TY  - JOUR
PY  - 2018//
TI  - Pre-impact alarm system for fall detection using MEMS sensors and HMM-based SVM classifier
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Liang, Shengyun
A1  - Chu, Tianyue
A1  - Lin, Dan
A1  - Ning, Yunkun
A1  - Li, Huiqi
A1  - Zhao, Guoru
SP  - 4401
EP  - 4405
VL  - 2018
IS  - 
N2  - Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2018.8513119
ID  - ref1
ER  -