Joint-angle-based yoga posture recognition for prevention of falls among older people

Date03 September 2019
Published date03 September 2019
AuthorPonmozhi Chezhiyan,Deepalakshmi P.
Subject MatterLibrary & information science
Joint-angle-based yoga posture
recognition for prevention of falls
among older people
Ponmozhi Chezhiyan
Department of MCA, Kalasalingam University,
Srivilliputhur, India, and
Deepalakshmi P.
School of Computing, Kalasalingam University, Srivilliputhur, India
Purpose United NationsWorld Population Ageing Report states that falls are one of the most common
problems in the elderl y around the world. Falls are a leading cause of mo rbidity and mortality among
mature adults, and the second leading cause of accidental or unintentional injury/death after road traffic
injuries. The rates are h igher in hospitalized p atients and nursing hom e residents. Major cont ributing
reasons for falling a re loss of footing or tra ction, balance proble m in carpets and rugs, r educed muscle
strength, poor vision, mobility/gait, cogn itive impairment: in ot her words lack of balance . Balance
can be improved by the practice of yog a which helps to balance both body and mind t hrough a series of
physical postures call ed asanas, breathing co ntrol and meditation. El ders, especially wome n, are often
unable to practice yoga r egularly, largely brought on by a feeling of disc omfort 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 syste m can be developed to help suc h elders, though imprope r training and the postur es
associated with it may harm th e bodys muscles and ligaments. To havea flawless system it is essential to
classify asanas, and id entify the one the practitioner is currently practici ng, following which the system ca n
offer the guidance necessary. The purpose of this paper is to propose a posture recognition system,
especially of sitting a nd standing postures . Asanas are chiefly cla ssified into two: sittin g and standing
postures. This study h elps to decide the values of the parameters for classifica tion, which involve the hip
and joint angles.
Design/methodology/approach 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 parentchild relationship. So that whenever there is a change in joint
its corresponding parent joint may also be altered.
Findings The findings have to do with analyzi ng the reasons for falls in the elderly and their need for
yoga as a precautiona ry measure. As yoga is ideally suited to self-assis ted learning, it is feasible t o design a
system that assists pe ople who do not wish to pra ctice yoga in public. H owever, asanas are to be
classified prior to doi ng so. In this paper, the aut hors have designed a po sture identificatio n framework
comprising the sitting and s tanding postures that are fun damental to all yoga asanas, us ing joint angle
measurements. Havin g fixed joint angle values is not possibl e, given the variations in angle values amo ng
the participants. Co nsequently, such paramete rs as the hip joint and knee angle s are to be specified in range
for a classification of asanas.
Research limitations/implications 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.
Practical implications 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.
Social implications 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.
Originality/value 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.
Keywords Falls, Yoga, Cognitive impairment, Assisted learning, Joint angle, Posture recognition
Paper type Research paper
Data Technologies and
Vol. 53 No. 4, 2019
pp. 528-545
© Emerald PublishingLimited
DOI 10.1108/DTA-03-2019-0041
Received 19 March 2019
Revised 5 August 2019
Accepted 3 September 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
1. Introduction
According to the World Health Organization, by 2050, there willbe around 2.1 billion people
aged over 60 years ( On the basis of the United
NationsWorld Population AgeingReport, falls are one of the most common problems in the
elderly aroundthe world. A fall is definedas a sudden movement downward,usually resulting
in injury. Elderly people with osteoporosis have fragile, brittle bones that break in a fall.
An estimate states that low- and middle-income countries will face 75 percent fall injuries
(Hestekin et al., 2013). Also, studies like those of Jagnoor et al. (2013) show that the major
health issue to be addressed by Asian countries is falls in the elderly.
The risk of falling is higher in elders with cognitive impairment than in other elderly
people (Shaw, 2007). Cognitive impairment reduces the ability of the executive function,
which is the ability to judge postural modification when needed. It is a lack of judgment in
terms of posture or poor cognitive flexibility that causes falls: that is, there is an inability
to calculate an appropriate gait when needed, as in, for instance, facing an obstacle or
sudden exposure to different lighting. A fear of falling is a related condition that restricts
the physical and social activities of older people (Gazibara et al., 2017). The risk of falling
increases when people with a fear of falling consciously modify their gait (Huang et al.,
2016). Regular exercise has shown a reduced risk of up to 35 percent in falls (Sherrington
et al., 2011). Research studies conducted to improve executive function through cognitive
enhancement have demonstrated promising results (Segev-Jacubovski et al., 2011).
Intervention methods may reduce the risk of falls as well as a fear of falling (Kumar et al.,
2016). Tennstedt et al. (1998) suggest that exercise, coupled with cognitive restructuring,
may offer better results than exercise alone.
Improving executive function through exercise, dual-task training, or cognitive
enhancement offers the best results (Segev-Jacubovski et al., 2011). Yoga has been considered
an alternative to physical exercise, and yoga intervention has a positive impact on reduced fall
risk factors (Morris, 2008).
Yoga uses a series of physical postures called asanas, breathing control and meditation.
Since yoga concentrates on both body and mind, it is far more therapeutic than exercise
(Chan et al., 2005). The practice of yoga must incorporate the stretching of major muscle
groups, so contributing to physical strength and flexibility (Van Puymbroeck et al., 2007).
Physical balance is one of the factors that influences falls (Vieira et al., 2016).
A community survey (Roland et al., 2011) states that regular yoga practice improves leg
strength and hence balance during mobility. The practice of yoga also improved associated
functional measures identified as predictors of fall risk (Tiedemann et al., 2008).
An improvement in gait andbalance has been reported, followingyoga program conducted
in aged care homes (Krishnamurthy and Telles, 2007). Balance and mobility in older people can
be improved by the practice of yoga (Tiedemann et al., 2013; Brown et al., 2008).
Several yoga-based interventions conducted in the community have been addressed.
Despite the fact that interventions help reduce falls and the fear of falling, elderly persons
with these issues may not readily come forward to participate in such community-based
programs (Tennstedt et al., 1998).
Certain elderly people evince discomfort at practicing yoga in public, and this is
especially true of women, who prefer to do so at home and at leisure. It is preferable
to practice yoga in a closed location with no disruption. In rural areas, however, yoga
trainers are scarce (Hamrick et al., 2017), and this factor is a major driver in the creation of a
self-assisted yoga training system.
Learning is often associated with two conditions, location and time, and is applicable to
exercise or yogaby the elderly as well. Given a general disinclination on the part of the elderly
to exercise or practiceyoga, the place and time of practice are to be flexible,in line with their
personal preferences. In addition, the qualityof learning is based on the individuals attitude
Yoga posture

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