WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition

Researchers have developed WISE-HAR, an ensemble deep learning framework that uses WiFi signals to recognize human activities including walking and arm-waving with 94.87% accuracy. The framework, tested on the Wallhack1.8k dataset, combines five CNN architectures and aggressive data augmentation to overcome performance variance and small dataset limitations. The system demonstrated strong real-world generalization with minimal accuracy drops of 1-2% when tested across different scenarios and antenna types.

arXiv:2606.02974v1 Announce Type: new Abstract: 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.