Deep Learning-Based Human Activity Classification with OTFS Radar and Attention Enhanced LSTM Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199728" target="_blank" >RIV/00216305:26220/26:0199728 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11351216" target="_blank" >https://ieeexplore.ieee.org/document/11351216</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WPMC67460.2025.11351216" target="_blank" >10.1109/WPMC67460.2025.11351216</a>

Result language
angličtina
Original language name
Deep Learning-Based Human Activity Classification with OTFS Radar and Attention Enhanced LSTM Networks
Original language description
Radar-based human activity recognition (HAR) is emerging as a resilient alternative to camera systems in industrial environments, where occlusions, reflective surfaces, and privacy concerns limit vision-based methods. This paper presents a deep learning framework that integrates convolutional feature extraction, denoising, multi-head attention, and bidirectional LSTM layers to classify activities from OTFS radar delay–Doppler signatures. Experiments in cluttered industrial environments demonstrate that denoising improves robustness against clutter, while attention stabilizes performance across longer temporal sequences. With a sequence length of 50 frames and 32 attention heads, the proposed model achieves 95.2% accuracy on unseen test data. Visualization using t-SNE confirms clear activity separation, with minor overlap between walking and multi-person walking due to shared Doppler patterns. These results highlight the effectiveness of combining OTFS radar with attention-based temporal modeling for reliable and efficient HAR in real-world industrial monitoring.
Czech name
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Czech description
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Project
<a href="/en/project/LUC24141" target="_blank" >LUC24141: Joint Communication and Sensing to Enhance Robustness of 6G Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2025 28th International Symposium on Wireless Personal Multimedia Communications (WPMC)
ISBN
979-8-3315-9128-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Sofia, Bulgaria
Event location
Sofia, Bulgaria
Event date
Nov 9, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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