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The experimental results demonstrate that the proposed hybrid fusion model significantly outperforms the individual CNN and LSTM models during training and validation. While the standalone CNN and LSTM achieve competitive accuracy and F1-scores, the hybrid architecture attains the highest performance with 96.9% accuracy and a 95.7% F1-score. Importantly, this performance gain is achieved without sacrificing efficiency, as the hybrid model maintains a low inference latency of approximately 20 ms, comparable to the unimodal models. These results confirm that multimodal fusion effectively enhances detection accuracy while remaining suitable for near real-time deployment on Android devices. |
IEEE/ICACT20260079 Slide.16
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