ULER: An Ultra-Lightweight Joint Attention Model for Emotion Recognition from Multimodal Physiological Signals

Emotion recognition using multimodal physiological signals has gained widespread attention for providing objective insight into emotional states. However, existing models are often computationally complex and parameter heavy, hindering deployment on resource constrained wearable devices. To overcome this, we propose ULER (Ultra Lightweight Emotion Recognition), an efficient framework that integrates lightweight multi-scale convolution (LMSC) with a novel joint attention mechanism (LJA) based on a dynamic feature fusion network (DFFN) for effective multimodal fusion. Using subject dependent and total dataset training strategies, ULER is evaluated on three benchmarks (DEAP, DREAMER, WESAD) and outperforms recent state-of-the-art (SOTA) methods. It achieves accuracies of 99.34%, 99.46%, and 99.23% on DEAP binary valence, binary arousal, and four class tasks, respectively, with only 0.60 M parameters, 9.33 M FLOPs, and 31.10 ms inference latency. In a wearable oriented reduced channel setup (11 EEG channels), ULER also surpasses most SOTA models using standard 32 channels. Principal contributions emphasize: (1) a systematic comparison against recent SOTA methods on multiple datasets; (2) a wearable design with reduced-channel configuration; and (3) the DFFN as a core component for efficient multimodal fusion. This work demonstrates the potential for practical, real-time emotion monitoring in personal healthcare and other scenarios.