1. Paula R. Pullen1 Afebuameh Ogbesor2, and William S. Seffens2
Kinect Acquisition of Skeleton Body Positions during Yoga and Tai Chi for Exergame Development
Paula R. Pullen1 Afebuameh Ogbesor2, and William S. Seffens2
Health and Human Performance, Georgia SouthWestern State University, Americus, GA.,
Physiology Department, Morehouse School of Medicine, Atlanta, GA.
Introduction
Exergaming or fitness game is a term used for video games that are also a form of exercise. Exergaming relies on technology that tracks body movement or reaction and has been credited with upending the stereotype of gaming as a sedentary activity. Newer systems such as Xbox use input devices like the Kinect that measures and scores movement then super-imposes animated objects to be interacted with over a video image of the user. In healthy older adults there is evidence that dance exergaming can improve mental health and improve measures of physical performance. A yoga exergame would have application in health promotion and therapy for a broad audience.
Inaccuracy of position capture was measured by the number of imputed joint positions in the skeleton stream using Kinect One sensor running SDK v2 software from Microsoft. A two-sample equal variance t-test was used for difference test.
In order to estimate energy expenditure during an exergame session, we measured O2 consumption rates for selected yoga postures using a Parvo Medics TrueOne 2400 metabolic cart. Setup and calibration as detailed in operating manual.
and supine positions, and separately 10 Tai Chi motions were performed (Figure 1). For both exercises, perpendicular orientation relative to sensor was slightly more accurate than frontal, but not significantly different (yoga data in [3]). Yoga standing poses were significantly more accurate than seated or supine body orientations. In contrast all tai chi motions were accurately captured. Assessment of positional accuracy in a therapeutic Exergame for yoga would need a state aware algorithm to correctly predict body positions in an exergame.
To add energy expenditure of participant as output during exergame for assessment, we measured O2 consumption for a series of yoga positions as done in [1]. As expected, more intense positions have higher energy consumption. Values will be programmed into the exergame and used when participant state is determined, either from the skeleton output, or from the depth stream.
Figure (2): Oxygen Consumption of a Yoga Posture
Purpose
We intend to utilize smart and connected technologies to augment yoga-based therapeutic intervention for clinic or home settings. For this we assessed the 3D room sensor built into the Microsoft Kinect for qualitative analysis of capturing yoga and Tai Chi postures. This low cost hardware/software gaming platform could be used to assess therapeutic outcomes such as compliance to ideal postures, respiration, or energy expenditure. This application can be delivered as a downloadable Exergame for PC or Xbox and engage multiple participants for motivation and intensity [2].
Methods
We captured whole body positions of a certified yoga instructor performing Yoga and Tai Chi in front of a Kinect attached to a PC (Figure 1).
References
1 - Clay, C. C., Lloyd, L. K., Walker, J. L., Sharp, K. R., & Pankey, R. B. (2005). The metabolic cost of hatha yoga. The Journal of Strength & Conditioning Research, 19(3),
2 - W. Peng and J Crouse (2013) “Playing in parallel:the effects of multiplayer modes in active video game on motivation and physical exertion”, CyberPsychol, Behavior, & Soc. Networking, 16:
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 Sept Poster 18.
Metabolic Equivalents for Yoga Postures
Figure (1): Exergame Skeletal Image
Conclusion
Therefore, to utilize Kinect in self-guided yoga therapies will require state-aware software using the raw depth data stream. From measurements of the metabolic rates for each state, it should be possible to provide the participant with an estimate of calories consumed during exercise.
Acknowledgements/Disclosures
Supported by 8U54MD and 1RC4MD grants from National Institute On Minority Health and Health Disparities, and Georgia Southwestern State University. The content is solely the responsibility of the authors and does not necessarily represent official views of the respective institutions.The authors of this presentation have no disclosures.
Results
Prior work has shown that some yoga positions are not accurately captured by the skeleton algorithm with either the first or second version of Kinect [3]. We expanded that work by comparison to other exercise modalities. A series of 17 hatha yoga postures, including a variety of standing