A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective

Citation data:

Computers in Industry, ISSN: 0166-3615, Vol: 92, Page: 50-66

Publication Year:
2017
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DOI:
10.1016/j.compind.2017.06.009
Author(s):
Munish Bhatia; Sandeep K. Sood
Publisher(s):
Elsevier BV
Tags:
Computer Science; Engineering
article description
Enormous potential of Internet of Things (IoT) Technology has made it feasible to perceive and analyze real time health conditions in ubiquitous manner. Moreover, incorporation of IoT in healthcare industry has led researchers around the world to develop smart applications like mobile healthcare, health-aware recommendations, and intelligent healthcare systems. Inspired from these aspects, this research presents an intelligent healthcare framework based on IoT Technology to provide ubiquitous healthcare to person during his/her workout sessions. The intelligence of the presented framework lies with its ability to analyze real time health conditions during workouts and predict probabilistic health state vulnerability. For predictive purpose, the proposed framework indulges the utilization of Artificial Neural Network (ANN) model, which is comprised of three phases namely, monitor, learn, and predict. In addition to this, the presented framework is supported by a mathematical foundation to predict probabilistic vulnerability, in terms of Probabilistic State of Vulnerability (PSoV). In order to determine the validity and applicability of the proposed framework, experiments were performed where 5 people with different attributes are monitored for 14 days using numerous smart sensors. Results, upon comparison with various state-of-the-art techniques, depict that the proposed system is superior in performance and is highly effective in delivering healthcare services during workouts.