@inproceedings{664d8a17ec944da9bf5e68df568e772f,
title = "Hand Gesture Recognition Based on Flex Sensors with Enhanced Focal Loss",
abstract = "A novel hand gesture detection algorithm for a smart Human Computer Interface (HCI) is developed in this study. Flex sensors are deployed in a smart glove for the collection sensory data for finger movements. Based on the sensory data, a modified centernet algorithm is presented for gesture detection and classification. To address class imbalance and enhance sequence-level precision, a Focal Loss function is incorporated into the model training. The algorithm has the advantages of accurate gesture detection and classification with low computational complexities. The algorithm can be deployed in an embedded system for effective online inference. Experimental results reveal that the proposed algorithm is an effective alternative for applications based on smart gloves requiring fast and accurate gesture detection recognition with low deployment overhead.",
keywords = "Artificial Intelligence, Convolutional Neural Networks, Focal Loss, Gesture Detection, Gesture Recognition, Internet of Things",
author = "Hwang, \{Wen Jyi\} and Hsu, \{Tai Wei\} and Chang, \{Shao Tong\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2025 ; Conference date: 03-12-2025 Through 05-12-2025",
year = "2025",
doi = "10.1109/AIoT66900.2025.00013",
language = "English",
series = "Proceedings - 2025 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--8",
booktitle = "Proceedings - 2025 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2025",
}