Now showing items 1-4 of 4

  • A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera 

    Authors:Ar, İlktan; Akgül, Yusuf Sinan
    Publisher and Date:(IEEE, 2014)
    Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However most methods in the literature view this task as a special case of motion recognition. In contrast we propose to employ the three main components of a physiotherapy exercise (the motion patterns the stance knowledge and the exercise object) as different recognition tasks and embed them separately into the recognition system. ...

  • A framework for combined recognition of actions and objects 

    Authors:Ar, İlktan; Akgül, Yusuf Sinan
    Publisher and Date:(Springer-Verlag Berlin, 2012)
    This paper proposes a novel approach to recognize actions and objects within the context of each other. Assuming that the different actions involve different objects in image sequences and there is one-to-one relation between object and action type we present a Bayesian network based framework which combines motion patterns and object usage information to recognize actions/objects. More specifically our approach recognizes high-level actions and the related objects without any body-part segmentation ...

  • A monitoring system for home-based physiotherapy exercises 

    Authors:Ar, İlktan; Akgül, Yusuf Sinan
    Publisher and Date:(2013)
    This paper describes a robust low-cost vision based monitoring system for home-based physical therapy exercises (HPTE). Our system contains two different modules. The first module achieves exercise recognition by building representations of motion patterns stance knowledge and object usage information in gray-level and depth video sequences and then combines these representations in a generative Bayesian network. The second module estimates the repetition count in an exercise session by a novel ...

  • Action Recognition Using Random Forest Prediction with Combined Pose-based and Motion-based Features 

    Authors:Ar, İlktan; Akgül, Yusuf Sinan
    Publisher and Date:(IEEE, 2013)
    In this paper we propose a novel human action recognition system that uses random forest prediction with statistically combined pose-based and motion-based features. Given a set of training and test image sequences (videos) we first adopt recent techniques that extract low-level features: motion and pose features. Motion-based features which represent motion patterns in the consecutive images are formed by 3D Haar-like features. Pose-based features are obtained by the calculation of scale invariant ...