Human–machine interface based on muscular and brain signals applied to a robotic wheelchair

Ferreira, André; Silva, R L; Celeste, Wanderley Cardoso; Bastos, Teodiano; Sarcinelli-Filho, Mário

doi:10.1088/1742-6596/90/1/012094

Cited by 38 publications

(27 citation statements)

References 3 publications

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“…In considering the potential capabilities of these bioelectric signals, their extracted features can be used individually or combined in many research areas, especially in designing human-machine interfaces. It was found that even the recorded signals from facial muscle activities show noticeable pattern changes as subjects began to become tired from repeated smiling, nose wrinkling, or frowning, causing the bioelectric signals to gradually lose similarity among the patterns as time goes [10].…”

Section: Introductionmentioning

confidence: 99%

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

Rezazadeh

Firoozabadi

et al. 2011

Australas Phys Eng Sci Med

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This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed.

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Section: Introductionmentioning

confidence: 99%

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

Rezazadeh

Firoozabadi

et al. 2011

Australas Phys Eng Sci Med

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“…Both devices use high-level motion primitives (e.g., go to the kitchen) in a menu-based system. Another synchronous device is the Ferreira et al wheelchair,which uses the desynchronization of alpha rhythms in the visual cortex that occur when the eyes are open or closed [17]. 1 This desynchronization is used as a binary input to select low-level motion primitives (e.g., front, back, left, and right) in a sweeping menu-based system.…”

mentioning

confidence: 99%

A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation

et al. 2009

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Abstract-This paper describes a new noninvasive brainactuated wheelchair that relies on a P300 neurophysiological protocol and automated navigation. When in operation, the user faces a screen displaying a real-time virtual reconstruction of the scenario and concentrates on the location of the space to reach. A visual stimulation process elicits the neurological phenomenon, and the electroencephalogram (EEG) signal processing detects the target location. This location is transferred to the autonomous navigation system that drives the wheelchair to the desired location while avoiding collisions with obstacles in the environment detected by the laser scanner. This concept gives the user the flexibility to use the device in unknown and evolving scenarios. The prototype was validated with five healthy participants in three consecutive steps: screening (an analysis of three different groups of visual interface designs), virtual-environment driving, and driving sessions with the wheelchair. On the basis of the results, this paper reports the following evaluation studies: 1) a technical evaluation of the device and all functionalities; 2) a users' behavior study; and 3) a variability study. The overall result was that all the participants were able to successfully operate the device with relative ease, thus showing a great adaptation as well as a high robustness and low variability of the system.

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“…Matsumoto and Ino et al [11] applied the recognition of head motion and eye gaze onto a locomotive wheelchair system. Ferreira and Silvaet al [12] proposed an HMI structure to control a robotic wheelchair by scalp EMG and EEG signals. Both eye blinking and eye close movements are used as control commands to control a mobile wheelchair through an onboard PDA.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance of a face movement based wheelchair control interface in an indoor environment

Wei

Lü

et al. 2010

2010 IEEE International Conference on Robotics and Biomimetics

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This paper presents a novel facial movement based human machine interface for an intelligent wheelchair to operate in an indoor environment. Five random selected intact subjects are designated to control a wheelchair by using the proposed control interface. The testing is carried out in a designed indoor environment. The system performance is evaluated by implementing same tasks ten times, with the criteria of control easiness and the time spent on each task. The experimental result shows that this new control strategy can assist disabled and elderly users for hands-free control of an electric powered wheelchair.

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Human–machine interface based on muscular and brain signals applied to a robotic wheelchair

Cited by 38 publications

References 3 publications

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation

Evaluating the performance of a face movement based wheelchair control interface in an indoor environment

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