Yu Li, David Zhang, Penghao Dong, Shanshan Yao, Ruwen Qin
{"title":"A surface electromyography–based deep learning model for guiding semi‐autonomous drones in road infrastructure inspection","authors":"Yu Li, David Zhang, Penghao Dong, Shanshan Yao, Ruwen Qin","doi":"10.1111/mice.13520","DOIUrl":null,"url":null,"abstract":"While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set, sEMG Commands for Piloting Drones (sCPD), and an sEMG‐based Cross‐subject Classification Network (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13520","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set, sEMG Commands for Piloting Drones (sCPD), and an sEMG‐based Cross‐subject Classification Network (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.