Thu 09 Dec 2021 15:11

On the 9th of December, Cristian Vilar successfully presented his doctoral thesis "Semi-Autonomous Navigation of Power Wheelchairs, 2D/3D Sensing and Positioning Methods" to the grading committee and he can now entitle himself as doctor.

A happy man in posing infront of camera with a bouquet of flowers and his doctoral thesis.

Opponent to the thesis was Dr. Nicolas Ragot at CESI Ecole d’Ingénieurs in Rouen. In the grading committee was Professor Amund Skavhaug from Norwegian University of Science and Technology. Professor George Nikolakopoulos from Luleå University of Technology and Associated Professor Johan Sidén from Mid Sweden University.


Autonomous driving and assistance systems have become a reality for the automotive industry to improve driving safety in the car. Hence, the cars use a variety of sensors, cameras and image processing techniques to measure their surroundings and control their direction, braking and speed for obstacle avoidance or autonomously driving applications.

Like the automotive industry, powered wheelchairs also require safety systems to ensure their operation, especially when the user has controlling limitations, but also to develop new applications to improve its usability. One of the applications is focused on developing a new contactless control of a powered wheelchair using the position of a caregiver beside it as a control reference. Contactless control can prevent control errors, but it can also provide better and more equal communication between the wheelchair user and the caregiver.

This thesis evaluates the camera requirements for a contactless powered wheelchair control and the 2D/3D image processing techniques for caregiver recognition and position measurement beside the powered wheelchair.

The research evaluates the strength and limitations of different depth camera technologies for caregiver feet detection above the ground plane to select the proper camera for the application. Then, a hand-crafted 3D object descriptor is evaluated for caregiver feet recognition and compared with respect to a state-of-the-art deep learning object detector. Results for both methods are good, however, the hand-crafted descriptor suffers from segmentation errors and consequently, their accuracy is lower. After the depth camera and image processing techniques evaluation, results show that it is possible to use only an RGB camera to recognize and measure his or her relative position.

Man står vid en dator och presenterar sin avhandling som syns på skärm i bakgrunden.

Watch the presentation on Youtube

Watch Cristian present his doctoral thesis "Semi-Autonomous Navigation of Power Wheelchairs, 2D/3D Sensing and Positioning Methods".





The page was updated 12/10/2021