Abstract
Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence approach is proposed in this study. In the fuzzy collaborative intelligence approach, alpha-cut operations are applied to derive the fuzzy weights of criteria for each decision maker. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers. Subsequently, the fuzzy technique for order preference by similarity to the ideal solution is applied to assess the suitability of a smart technology application for fall detection. The fuzzy collaborative intelligence approach is a posterior-aggregation method that guarantees a consensus exists among decision makers. After applying the fuzzy collaborative intelligence approach to assess the suitabilities of four existing smart technology applications for fall detection, the most and least suitable smart technology applications were smart carpet and smart cane, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using three existing methods.
Highlights
As people get older, fall detection and prevention becomes a more critical task [1]
Chen [17] proposed the fuzzy geometric mean (FGM)-alpha-cut operations (ACO)-fuzzy weighted average (FWA) method to evaluate the sustainability of a smart technology application in mobile health care, including fall detection, in which decision makers’ judgments were aggregated using FGM before deriving the fuzzy weights of criteria using ACO
A fuzzy collaborative intelligence approach is proposed in this study to evaluate the suitability of a smart technology application for fall detection
Summary
Fall detection and prevention becomes a more critical task [1]. In addition, children like to run and jump and may fall down accidently. Chen [17] proposed the fuzzy geometric mean (FGM)-alpha-cut operations (ACO)-fuzzy weighted average (FWA) method to evaluate the sustainability of a smart technology application in mobile health care, including fall detection, in which decision makers’ judgments were aggregated using FGM before deriving the fuzzy weights of criteria using ACO. Only very few specific smart technology applications were compared To solve these problems, a fuzzy collaborative intelligence approach is proposed in this study. A fuzzy collaborative intelligence approach is proposed in this study to evaluate the suitability of a smart technology application for fall detection. The aggregation result is fed into the fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) method [19] to assess the suitability of a smart technology application for fall detection.
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