An EOG-Based Human–Machine Interface for Wheelchair Control

Huang, Qiyun; He, Shenghong; Wang, Qihong; Gu, Zhenghui; Peng, Nengneng; Li, Kai; Zhang, Yuandong; Shao, Ming; Li, Yuanqing

doi:10.1109/tbme.2017.2732479

Cited by 85 publications

(64 citation statements)

References 25 publications

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“…Delicate motions of the upper limb controlled joysticks-based smart wheelchairs [ 83 ], but they are not capable for patients with complete or partial loss of muscle activities. The EEG-based [ 83 ] and EOG-based [ 7 ] wheelchairs with automated navigation systems were proposed. In Huang’s work [ 7 ], subjects could control the wheelchair to finish all tasks within 227 s and 277 s by joysticks and EOG signals, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The EEG-based [ 83 ] and EOG-based [ 7 ] wheelchairs with automated navigation systems were proposed. In Huang’s work [ 7 ], subjects could control the wheelchair to finish all tasks within 227 s and 277 s by joysticks and EOG signals, respectively. The recognition rate for healthy subjects was 91.7%.…”

Section: Discussionmentioning

confidence: 99%

“…HCI provides communication and control channels between human subjects and the surrounding environment with the purpose of replacement or augmentation of muscle activity [ 1 ]. Common classes of bio-signals used to control assistive devices include electromyography (EMG) [ 2 , 3 ], electroencephalography (EEG) [ 4 , 5 ], electrooculography (EOG) [ 6 , 7 ], and fusions of these signals [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

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The novel human-computer interface (HCI) using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG) signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC) and Fisher discrimination (FD) criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT) and recognition rate (RR). The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU) performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR) were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF) performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s, respectively. These experiments validate the feasibility of proposed real-time wearable HCI system and algorithms, providing a potential assistive device interface for persons with disabilities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

Sun

Zhang

Zhao

et al. 2018

Sensors

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“…Because of the recognizing mechanism described above, to make use of double blink for changing focal length, a time delay around 500 ms was introduced into the system. The delay between the eye movement and the response of the system was also introduced in the previous work on EOG controlled virtual keyboard or wheelchair . The flowchart of the recognition algorithm is illustrated in Figure S7 in the Supporting Information.…”

Section: Resultsmentioning

confidence: 98%

A Biomimetic Soft Lens Controlled by Electrooculographic Signal

Wang

Liu

et al. 2019

Adv Funct Materials

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Thanks to many unique features, soft robots or soft machines have been recently explored intensively to work collaboratively with human beings. Most of the previously developed soft robots are either controlled manually or by prewritten programs. In the current work, a novel human-machine interface is developed to use electrooculographic signals generated by eye movements to control the motions and the change of focal length of a biomimetic soft lens. The motion and deformation of the soft lens are achieved by the actuation of different areas of dielectric elastomer films, mimicking the working mechanisms of the eyes of human and most mammals. The system developed in the current study has the potential to be used in visual prostheses, adjustable glasses, and remotely operated robotics in the future.of freedom motions of wheelchair and robots, [7,[18][19][20][21] the current work is the first proof-of-concept design of using EOG signals to control soft machines. Thanks to the fast response of the DEs, [36] the motion and deformation of the soft lens could be easily synchronized with the movements of the eyes. The system developed in the current study has the potential to be used in visual prostheses, adjustable glasses, and remotely operated robotics in the future.

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“…The average accuracy of the other systems was greater than 90% with ITR greater than 56 bits/min. Some of these methods deliver a very high accuracy at a high transfer rate, but are too large and immobile for realistic wearable applications [67]- [72]. The system demonstrated by Graybill et al [73] involved developing smaller, more mobile, light insensitive and more inexpensive solutions.…”

Section: Resultsmentioning

confidence: 99%

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

Tanwear

Liang

Liu

et al. 2020

IEEE Trans. Biomed. Circuits Syst.

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The tracking of eye gesture movements using wearable technologies can undoubtedly improve quality of life for people with mobility and physical impairments by using spintronic sensors based on the tunnel magnetoresistance (TMR) effect in a human-machine interface. Our design involves integrating three TMR sensors on an eyeglass frame for detecting relative movement between the sensor and tiny magnets embedded in an in-house fabricated contact lens. Using TMR sensors with the sensitivity of 11mV/V/Oe and ten <1 mm 3 embedded magnets within a lens, an eye gesture system was implemented with a sampling frequency of up to 28 Hz. Three discrete eye movements were successfully classified when a participant looked up, right or left using a threshold-based classifier. Moreover, our proof-ofconcept real-time interaction system was tested on 13 participants, who played a simplified Tetris game using their eye movements. Our results show that all participants were successful in completing the game with an average accuracy of 90.8%.

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An EOG-Based Human–Machine Interface for Wheelchair Control

Abstract: The proposed HMI provides a novel nonmanual approach for severely paralyzed individuals to control a wheelchair. Compared with a newly established EOG-based HMI, the proposed HMI can generate more commands with higher accuracy, lower FPR, and fewer electrodes.

Cited by 85 publications

References 25 publications

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

A Biomimetic Soft Lens Controlled by Electrooculographic Signal

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

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