Gesture Analysis for Yoga Alignment (GAYA) Nataly Gonzalez1, William S. Seffens2 and Paula R. Seffens1 Department of Health & Physical Education, University of North Georgia, Oakwood, GA., Physiology Department, Morehouse School of Medicine, Atlanta, GA. A convenience sample of undergraduate students with various levels of yoga experience, was recorded executing the same five postures before and after the first yoga class. We assessed the correlation points between yoga experience level and VGB posture accuracy. Likert scales are a common rating format for surveys. Statisticians have generally grouped data collected from these surveys into a hierarchy of four levels of measurement (nominal, ordinal, interval, and ratio). Data analyses using nominal, interval and ratio data are generally straightforward and transparent. An underlying reason for analyzing ordinal data as interval data might be the contention that parametric statistical are more powerful than nonparametric alternatives. We assume that the data is interval data calculating the regression. 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. Assuming that the data is interval data we calculated the regression. 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. We recognize that the Likert scale may not be ordinal in nature and plan to further explore the statistical options. However, treating ordinal data as interval (or even ratio) data without examining the values of the dataset and the objectives of the analysis can both mislead and misrepresent the findings of a survey. Another method to use is Spearman's rank correlation coefficient. Spearman's coefficient, like any correlation calculation, is appropriate for both continuous and discrete variables, including ordinal variables. Deciding the statistical methods for the data are being explored. Our future plans, we will increase the sample size, study the potential affects of body mass and age can have on posture alignment. We plan to develop a standardize assessment of yoga skills that will be compared to the VGB scores. 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. Statistical Analysis: Linear Regression Purpose The purpose of this research is to utilize existing gesture analysis software to provide skill improvement feedback to students in a college Yoga course setting. AIM 1: To measure yoga posture alignment over the course of a 10-week period to provide objective feedback toward the goal of improved posture alignment. AIM 2: To improve balance and kinesthetic awareness utilizing noninvasive measurements via an x, y, and z-point skeletal joint measurement platform. AIM 3: To provide a hands on research experience for our Graduate Student Assistant in the department of Health & Physical Activity. References 1 -Field, T., (2011), “Yoga clinical research review”, Complementary Therapies in Clinical Practice, vol. 17:1-8. 2 -Pullen P., Ogbesor, A., William Seffens, (2015), “Kinect Acquisition of Skeleton Body Positions During Yoga and Tai Chi for Exergame Development”, Am Coll. Sports Medicine Annual Meeting poster May 2015 poster 34. 3 - P Pullen and W Seffens, (2014), “Exergame development study of Kinect for yoga postures”, Symposium on Yoga Research, by Int. Assoc. Yoga Therapists, held at Kripalu Institute, Stockbridge, MA. on 24-26 Sept. 2014. Poster 18. 4-2013 Microsoft, (2014), “Visual Gesture Builder: A Data-Driven Solution to Gesture Detection”, Windows v2 SDK, 1-17. Figure (1): Exergame Skeletal Image Demographics Discussion Based on the preliminary results the positive slope of the graph indicates that the greater the self-rated yoga experience level, the higher the gesture score. The higher gesture score the closer the posture execution is to the “gold standard” (obtained by capturing the postures performed by the yoga instructor). Because of the limited sample size it is difficult to conclude the accuracy of the VGB in determining the statistical durability of the study participants experience with yoga. However, because of the inherent differences in participant’s ability to perform yoga at a beginning skill level, the study should yield useful measurements of the skill growth as students learn yoga. Methods Five 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. (Figure 1) In the table for demographics all participants are female (n=8) and for the weight, age, height, and level of yoga we took an average. Yoga experience level was assessed utilizing a Likert Scale from 1-10 (10 being expert instructor level) for level of yoga proficiency. Based on the above table the average BMI was calculated as 24.5 for this cohort. Results Three 3D video clips of the five 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 five postures. Acknowledgements/Disclosures We acknowledge partial support from 8G12MD007602, 8U54MD007588 to MSM from NIH/NIMHD, and the University of North Georgia. .