2019/08/16 by Md Tamzeed Islam, Islam, Md Tamzeed, Shahriar Nirjon +1
Computer Science · Engineering · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Wireless Networks and Protocols #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.06803
openalex publication_date 2019/08/16 · openalex created_date 2022/07/22 · openalex updated_date 2026/07/28
The lack of adequate training data is one of the major hurdles in WiFi-based\nactivity recognition systems. In this paper, we propose Wi-Fringe, which is a\nWiFi CSI-based device-free human gesture recognition system that recognizes\nnamed gestures, i.e., activities and gestures that have a semantically\nmeaningful name in English language, as opposed to arbitrary free-form\ngestures. Given a list of activities (only their names in English text), along\nwith zero or more training examples (WiFi CSI values) per activity, Wi-Fringe\nis able to detect all activities at runtime. In other words, a subset of\nactivities that Wi-Fringe detects do not require any training examples at all.\n