Parametric Bayesian Rejuvenation in Ambient Assisted Living through Software-based Thematic 5G Management

TL;DR

This paper proposes a Bayesian, topic-model based framework to predict and enhance elderly engagement in ambient assisted living by managing context-aware services through 5G and IoT technologies.

Contribution

It introduces a novel context-aware model that captures complex relationships and scalability of elderly engagement using a software-based thematic 5G management approach.

Findings

Effective prediction of elderly engagement achieved

Framework successfully manages context relevance and scalability

Improved personalization of elderly care services

Abstract

Ameliorating elderly engagement is vital in rejuvenating independent living. However, recommended practices lack realization of personal traits despite socio -economic promise. The recent proliferation of IoT with the advent of smart-objects/things and personalized services pave the way for context-aware service management. Eventually, the major goal of this paper is to develop a context-aware model in predicting engagement of elderly care. Hence, key requirements are identified for elderly engagement, namely (a) discovery of contexts, which are relevant (b) scaling up (over time) of engagement. However, paramount challenges are imposed on this stipulation, such as, un-observability, independence and composite relationship of contexts. Therefore, a Topic-model based model is proposed to address scalability of contexts and its conjugal relationship with engagement. Eventually, systematic framework is demonstrated, which pinpoints key goals of context-aware services by participants' opinions, usage and feed-back.