United Nations’ World Population Ageing Report states that falls are one of the most common problems in the elderly around the world. Falls are a leading cause of morbidity and mortality among mature adults, and the second leading cause of accidental or unintentional injury/death after road traffic injuries. The rates are higher in hospitalized patients and nursing home residents. Major contributing reasons for falling are loss of footing or traction, balance problem in carpets and rugs, reduced muscle strength, poor vision, mobility/gait, cognitive impairment: in other words lack of balance. Balance can be improved by the practice of yoga which helps to balance both body and mind through a series of physical postures called asanas, breathing control and meditation. Elders, especially women, are often unable to practice yoga regularly, largely brought on by a feeling of discomfort at having to do so in full public view, preferring instead to have private sessions at home, and at leisure. A computer-assisted self-learning system can be developed to help such elders, though improper training and the postures associated with it may harm the body’s muscles and ligaments. To have a flawless system it is essential to classify asanas, and identify the one the practitioner is currently practicing, following which the system can offer the guidance necessary. The purpose of this paper is to propose a posture recognition system, especially of sitting and standing postures. Asanas are chiefly classified into two: sitting and standing postures. This study helps to decide the values of the parameters for classification, which involve the hip and joint angles.
To model human bodies, skeleton parts such as head, neck (which are responsible for head movements), arms, hands (to decide on hand postures), and legs and feet (for standing posture identification) have been modeled and stored as a vector. Each feature is defined as a set of movable joints. Every interaction among the skeleton joints defines an action. Human skeletal information may be represented as a hierarchy of joints, in a parent–child relationship. So that whenever there is a change in joint its corresponding parent joint may also be altered.
The findings have to do with analyzing the reasons for falls in the elderly and their need for yoga as a precautionary measure. As yoga is ideally suited to self-assisted learning, it is feasible to design a system that assists people who do not wish to practice yoga in public. However, asanas are to be classified prior to doing so. In this paper, the authors have designed a posture identification framework comprising the sitting and standing postures that are fundamental to all yoga asanas, using joint angle measurements. Having fixed joint angle values is not possible, given the variations in angle values among the participants. Consequently, such parameters as the hip joint and knee angles are to be specified in range for a classification of asanas.
This work identifies the angle limits of standing and sitting postures so as to design a self-assisting system for yoga. Yoga asanas are classified and tested to enable their accurate identification. Extensive testing with older people is needed to assess the system.
The increase in the population of the elderly, coupled with their need for medical care, is a major concern worldwide. As older people are reluctant to practice yoga in public, it is anticipated that the proposed system will motivate them to do so at their convenience, and in the seclusion of their homes.
As older people are reluctant to adapt as well as practice yoga in public view, the proposal motivates and helps them to carry out yoga practices at their convenience.
This paper fulfills the initial study on the need and feasibility of creating a self-assisted yoga learning system. To identify postures and classify them joint angles are used; their range of motion has been calculated in order to set them as parameters of classification.
Chezhiyan, P. and P., D. (2019), "Joint-angle-based yoga posture recognition for prevention of falls among older people", Data Technologies and Applications, Vol. 53 No. 4, pp. 528-545. https://doi.org/10.1108/DTA-03-2019-0041Download as .RIS
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