Alternative Human-Machine Interface system for powered wheelchairs

Berjon, R.; Mateos, Mariano Raboso; Barriuso, Alberto López; Muriel, I.; Villarrubia, Gabriel

doi:10.1109/segah.2011.6165452

Cited by 12 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Berjón et al [39] introduced a system which combines alternative Human-Computer Interfaces including head movements, voice recognition and mobile devices. The head movement detection part uses an RGB camera and image processing techniques.…”

Section: Head Movement Detectionmentioning

confidence: 99%

Eye Tracking and Head Movement Detection: A State-of-Art Survey

Al‐Rahayfeh

Faezipour

2013

IEEE J. Transl. Eng. Health Med.

207

View full text Add to dashboard Cite

Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated.

show abstract

Section: Head Movement Detectionmentioning

confidence: 99%

Eye Tracking and Head Movement Detection: A State-of-Art Survey

Al‐Rahayfeh

Faezipour

2013

IEEE J. Transl. Eng. Health Med.

207

View full text Add to dashboard Cite

show abstract

“…The method presented in [5] employs speech recognition to control the movement of the wheelchair in different directions (back, forward, left, right), they intend to use this system for people that cannot use any other user interface, however, the low bandwidth of this kind of interface makes it difficult for the user to drive if no further assistance is provided. In [6] the Google Speech Recognition Service and Microsoft SAPI are used in combination with a 2D face tracking to control a mobile robot. The speech recognition is used to switch between modes.…”

Section: Related Workmentioning

confidence: 99%

“…1 .02 .06 .02 .03 .25 d (4) .03 .02 .03 .35 .01 .35 .02 .02 .03 .1 d (5) .15 .01 .01 .01 . 1 .01 .02 .03 .45 .2 d (6) .03 .02 .02 .03 . 1 .25 .01 .04 .45 .02 d (7) .03 .02 .03 .…”

Section: User's Intention Estimationmentioning

confidence: 99%

Multimodal control of a robotic wheelchair: Using contextual information for usability improvement

Escobedo

Spalanzani

Laugier

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

In this paper, a method to perform semiautonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interface was used. The user's intention was modeled as a set of typical destinations visited by the user. The algorithm was implemented to one experimental wheelchair robot. The new application of the wheelchair system with more natural and easy-to-use human machine interfaces was one of the main contributions. as user's habits and points of interest are employed to infer the user's desired destination in a map. Erroneous steering signals coming from the usermachine interface input are filtered out, improving the overall performance of the system. Human aware navigation, path planning and obstacle avoidance are performed by the robotic wheelchair while the user is just concerned with "looking where he wants to go".

show abstract

“…Finally, a research group of the Pontifical University of Salamanca carried out a project, called AESCOLAPIUS, for the construction of a wheelchair able to move using the different functionalities that smartphones offer. Within the scope our work the most interesting functionality is the offered by the accelerometer, since the chair reacts to the movements made with the device [11].…”

Section: Devices With Accelerometermentioning

confidence: 99%