Credit: AI-generated image
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 health care 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.
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 are published in the IEEE Internet of Things Journal.
