优化多模态生理信号中的应力检测模型，更高效且准确。
Efficient-Husformer: Efficient Multimodal Transformer Hyperparameter Optimization for Stress and Cognitive Loads
- 设计结构化超参搜索空间，精准优化Transformer模型。
- 在两个数据集上准确率提升超6%，最高达92.61%。
- 模型仅3万参数，适合资源受限的实时应用。
基于Transformer的模型在生理信号分析中表现出色，能捕捉时间序列中的长程依赖与复杂模式，性能优于传统RNN和CNN模型。然而，其计算量大、内存需求高。本文提出Efficient-Husformer，一种针对多分类压力检测任务的新型Transformer架构，结合超参数优化（HPO）方法，在WESAD和CogLoad两个多模态生理数据集上进行验证。主要贡献包括：(1) 设计结构化的超参数搜索空间以实现高效优化；(2) 通过全面消融实验评估架构决策的影响；(3) 在两个数据集上均取得显著性能提升，最佳配置下准确率分别达到88.41和92.61，相较原Husformer分别提高13.83%和6.98%。最优模型采用(L + dm)或(L + FFN)模态组合，仅需单层、3个注意力头、模型维度18/30、前馈网络维度120/30，参数量约3万，结构紧凑。
原文摘要 · Abstract (English)
Transformer-based models have gained considerable attention in the field of physiological signal analysis. They leverage long-range dependencies and complex patterns in temporal signals, allowing them to achieve performance superior to traditional RNN and CNN models. However, they require high computational intensity and memory demands. In this work, we present Efficient-Husformer, a novel Transformer-based architecture developed with hyperparameter optimization (HPO) for multi-class stress detection across two multimodal physiological datasets (WESAD and CogLoad). The main contributions of this work are: (1) the design of a structured search space, targeting effective hyperparameter optimization; (2) a comprehensive ablation study evaluating the impact of architectural decisions; (3) consistent performance improvements over the original Husformer, with the best configuration achieving an accuracy of 88.41 and 92.61 (improvements of 13.83% and 6.98%) on WESAD and CogLoad datasets, respectively. The best-performing configuration is achieved with the (L + dm) or (L + FFN) modality combinations, using a single layer, 3 attention heads, a model dimension of 18/30, and FFN dimension of 120/30, resulting in a compact model with only about 30k parameters.
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