Real-time hand gesture recognition for uncontrolled environments using adaptive SURF tracking and hidden conditional random fields

Challenges from the uncontrolled environments are the main difficulties in making hand gesture recognition methods robust in real-world scenarios. In this paper, we propose a real-time and purely vision-based method for hand gesture recognition in uncontrolled environments. A novel tracking method is introduced to track multiple hand candidates from the first frame. The movement directions of all hand candidates are extracted as trajectory features. A modified HCRF model is used to classify gestures. The proposed method can survive challenges including: gesturing hand out of the scene, pause during gestures, complex background, skin-coloured regions moving in background, performers wearing short sleeve and face overlapping with hand. The method has been tested on Palm Graffiti Digits database and Warwick Hand Gesture database. Experimental results show that the proposed method can perform well in uncontrolled environments

Online student profile management system

Master of ScienceDepartment of Computing and Information SciencesDaniel A. AndresenAll the students who are enrolled in Computing and Information Sciences (CIS) major in Kansas State University are required to submit their Program of Study (POS) which they manually do by filling in all the necessary details and submit the form to the department. The main objective of this project is to develop an online submission of program of study. The online student profile management system is a web-based application that provides students of CIS major to submit their program of study in an easy and efficient manner. This application mainly allows the students to enter their personal information (viz., contact information, previous education) and to choose core as well as non-core courses of their choice. In addition the faculty of CIS department can also log on to the application and view the POS of the students by entering their wildcat ID. The primary focus is to get familiar with .NET framework and to code in C#.NET. This in turn uses MS SQL server 2005 as database for storing and retrieving of data. This project is implemented using C#.NET on Microsoft visual studio 2005

Plantar Grasp sign as a screening tool for Orthostatic Tremor (OT)

Introduction: Orthostatic tremor (OT) is a rare neurological disorder characterized by a sensation of instability while standing. Very few clinical signs have been described for OT to date. Finding other symptoms and signs could prove valuable for this hard-to-recognized disease. Methods: This protocol is part of the University of Nebraska Medical Center Orthostatic Tremor longitudinal study. It was noted that OT patients flex their toes and sometimes the foot arch while standing (Plantar Grasp). They reported doing this to “grab” the floor and improve stability. This paper analyses the diagnostic test characteristics of the patient-self-reported Plantar Grasp, a new sign in OT. Results: There were 34 OT patients (88% females), and 20 controls (65% females). Eighty-eight percent of patients with OT reported the plantar grasp sign and none of the controls. The Plantar Grasp Sign was found to be very sensitive (88%), and extremely specific (100%) in our cohort. Non-weighted Negative Likelihood Ratio (NLR) was 0.12. And the 3% prevalence-weighted NLR was so low that the negative post-test probability was close to zero. Conclusion: Due to its high sensitivity, specificity, and ideal likelihood ratio, we propose that the Plantar Grasp sign could be considered to screen patients with possible OT. Further studies are needed to determine the specificity of this sign in OT versus other balance disorders