3D+Stereo Augmented Reality for Landing Systems Feasibility and Experiment Design

Gorbunov, Andrey L.

doi:10.5923/j.scit.20120201.04

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Palm movement can be represented using first order Markov system. Such assumptions are common in sequence detection, especially in the context of augmented reality [26], human computer interaction [10], and health-care related applications [28]. Our model captures feature variations to train the parameters of the proposed Bayesian framework.…”

Section: Problem Formulation and Contributionsmentioning

confidence: 99%

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

Vamsikrishna

Dogra

Desarkar

2016

IEEE Trans. Biomed. Eng.

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Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

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Section: Problem Formulation and Contributionsmentioning

confidence: 99%