PECL: A Heterogeneous Parallel Multi-Domain Network for Radar-Based Human Activity Recognition
This work addresses the challenge of radar-based human activity recognition for medical monitoring applications, representing an incremental improvement with specific gains.
The paper tackled the problem of classifying similar human activities from radar signals by designing the PECL network to process data in three complementary domains, achieving an accuracy of 96.16% and outperforming existing methods by at least 4.78%.
Radar systems are increasingly favored for medical applications because they provide non-intrusive monitoring with high privacy and robustness to lighting conditions. However, existing research typically relies on single-domain radar signals and overlooks the temporal dependencies inherent in human activity, which complicates the classification of similar actions. To address this issue, we designed the Parallel-EfficientNet-CBAM-LSTM (PECL) network to process data in three complementary domains: Range-Time, Doppler-Time, and Range-Doppler. PECL combines a channel-spatial attention module and temporal units to capture more features and dynamic dependencies during action sequences, improving both accuracy and robustness. The experimental results show that PECL achieves an accuracy of 96.16% on the same dataset, outperforming existing methods by at least 4.78%. PECL also performs best in distinguishing between easily confused actions. Despite its strong performance, PECL maintains moderate model complexity, with 23.42M parameters and 1324.82M FLOPs. Its parameter-efficient design further reduces computational cost.