Personalised AI Based Yoga-Pose Recommendation System Using Game Theory
Nidhi Pravin Parab, Alefiya Abbas Rampurawala, Siddhi Sunil Khade, Manimala Mahato · 2023
Yoga is an ancient practice that seeks to unite the mind with divine awareness. However, to derive maximum benefits from yoga, it is important to select the appropriate asana according to an individual’s needs. Not everybody has access to good yoga instructors who can make tailored yoga routines for them. An artificial intelligence (AI)-based model which uses Game Theory on yoga asanas with similar benefits, could help recommend a personalized yoga routine to support users on their journey towards a healthier lifestyle. In certain cases, the person is unable to determine the part of their body that is stiff or causing unease, for example, the stiffness may be very mild or only occur during certain movements or activities. In some cases, the person may simply not be paying close attention to their body or may have become accustomed to the sensation of stiffness in that particular area. A model which uses computer vision can help overcome this issue. The traditional approach to yoga instruction relies on a one-size-fits-all approach, but Game Theory offers a more nuanced approach that considers individual preferences, like medical conditions, areas of discomfort, level of experience and lifestyle goals. The model is tested using data analysis of a test dataset. The paper concludes with a discussion of the implications of the findings for the field of yoga instruction and the results suggest that Game Theory is an effective tool for developing personalized yoga recommendations.