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Control of a Wheelchair using an Adaptive K-Means Clustering of Head poses

                                                                             by Luis Rivera and L. Danny Franklin 

  Old Demo Video -- First Prize in the IEEE-CIS Undergrad Student Poster Competition

 Introduction

    Operating a wheelchair is often a difficult task for individuals with severe disabilities. Also, with the progress of the condition, the use of most current robotic assistive technologies becomes less attractive or simply not applicable anymore. In this work, we developed a system that allows a user to operate a wheelchair using only their heads. Our method utilizes an Infrared (IR) depth sensor to capture the user’s head pose, while it includes an adaptive component to the detection of that pose. The adaptation, based on a type of Re-enforcement K-Means clustering, can accommodate users with limited and changing head mobility – no matter how skewed the head motion may become with the progress of the condition.

 

Calibration and Normal Driving: Our system begins by performing a calibration of the head motions to tailor the algorithm to each user. The calibration is quick and simple, and it allows the user to set the coordinates of the control system to the most comfortable configuration. After the system is calibrated, the wheelchair is easily controlled through the user’s head motions. Head_Wheelchair_video_normal.wmv

Skewed Head poses: Some people may have difficulties to move their heads orthogonally, and they may have limited ranges of motion. The system can adapt to these situations, allowing users with different degrees of disabilities to control the wheelchair. Head_Wheelchair_video_tilted.wmv

Adaptation: Over short periods of time, users may become fatigued and they may need to reposition their bodies on the wheelchair. Also, over long periods of time, the disease may progress, imposing greater limitations on the ranges and angles of the head motions. In either case, the head motions may start deviating from the calibrated poses. The system can adapt to such changes “on the fly”, without needing a recalibration. Head_Wheelchair_video_adaptation.wmv

 

    References
 

  1. Rivera, L. A., Franklin, L. D. and DeSouza, G. N. “Control of a Wheelchair using an Adaptive K-Means Clustering of Head Poses”. IEEE Symposium Series on Computational Intelligence, Symposium on CI in Rehabilitation and Assistive Technologies, April, 2013. Singapore.

 

 

 

 

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