A surface electromyography–based deep learning model for guiding semi-autonomous drones in road infrastructure inspection

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.