Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living. Unlike traditional camera-based systems that raise significant privacy concerns and fail in low-light conditions, or wearable sensors that require user compliance, WiFi-based HAR is non-intrusive, privacy-preserving, cost-effective, and works seamlessly in any lighting condition. This paper presents a comprehensive approach to recognize three distinct human activities: "No Presence" (empty room), "Walking", and "Walking + Arm-waving" using the Wallhack1.8k WiFi spectrogram dataset. We propose three key improvements to address the main challenges in WiFi-based HAR. First, to address high performance variance, we implement ensemble learning with five different CNN architectures (Deep CNN, Wide CNN, MobileNetV2, ResNet50V2, and EfficientNetB0). Second, to address the small dataset size limitation, we apply aggressive data augmentation techniques including time-warping, frequency masking, and noise addition. Third, to evaluate real-world generalization capability, we perform cross-scenario evaluation (training on Line-of-Sight and testing on Non-Line-of-Sight) and cross-antenna evaluation (training on Biquad antenna and testing on PIFA antenna). Our ensemble model achieved a test accuracy of 94.87% on the LOS scenario with Biquad antenna, outperforming the best individual model by 0.66%. Data augmentation improved Random Forest performance from 60% to 95%. Cross-scenario evaluation showed minimal accuracy drops of only 1.37% and 2.07%, demonstrating strong generalization capabilities. The results indicate that the proposed approach is robust, reliable, and suitable for real-world deployment in diverse environments with different hardware configurations.

翻译：基于WiFi信号的人体活动识别（HAR）已成为智慧家居、医疗监护、安防系统及环境辅助生活领域的一项变革性技术。与存在隐私隐患且弱光环境下失效的传统摄像头系统，或需要用户配合的可穿戴传感器不同，基于WiFi的HAR具有非侵入性、隐私保护性强、成本低廉且可在任何光照条件下无缝运行等优势。本文利用Wallhack1.8k WiFi频谱数据集，提出一种综合方法以识别三种不同人体活动："无人存在"（空房间）、"行走"和"行走+挥臂"。针对基于WiFi的HAR面临的主要挑战，我们提出三项关键改进：首先，为应对高性能波动问题，采用集成学习策略，融合五种不同CNN架构（深度CNN、宽CNN、MobileNetV2、ResNet50V2与EfficientNetB0）；其次，针对小规模数据集局限，引入激进的数据增强技术，包括时间扭曲、频率掩蔽和噪声注入；最后，为评估真实场景泛化能力，执行跨场景评估（视距场景训练/非视距场景测试）与跨天线评估（双锥天线训练/PIFA天线测试）。实验表明，在双锥天线的视距场景下，集成模型测试准确率达94.87%，较最优单模型提升0.66%。数据增强使随机森林性能从60%提升至95%。跨场景评估显示准确率仅下降1.37%和2.07%，展现出强大的泛化能力。结果表明，所提方法具备鲁棒性与可靠性，适用于不同硬件配置的多样化真实环境部署。