Gaze, Visual, Myoelectric, and Inertial Data of Grasps for Intelligent Prosthetics
Gaze, Visual, Myoelectric, and Inertial Data of Grasps for Intelligent Prosthetics
Abstract Hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among which the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.
Gijsberts Arjan、Gregori Valentina、Hager Anne-Gabrielle Mittaz、Faccio Diego、Tiengo Cesare、Bassetto Franco、Caputo Barbara、Brugger Peter、M¨1ller Henning、Saetta Gianluca、Gigli Andrea、Giacomino Katia、Atzori Manfredo、Cognolato Matteo
Istituto Italiano di TecnologiaIstituto Italiano di Tecnologia||Department of Computer, Control, and Management Engineering, University of Rome La SapienzaDepartment of Physical Therapy, University of Applied Sciences Western Switzerland (HES-SO Valais)Clinic of Plastic Surgery, Padova University HospitalClinic of Plastic Surgery, Padova University HospitalClinic of Plastic Surgery, Padova University HospitalIstituto Italiano di Tecnologia||Politecnico di TorinoDepartment of Neurology, University Hospital of ZurichInformation Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)Department of Neurology, University Hospital of ZurichIstituto Italiano di TecnologiaDepartment of Physical Therapy, University of Applied Sciences Western Switzerland (HES-SO Valais)Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)||Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich
自动化技术、自动化技术设备生物工程学计算技术、计算机技术
Gijsberts Arjan,Gregori Valentina,Hager Anne-Gabrielle Mittaz,Faccio Diego,Tiengo Cesare,Bassetto Franco,Caputo Barbara,Brugger Peter,M¨1ller Henning,Saetta Gianluca,Gigli Andrea,Giacomino Katia,Atzori Manfredo,Cognolato Matteo.Gaze, Visual, Myoelectric, and Inertial Data of Grasps for Intelligent Prosthetics[EB/OL].(2025-03-28)[2025-04-26].https://www.medrxiv.org/content/10.1101/19010199.点此复制
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