Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning

Stadelmayer T, Stadelmayer M, Santra A, Weigel R, Lurz F (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Edited Volumes: Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems

Pages Range: 1-6

Conference Proceedings Title: Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems

Event location: London GB

DOI: 10.1145/3412060.3418430

Abstract

The paper proposes a novel Euclidean distance softmax layer for radar-based human activity classification. The method aims to overcome the angular dependency of classical softmax approaches. Through the freedoms thus gained, the activity classes can be distributed freely within the entire embedded feature space, due to which the dimension of the embeddings and the whole neural network size can be reduced. The performance of our novel deep learning architecture is evaluated for 60 GHz mm-wave radar sensor-based human activity classification. The results show that the proposed approach increases the robustness against random and unknown movements compared to state-of-art representation learning techniques.

Authors with CRIS profile

Thomas Stadelmayer Lehrstuhl für Technische Elektronik (LTE) Markus Stadelmayer Lehrstuhl für Technische Elektronik (LTE) Robert Weigel Lehrstuhl für Technische Elektronik (LTE) Fabian Lurz Lehrstuhl für Technische Elektronik (LTE)

Involved external institutions

Infineon Technologies AG DE Germany (DE)

How to cite

APA:

Stadelmayer, T., Stadelmayer, M., Santra, A., Weigel, R., & Lurz, F. (2020). Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning. In Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems (pp. 1-6). London, GB.

MLA:

Stadelmayer, Thomas, et al. "Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning." Proceedings of the ACM Workshop on Millimeter-Wave Networks and Sensing Systems (mmNets), London 2020. 1-6.

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