Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.
FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings
WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework
University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）；Guizhou Normal University（贵州师范大学）；The University of Electro-Communications（电气通信大学）
Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition
Tianjin University（天津大学）；Guizhou Normal University（贵州师范大学）；Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；The University of Electro-Communications（电子通信大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Inspur Computer Technology Co., Ltd（浪潮信息科技有限公司）
Devising Interactive Spaces: A Rehearsal-Oriented Tool for Creating Responsive Environments for Immersive Theatre
Mixed Reality Lab, School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院混合现实实验室）；Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham（诺丁汉大学工程学院建筑与建成环境系）
Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting
City University of Hong Kong（香港城市大学）；Shandong University（山东大学）；Delft University of Technology（代尔夫特理工大学）；University at Buffalo, SUNY（纽约州立大学布法罗分校）；Macquarie University（麦考瑞大学）
Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Technical University of Munich（慕尼黑技术大学）；Infineon Technologies AG（英飞凌科技有限公司）；Johannes Kepler University Linz（约翰·开普勒大学林茨分校）；Software Competence Center Hagenberg GmbH (SCCH)（哈根贝格软件竞争力中心（SCCH））
