Robust Bio-Signal Based Control of an Intelligent Wheelchair

Xu, Xiaodong; Zhang, Yi; Luo, Yuan; Chen, Dongyi

doi:10.3390/robotics2040187

Cited by 24 publications

(19 citation statements)

References 25 publications

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“…[38], only two superficial EMG electrodes were used to detect shoulder movements to finally control a wheelchair. In addition, Xu et al suggested a system to control a wheelchair using EMG signals generated by the facial movements [57]. Nonetheless, facial and shoulder muscles, used in Refs.…”

Section: Introductionmentioning

confidence: 99%

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A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Shahin

Tharwat

Gaber

et al. 2017

Journal of Intelligent Systems

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Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

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Section: Introductionmentioning

confidence: 99%

“…Nonetheless, facial and shoulder muscles, used in Refs. [23,38,57], are weak and cannot be used for a long period [26].…”

Section: Introductionmentioning

confidence: 99%

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Shahin

Tharwat

Gaber

et al. 2017

Journal of Intelligent Systems

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show abstract

“…During the past decade, a number of navigation algorithms have been explored for IWs, and most of them have used various range sensors for obstacle detection and avoidance [4,5,6,7,8,9,10,11,12]. These sensor-based navigation systems consider objects that protrude more than a given distance from the ground as obstacles.…”

Section: Introductionmentioning

confidence: 99%

Wheelchair Navigation System for Disabled and Elderly People

Kim

2016

Sensors

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An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy.

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“…In general SVM, mainly with RBF kernel, performs with excellent accuracy even in case of a high number of tasks to be recognized, as in [11,32,138,149,151]. Also the low recognition time, typical of some particular SVM implementations, as pointed out in [145,149,151,158], makes SVM preferable for real-time control. As a result, with respect to other classifiers commonly employed in the HCI literature for the classification of physiological patterns, such as LDA and ANN, SVMs have peculiarities that make them recommended when dealing with particular classification scenarios: 1) Insensitivity to big amount of features: SVM is insensitive to the number of features extracted to describe data, thus leading to a reduction of the problem complexity.…”

mentioning

confidence: 99%

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

et al. 2017

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Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

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Robust Bio-Signal Based Control of an Intelligent Wheelchair

Cited by 24 publications

References 25 publications

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Wheelchair Navigation System for Disabled and Elderly People

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

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