Machine learning gesture analysis of yoga for exergame development

Abstract

Many successful and innovative information technology applications use gestures as input. These programs span a wide variety of genres, platforms and input technologies, from the touch screen of a smart phone to the full-motion, the natural input of devices like the Kinect Sensor. Visual Gesture Builder, a data-driven machine-learning solution for gesture detection, was used to capture useful yoga gestures with high accuracy. This gesture analysis technology is being explored for incorporation into exergames for personalised medical interventions. The research goal is to test whether a machine learning algorithm in a basic computer video exergame can assess yoga skill acquisition in targeted select populations as a means to promote healthy physical activity.

Keywords

• computer games
• learning (artificial intelligence)
• gesture recognition
• biomechanics
• patient treatment
• exergame development
• input technologies
• touch screen
• smart phone
• Kinect Sensor
• Visual Gesture Builder
• gesture detection
• gesture analysis technology
• personalised medical interventions
• machine learning algorithm
• basic computer video exergame
• yoga skill acquisition
• information technology applications
• gesture analysis
• healthy physical activity