Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly
Chapter, Peer reviewed
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https://hdl.handle.net/10642/1748Utgivelsesdato
2013Metadata
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Originalversjon
Huang, Y-P, Liu, W-H, Chen, S-Y & Sandnes, F.E. (2013). Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly. In: V. Marik, A. M. Tjoa & H. Liu, Proceedings of System, Man and Cybernetics Conference SMC 2013. IEEE Computer Society http://dx.doi.org/10.1109/SMC.2013.587Sammendrag
— June 2012, 11% of the overall population in Taiwan
was over the age of 65. This ratio is higher than the average
figure for the United Nations (8%)
. Critical issues concerning
elderly in healthcare include fall detection, loneliness prevention
and retard of obliviousness. In this study we design type-2 fuzzy
models that utilize smart phone tri-axial accelerometer signals to
detect fall incidents and identify abnormal gaits among elderly.
Once a fall incident is detected an alarm is sent to notify the
medical staff for taking any necessary treatment. When the
proposed system is used as a pedometer, all the tri-axial
accelerometer signals are used to identify the gaits during
walking. Based on the proposed type-2 fuzzy models, the walking
gaits can be identified as normal, left-tilted, and right-tilted.
Experimental results from type-2 fuzzy models reveal that the
accuracy rates in identifying normal walking and fall over are
92.3% and 100%, respectively, exceeding what are obtained
using type-1 fuzzy models.