TY - GEN
T1 - Fall perception for elderly care
T2 - 2014 1st IEEE International Conference on Communications, ICC 2014
AU - Li, Zhinan
AU - Huang, Anpeng
AU - Xu, Wenyao
AU - Hu, Wei
AU - Xie, Linzhen
PY - 2014
Y1 - 2014
N2 - Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the 'bottleneck' of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level.
AB - Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the 'bottleneck' of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level.
KW - Clinical Trials
KW - Fall Perception
KW - Smart Wristlet
KW - TF-IDF (Term Frequency-Inverse Document Frequency)
KW - mHealth (mobile Health)
UR - https://www.scopus.com/pages/publications/84906996773
U2 - 10.1109/ICC.2014.6883991
DO - 10.1109/ICC.2014.6883991
M3 - Conference contribution
AN - SCOPUS:84906996773
SN - 9781479920037
T3 - 2014 IEEE International Conference on Communications, ICC 2014
SP - 4270
EP - 4274
BT - 2014 IEEE International Conference on Communications, ICC 2014
PB - IEEE Computer Society
Y2 - 10 June 2014 through 14 June 2014
ER -