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Title FallDroid: An Automated Smart Phone based Fall Detection System using Multiple Kernel Learning
Journal Name IEEE Transactions on Industrial Informatics
First Author Ahsan Shahzad
Coauthor Kiseon Kim
Publication Date 2018.05.23 Link Link icon
Impact Factor (%) 6.76 Date 2018-07-03 23:16
Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper
presents a pervasive fall detection system developed on smart phones (SPs) namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the embedded
accelerometer signals. Comprising of the threshold based method (TBM) and multiple kernel learning support vector machine (MKL-SVM), the proposed algorithm uses novel techniques to
effectively identify fall-like events (such as lying on a bed or sudden stop after running) and reduce false alarms. In addition to user convenience and low power consumption, experimental
results reveal that the system detects falls with high accuracy (97.8% and 91.7%), sensitivity (99.5% and 95.8%), and specificity (95.2% and 88.0%) when placed around the waist and thigh,
respectively. The system also achieves the lowest false alarm rate of 1 alarm per 59 hours of usage, which is best till date.
광주과학기술원 한·러 MT-IT 융합기술연구센터 광주과학기술원정보통신공학부