Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces

Moon, Inhyuk; Lee, Myung-Joon; Ryu, Jeicheong; Mun, Mingyon

doi:10.1109/iros.2003.1249690

Cited by 32 publications

(8 citation statements)

References 10 publications

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“…Such systems aim to provide a reliable pathway between people with severe motor dysfunction and the world around them. Common means of HCI that have been widely employed are human hands, voice, head and gaze [1,2]. Biosignal-based HCIs have been recently proposed which are executed by voluntary signals generated from either a brain activity known as brain computer interaction (BCI) or a body muscle recognition system of the three facial expressions happiness, anger and sadness through three pairs of electrodes.…”

Section: Introductionmentioning

confidence: 99%

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Hamedi

Salleh

Noor

2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Hamedi

Salleh

Noor

2015

Applied Soft Computing

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“…In addition, researchers have used technologies originally developed for mobile robots to create smart wheelchairs that reduce the physical, perceptual, and cognitive skills necessary to operate a power wheelchair for individuals with severe dysfunction disorders such as amyotrophic lateral sclerosis (ALS), spinal cord injury (SCI), and muscle dystrophy (MS) [13,49]. Different kinds of input methods, such as joysticks [2,50], voice commands [51,52], the sip-and-puff interface [6], BCI [9,10,17,43], the tongue drive system (TDS) [7,8,[32][33][34][35], the head gesture based interface (HGI) [53,54], the eye-controlled interface [3,39,[55][56][57][58][59][60][61], the EMG-based interface [62,63], and the multimodal interface [64,65], have been used in EPW HMI to accommodate the disabled. Some examples of the remarkable technological advances in EPW HMI methodology in recent years are shown in detail in Figure 8.…”

Section: Mapping and Analysis By Keywordmentioning

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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“…Cameras are commercially available at pretty low price, and they can be easily mounted at multiple locations on a wheelchair. Some smart wheelchairs already use computer vision as a means of head-and eye-tracking for wheelchair navigation control [1], [6]. These human gestures or activities could be used as the input of smart wheelchairs.…”

Section: A Smart Wheelchairsmentioning

confidence: 99%

Smarter wheelchairs who can talk to each other: An integrated and collaborative approach

Kong

Cao

Liu

et al. 2012

2012 IEEE 14th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Abstract-Pervasive computing technologies can benefit the injured, disabled or elderly people in their daily lives, and smart wheelchair has been a representative of this kind of technologies. However, most existing smart wheelchairs have limitations on extensibility and flexibility of building new functionalities. One big reason is they are just stand-alone ones considering other wheelchairs and surrounding things as dummy objects. To address this issue, we proposed an integrated and collaborative approach: Smarter Wheelchairs that can "talk" to each other, and even "talk" to other things in the surrounding. Smarter Wheelchairs take advantages of smart objects deployed in living environment or hospital environment. Smarter Wheelchairs can harness functions provided by smart objects, therefore, their functionality can be flexibly extended. We have implemented and evaluated a prototype system of Smarter Wheelchairs to demonstrate the feasibility and efficiency of our approach.

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Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces

Cited by 32 publications

References 10 publications

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

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

Smarter wheelchairs who can talk to each other: An integrated and collaborative approach

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