Liheng Dong , Xin Xu , Guiqing He , Yuelei Xu , Jarhinbek Rasol , Chengyang Tao , Zhaoxiang Zhang
{"title":"An efficient gesture recognition for HCI based on semantics-guided GCNs and adaptive regularization graphs","authors":"Liheng Dong , Xin Xu , Guiqing He , Yuelei Xu , Jarhinbek Rasol , Chengyang Tao , Zhaoxiang Zhang","doi":"10.1016/j.aej.2025.04.019","DOIUrl":null,"url":null,"abstract":"<div><div>In the embedded system, real-time gesture recognition is crucial to human–computer interaction (HCI). Recently, Graph Convolutional Networks (GCNs) have been applied to inertial measurement unit-based (IMU-based) gesture recognition. However, the disadvantage of these GCN-based methods is that they use very deep networks to capture deep motion features, without considering computational efficiency. In this paper, we propose a shallow GCN as the basic framework to ensure the real-time performance of gesture recognition. To solve the problem of shallow networks’ difficulty capturing deep motion features, we provide hand-crafted semantic information about the positions of nodes (sensors) and frames to guide deep feature extraction. Furthermore, we propose a regularization module named Double-Mask (2MASK) to enhance the network’s generalization. Experiments show that the average inference time on raspberry pi 4b is less than 4 ms. Extensive testing on the self-constructed dataset indicates that the proposed method outperforms previous state-of-the-art (SOTA) methods on multiple metrics. The accuracy reaches 89.47% and 98.70% on two public datasets, outperforming other methods. Experiments in an HCI application show that our method meets the high-precision and low-latency requirements for autonomous taxiing of UAVs. The code for this paper has been uploaded to <span><span>https://github.com/oldbowls/2MAGCN-FN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 30-44"},"PeriodicalIF":6.2000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016825005058","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In the embedded system, real-time gesture recognition is crucial to human–computer interaction (HCI). Recently, Graph Convolutional Networks (GCNs) have been applied to inertial measurement unit-based (IMU-based) gesture recognition. However, the disadvantage of these GCN-based methods is that they use very deep networks to capture deep motion features, without considering computational efficiency. In this paper, we propose a shallow GCN as the basic framework to ensure the real-time performance of gesture recognition. To solve the problem of shallow networks’ difficulty capturing deep motion features, we provide hand-crafted semantic information about the positions of nodes (sensors) and frames to guide deep feature extraction. Furthermore, we propose a regularization module named Double-Mask (2MASK) to enhance the network’s generalization. Experiments show that the average inference time on raspberry pi 4b is less than 4 ms. Extensive testing on the self-constructed dataset indicates that the proposed method outperforms previous state-of-the-art (SOTA) methods on multiple metrics. The accuracy reaches 89.47% and 98.70% on two public datasets, outperforming other methods. Experiments in an HCI application show that our method meets the high-precision and low-latency requirements for autonomous taxiing of UAVs. The code for this paper has been uploaded to https://github.com/oldbowls/2MAGCN-FN.
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering