference
Conference | 2023 IEEE Radar Conference (RadarConf23) |
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Country/Territory | United States |
City | San Antonio |
Period | 1/05/23 → 5/05/23 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Human Activity Recognition
- Imaging Radar
- Deep Learning
- Point Transformer
- Activities of Daily Living