Deep Learning Based Gesture Recognition and Its Application in Interactive Control of Intelligent Wheelchair

Zhou, Xingqun; Wang, Fei; Wang, Jianhui; Wang, Yufan; Zhou, Guilin

doi:10.1007/978-3-030-27526-6_48

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The appearance of signals, recognition, feature extraction, classification, and neural networks focusing on information processing and shared control focusing on control strategies proves that not only machine performance (efficiency and accuracy) but also human factors (comfort and independence) are increasingly considered in EPW HMI research [72][73][74][75][76][77][78][79]. The relatively large nodes of the time zone map in recent years, such as speed, tetraplegia, brain, switch, eye movement, and human-robot interaction, demonstrate that speed control (high speed, low speed) and state (moving forward, stopped) or control mode switching [80][81][82][83], the application of computer technology (neu-ral network, deep learning) [84], the development of novel EOG-based HMI [85], and the accessibility of people with tetraplegia [86], have become hot topics in recent years. [88].…”

Section: Actuated Wheelchair Computer Interfacementioning

confidence: 99%

A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

Zhang

Hui

Wei

et al. 2021

IJERPH

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Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

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