Title

41. Gesture Analysis For Yoga Alignment

Faculty Mentor(s)

Paula Seffens

Campus

Gainesville

Proposal Type

Poster

Subject Area

Physical Education

Location

Nesbitt 3110

Start Date

25-3-2016 11:30 AM

End Date

25-3-2016 12:30 PM

Description/Abstract

INTRODUCTION: Yoga Therapy research has recently become the focus of rigorous scientific inquiry in the interest of understanding and quantifying its benefits for a wide variety of medical conditions. There remains a disparity between segments of the population who can readily access yoga classes and therapies. For difficult to reach individuals, Yoga in an exergame format could be utilized in clinical or home environments. The purpose of this study was to analyze Yoga posture alignment using a gesture analysis program in order to produce a yoga exergame using the Microsoft Kinect. We captured six yoga postures demonstrated by an advanced yoga teacher, as a gold standard for comparison purposes. METHODS: Six yoga postures were selected for the basis of the training set using Microsoft Visual Gesture Builder (VGB). Programs utilized were included in Kinect version 2 SDK and ran on a PC. RESULTS: Three 3D video clips of the six yoga postures were captured from the yoga teacher, two for VGB training and one for validation. We found that adding the second training clip increased performance accuracy for four out of the six postures. The forward bend and arms up postures might improve with additional training clips. Our prior research has shown that the Kinect skeleton algorithms become confused with yoga postures that change the usual orientation of the head. A convenience sample of undergraduate students with various levels of yoga experience, were recorded executing the same 6 postures before, at the mid-point and at the conclusion of a 10-week yoga class series. We assessed the longitudinal correlation points between yoga experience level and VGB posture accuracy. CONCLUSION: Gesture analysis for yoga alignment training may be a useful tool for the development of home and clinical yoga therapy for hard to reach populations. The Kinect sensor provides a tool that could score the performance of yoga therapy and provide quantitate measures of posture adherence and improvement. ACKNOWLEDGEMENT: We acknowledge partial support from 8G12MD007602, 8U54MD007588 to MSM from NIH/NIMHD, and the University of North Georgia.

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Mar 25th, 11:30 AM Mar 25th, 12:30 PM

41. Gesture Analysis For Yoga Alignment

Nesbitt 3110

INTRODUCTION: Yoga Therapy research has recently become the focus of rigorous scientific inquiry in the interest of understanding and quantifying its benefits for a wide variety of medical conditions. There remains a disparity between segments of the population who can readily access yoga classes and therapies. For difficult to reach individuals, Yoga in an exergame format could be utilized in clinical or home environments. The purpose of this study was to analyze Yoga posture alignment using a gesture analysis program in order to produce a yoga exergame using the Microsoft Kinect. We captured six yoga postures demonstrated by an advanced yoga teacher, as a gold standard for comparison purposes. METHODS: Six yoga postures were selected for the basis of the training set using Microsoft Visual Gesture Builder (VGB). Programs utilized were included in Kinect version 2 SDK and ran on a PC. RESULTS: Three 3D video clips of the six yoga postures were captured from the yoga teacher, two for VGB training and one for validation. We found that adding the second training clip increased performance accuracy for four out of the six postures. The forward bend and arms up postures might improve with additional training clips. Our prior research has shown that the Kinect skeleton algorithms become confused with yoga postures that change the usual orientation of the head. A convenience sample of undergraduate students with various levels of yoga experience, were recorded executing the same 6 postures before, at the mid-point and at the conclusion of a 10-week yoga class series. We assessed the longitudinal correlation points between yoga experience level and VGB posture accuracy. CONCLUSION: Gesture analysis for yoga alignment training may be a useful tool for the development of home and clinical yoga therapy for hard to reach populations. The Kinect sensor provides a tool that could score the performance of yoga therapy and provide quantitate measures of posture adherence and improvement. ACKNOWLEDGEMENT: We acknowledge partial support from 8G12MD007602, 8U54MD007588 to MSM from NIH/NIMHD, and the University of North Georgia.