euro-nursing-2019-posters-abstracts

Page 30

Notes:

allied

academies

March 04-05, 2019 | London, UK

European Nursing Congress

Journal of Intensive and Critical Care Nursing | Volume 2

Long-term behavioural modal observation and risk warning in the elderly

Chong Tian, Jie Li, Qing Yang

and

Jing Mao

Huazhong University of Science and Technology, China

opulation aging is a common problem facing all over the

world. China has the biggest elderly population in theworld.

China’s population structure has started to show an inverted

pyramid trend since 2010. Human resources for aged care are

seriously insufficient. At the same time, due to the increase

in the number of empty nested families and families that lost

their only child, traditional home care functions are gradually

disintegrating, most of the elderlies lives by themselves in most

of the time. Theelderlypopulation facesmany risks, suchas falls,

falling from bed, cardiovascular and cerebrovascular incidents.

Prevention, in-time detection and management of these

situations are critical to the life safety of the elderlies. How to

ensure the safety of the elderly population in the case of limited

human resources has become an important practical issue.

Therefore, we focused on developing an intelligent system that

can acquire, identify and analyze the behavior of the elderly and

promptly alert the abnormalities. Meanwhile, corresponding

emergency response and nursing protocol are developed. At

present, the technologies for intelligent monitoring and early

warning of the elderly mainly include: Wearable devices, 2D

cameras and sensors. For wearable devices, the elderlies are

easy to forget to wear, and the effect will be compromised;

2D cameras are sensitive to changes in lighting, and privacy is

a great concern; Sensors are relatively expensive for most of

the families in China. We tried to develop a new strategy using

deep camera combined with machine learning technology. It

does not affect the daily life of the elderly or change the living

habits of the elderly and be work around the clock. Alert will be

triggered when accidents like falling or fall off the bed happens.

Moreover, interpretation of the uploaded data will provide

evidence for personalized intelligent care.