Non Contact

Non-contact sensing research focuses on developing methods to acquire data without physical contact, addressing limitations of traditional methods and enabling new applications. Current efforts utilize diverse techniques, including computer vision (often employing deep learning architectures like U-Net, convolutional attention networks, and LSTMs), infrared and radar imaging, and signal processing to extract relevant information from non-contact measurements such as thermal images, video, and radio frequency signals. This field is significantly impacting various domains, from healthcare (e.g., remote heart rate and respiration monitoring, pressure ulcer detection) to industrial applications (e.g., liquid level detection, wind turbine blade inspection) and sports analysis (e.g., race walking fault detection). The development of robust and accurate non-contact sensing methods is driving innovation across numerous scientific and engineering disciplines.