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
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The proposed framework is promising for coarse and fine human activity recognition in smart homes.
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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!
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Reference
DOI: https://doi.org/10.1109/JIOT.2024.3400773
Authors: Junxin Chen1, Xu Xu1,2, Tingting Wang3, Gwanggil Jeon4, and David Camacho5
Affiliations:
1School of Software, Dalian University of Technology, China
2School of Computer Science and Engineering, Northeastern University, China
3School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, China
4College of Information Technology, Incheon National University, South Korea
5School of Computer Systems Engineering, Universidad Politecnica de Madrid, Spain
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.
Journal
IEEE Internet of Things Journal
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
An AIoT Framework With Multimodal Frequency Fusion for WiFi-Based Coarse and Fine Activity Recognition
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