Artificial intelligence-enhanced CSI-based Wi-Fi sensing for non-contact vital sign monitoring: a systematic review - Al-Rajab,Murad; Qassem,Kamel; Seyam,Sara; Al Hamadi,Youssef; Al-Dubaisi,Ahmed Hadi; Mallat,Jihad; Lu,Joan; Ellahham,Samer H.; Sawsaa,Ahlam; Alzraiqat,Shadi (PeerJ Computer Science, 2026)
Background In the recent era, non-invasive health monitoring has gained attention as a crucial component of healthcare. Especially with the advancements in artificial intelligence (AI) and Wi-Fi-based sensing technologies offering promising solutions. These technologies can detect vital signs and human activity without physical contact, making them well suited to diverse healthcare environments. However, research on AI-enhanced Wi-Fi signal processing for health monitoring needs to be synthesized to understand its current capabilities, limitations, and potential. Objectives This systematic review evaluates Channel State Information (CSI)-based Wi-Fi health monitoring systems, both with and without AI integration. It focuses on key advancements in vital signs detection, assessing the effectiveness of different AI techniques and identifying opportunities for future research. Methods Following PRISMA and Kitchenham guidelines, we systematically searched six databases (IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, Google Scholar, and ResearchGate-), retrieving 8,573 records. After screening, 45 published peer-reviewed studies from 2019 to 2024 were selected for analysis. The review focused on AI techniques, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVMs). Performance was evaluated using metrics such as precision, recall, F1-score, and sensitivity in scenarios, including multi-user detection and real-time applications. Results Experimental findings revealed that AI models demonstrate high accuracy rates, achieving over 95% for heart rate and respiration monitoring, and demonstrate strong scalability and adaptability across various healthcare environments. However, significant challenges remain, including reduced performance in multi-person settings, privacy concerns, and computational efficiency limitations. Conclusions AI-enhanced Wi-Fi sensing technologies show immense potential for non-invasive health monitoring, with high accuracy in vital sign detection. But challenges related to multi-user environments, privacy, and computational demands must be addressed. Future research should focus on overcoming these obstacles to advance AI-driven health monitoring technologies in clinical and home settings.
Keywords: AI in health monitoring; Wi-Fi signal-based health monitoring; Security in health monitoring systems; Non-contact vital signs monitoring; Machine learning in healthcare; Remote patient monitoring