Abstract
The fall of the elderly population is a significant source of serious illness. Various wearable fall warning devices have been created recently to ensure older people's health. However, most of these devices are dependent on local data processing. This paper presents a new algorithm used in wearable sensors to track a real-time fall effectively and focuses on fall detection via fuzzy-as-a-service based on IEEE 1855-2016, Java fuzzy markup language and service-oriented architecture. Fuzzy logic systems (FLSs) have revealed their capability in ambient intelligence (AmI) applications. However, FLS deployment requires committed and quasi-scalable hardware/software systems. Sharing FLSs capability as web services allows flexibility, transparency, load balancing, efficient allocation of resources and ultimately cost-effectiveness. In this study, wearable sensors (i.e., accelerometer and gyroscope) that stimulate human activity monitoring using a rule-dependent FLS are demonstrated. Research findings exhibit that the proposed algorithm could easily differentiate between fall and non-fall occurrences with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively.
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