Wi-Fi sensing is a leading technology for Human Activity Recognition (HAR), offering a non-intrusive and cost-effective solution for healthcare and smart environments. Despite its potential, existing methods struggle with domain shift issues, often failing to generalize to unseen environments due to overfitting. This paper proposes IBIS, a robust ensemble framework combining Inception-Bidirectional Long Short-Term Memory (BiLSTM) for feature extraction and Support Vector Machine (SVM) for classification of Doppler signatures. The proposed architecture specifically targets generalization capabilities. Experimental results on multiple datasets show that IBIS achieves 95.40% accuracy, delivering a 7.58% performance gain compared to standard architectures in cross-scenario evaluations on external datasets. The analysis confirms that IBIS effectively mitigates environmental dependency in Wi-Fi-based HAR.

翻译：Wi-Fi感知技术作为人体活动识别（HAR）的主流方案，为医疗健康与智能环境提供了一种非侵入式且经济高效的解决方案。尽管潜力显著，现有方法常受限于领域偏移问题，因过拟合而难以泛化至未见过的环境。本文提出IBIS——一种鲁棒的集成框架，其结合Inception-双向长短期记忆网络（BiLSTM）进行特征提取，并采用支持向量机（SVM）对多普勒特征信号进行分类。该架构特别针对模型的泛化能力进行优化。在多组数据集上的实验结果表明，IBIS在外部数据集的跨场景评估中达到95.40%的准确率，相较于标准架构实现了7.58%的性能提升。分析证实，IBIS能有效缓解基于Wi-Fi的HAR系统对环境因素的依赖。