Skip to Main content Skip to Navigation

Visual intention detection algorithm for wheelchair motion

Abstract : In this thesis, a methodological and algorithmic approach is proposed, for visual intention recognition based on the rotation and the vertical motion of the head and the hand. The context for which this solution is intended is that of people with disabilities whose mobility is made possible by a wheelchair. The proposed system is an interesting alternative to classical interfaces such as joysticks and pneumatic switches. The video sequence comprising 10 frames is processed using different methods leading to the construction of what is referred to in this thesis as an “intention curve”. A decision rule is proposed to subsequently classify each intention curve. For recognition based on head motions, a symmetry-based approach is proposed to estimate the direction intent indicated by a rotation and a Principal Component Analysis (PCA) is used to classify speed variation intents of the wheelchair indicated by a vertical motion. For recognition of the desired direction based on the rotation of the hand, an approach utilizing both a vertical symmetry-based approach and a machine learning algorithm (a neural network, a support vector machine or k-means clustering) results in a set of two intention curves subsequently used to detect the direction intent. Another approach based on the template matching of the finger region is also proposed. For recognition of the desired speed variation based on the vertical motion of the hand, two approaches are proposed. The first is also based on the template matching of the finger region, and the second is based on a mask in the shape of an ellipse used to estimate the vertical position of the hand. The results obtained display good performance in terms of classification both for single pose in each frame and for intention curves. The proposed visual intention recognition approach yields in the majority of cases a better recognition rate than most of the methods proposed in the literature. Moreover, this study shows that the head and the hand in rotation and in vertical motion are viable intent indicators
Document type :
Complete list of metadata

Cited literature [118 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Tuesday, February 26, 2013 - 10:02:31 AM
Last modification on : Tuesday, October 19, 2021 - 4:08:18 PM
Long-term archiving on: : Monday, May 27, 2013 - 5:00:51 AM


Version validated by the jury (STAR)


  • HAL Id : tel-00794527, version 1



Thierry Luhandjula. Visual intention detection algorithm for wheelchair motion. Other [cs.OH]. Université Paris-Est, 2012. English. ⟨NNT : 2012PEST1092⟩. ⟨tel-00794527⟩



Record views


Files downloads