Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly
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Original versionHuang, 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.2013.587
— 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.