A Posture Recognition System for Assisted Self-Learning of Yoga by Cognitive Impaired Older People for the Prevention of Falls

Abstract

According to United Nation’s World Population ageing report, every country is facing an ageing issue by most of their citizens. It is estimated that 75% of fall injuries occur in low- and middle-income countries. Reasons for fall include slipping due to loss of footing or traction, problem in balancing, reduced muscle strength, poor vision, mobility/gait, and cognitive impairment. Fall risk in the older people that too those who are with cognitive impairment is very high than others. Yoga uses a series of physical postures called asanas, breathing control, and meditation. Since Yoga concentrates on both body and mind, it is more therapeutic than exercise. Some older people, especially women, showed discomfort of doing yoga in public view and prefer to do yoga at their home during their free time. Assisted self-learning can address the needs of above said people. Most of the Yoga asanas depend on either sitting or standing postures. The identification of postures is based on the joint angles. Sensor like Orbbec Astra observes the scene and provides skeletal information of a human being. We use this information for calculating the joint angles to identify the postures required as part of an Assisted Self-Learning System.