Identifying elderly activity types by interval type-2 fuzzy models
Yo‐Ping Huang, Jingyu Chen · 2014
Fall detection is an active research topic due to the need to prevent accidents from occurring among increasingly aged population in the world. Fall accident is not only harmful to elderly physical health but also will leave side effects, such as emotional trauma, to their daily life because of fear of falling again. Most fall-related research only focused on proposing methodologies to identify whether fall accidents occurred. This study approaches from analyzing elderly daily activities that may cause fall accidents. Interval type-2 fuzzy models are proposed to automatically detect elderly activity patterns. A multilayer detection system is devised to further identify elderly activity types. Signal Vector Magnitude (SVM) and Signal Magnitude Area (SMA) methods are used to discriminate fall activities from moderate and jog ones so that detection effort can be further simplified. Experimental results reveal that the proposed system can correctly identify fall activities. As for normal walking and jog activities the accuracy rates are higher than 80%.