Incheon National University Scientists Enhance Smart Home Security with AIoT and WiFi

The Artificial Intelligence of Things framework improves the accuracy of human activity recognition

Artificial Intelligence of Things (AIoT) is becoming immensely popular because of its widespread applications. In a groundbreaking study, researchers from Incheon National University present a new AIoT framework called MSF-Net for accurately recognizing human activities using WiFi signals. The framework utilizes a novel approach that combines different signal processing techniques and a deep learning architecture to overcome 
challenges like environmental interference and achieve high recognition accuracy.

Image title: WiFi Signal Intelligence: AIoT Framework for Enhanced Smart Home Living

Image caption: The proposed framework is promising for coarse and fine human activity recognition in smart homes.

Image credits: deepakiqlect from Openverse

Image source link: https://openverse.org/image/be6a1d1f-2d14-4d0e-ae9b-9bf1875fc9a2

 License type: CC BY-SA 2.0

Usage restrictions: Credit must be given to the creator. Adaptations must be shared under the same terms.

Artificial Intelligence of Things (AIoT), which combines the advantages of both Artificial Intelligence and Internet of Things technologies, has become widely popular in recent years. In contrast to typical IoT setups, wherein devices collect and transfer data for processing at some other location, AIoT devices acquire data locally and in real-time, enabling them to make smart decisions. This technology has found extensive applications in intelligent manufacturing, smart home security, and healthcare monitoring.

In smart home AIoT technology, accurate human activity recognition is crucial. It helps smart devices identify various tasks, such as cooking and exercising. Based on this information, the AIoT system can tweak lighting or switch music automatically, thus improving user experience while also ensuring energy efficiency. In this context, WiFi-based motion recognition is quite promising: WiFi devices are ubiquitous, ensure privacy, and tend to be cost-effective.

Recently, in a novel research article, a team of researchers, led by Professor Gwanggil Jeon  from the College of Information Technology at Incheon National University, South Korea, has come up with a new AIoT framework called multiple spectrogram fusion network (MSF-Net) for WiFi-based human activity recognition. Their findings were made available online on 13 May 2024 and published in Volume 11, Issue 24 of the IEEE Internet of Things Journal on 15 December 2024.

Prof. Jeon explains the motivation behind their research. “As a typical AIoT application, WiFi-based human activity recognition is becoming increasingly popular in smart homes. However, WiFi-based recognition often has unstable performance due to environmental interference. Our goal was to overcome this problem.”

In this view, the researchers developed the robust deep learning framework MSF-Net, which achieves coarse as well as fine activity recognition via channel state information (CSI). MSF-Net has three main components: a dual-stream structure comprising short-time Fourier transform along with discrete wavelet transform, a transformer, and an attention-based fusion branch. While the dual-stream structure pinpoints abnormal information in CSI, the transformer extracts high-level features from the data efficiently. Lastly, the fusion branch boosts cross-model fusion.

The researchers performed experiments to validate the performance of their framework, finding that it achieves remarkable Cohen’s Kappa scores of 91.82%, 69.76%, 85.91%, and 75.66% on SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively. These values highlight the superior performance of MSF-Net compared to state-of-the-art techniques for WiFi data-based coarse and fine activity recognition.

“The multimodal frequency fusion technique has significantly improved accuracy and efficiency compared to existing technologies, increasing the possibility of practical applications. This research can be used in various fields such as smart homes, rehabilitation medicine, and care for the elderly. For instance, it can prevent falls by analyzing the user's movements and contribute to improving the quality of life by establishing a non-face-to-face health monitoring system,” concludes Prof. Jeon.

Overall, activity recognition using WiFi, the convergence technology of IoT and AI proposed in this work, is expected to greatly improve people's lives through everyday convenience and safety!

Reference

About Incheon National University

Incheon National University (INU) is a comprehensive, student-focused university. It was founded in 1979 and given university status in 1988. One of the largest universities in South Korea, it houses nearly 14,000 students and 500 faculty members. In 2010, INU merged with Incheon City College to expand capacity and open more curricula. With its commitment to academic excellence and an unrelenting devotion to innovative research, INU offers its students real-world internship experiences. INU not only focuses on studying and learning but also strives to provide a supportive environment for students to follow their passion, grow, and, as their slogan says, be INspired.

Website: https://www.inu.ac.kr/sites/inuengl/index.do?epTicket=LOG

About the author Prof. Gwanggil Jeon

Dr. Gwanggil Jeon received his B.S., M.S., and Ph.D. degrees from the Department of Electronics and Computer Engineering at Hanyang University. He is currently a Full Professor at Incheon National University. He is an Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology, Elsevier Sustainable Cities and Society, IEEE Access, Springer Real-Time Image Processing, Journal of System Architecture, and Wiley Expert Systems. He has received the IEEE Chester Sall Award, ACM’s Distinguished Speaker Award, the ETRI Journal Paper Award, and the Industry-Academic Merit Award from the Ministry of SMEs and Startups of Korea Minister.