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May 23-24, 2019 | Vienna, Austria
Nursing Care
28th International Conference on
Journal of Intensive and Critical Care Nursing | Volume 2
J Intensive Crit Care Nurs, Volume 2
Long-term behavioral modal observation and risk warning in the elderly
Chong Tian, Jie Li, Qing Yang and Jing Mao
Huazhong University of Science and Technology, China
P opulation aging is a common problem facing all over
the world. China has the biggest elderly population in
the world. 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. The elderly
population faces many risks, such as 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.
Speaker Biography
Chong Tian got her PhD in school of Public Health, Tongji medical college,
Huazhong University of Science and Technology in 2013 (Wuhan, China).
She is now a teacher in school of Nursing, Tongji medical college, HUST.
She is a highly motivated researcher in nursing of patients with chronic
conditions and the elderly. She has published about 30 academic articles
and co-edited 8 books, in which 2 designated textbooks as national
level and 1 national reports are included. Her H-index of publication is
10 and has been serving as an editorial board member and guest editor
of academic Journals. Her background includes molecular biological
research, population-based research and social science research
experiences. She is now devoted to interdisciplinary innovation care for
patients with chronic conditions and for the elderly, and is working with
data scientists, engineers, clinical doctors, and management scientists.
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