An intelligent framework for workouts in gymnasium: M-Health perspective. (January 2018)

• Record Type: Journal Article
• Authors: Bhatia, Munish; Sood, Sandeep K.
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 292
• Page End: 309
• Publication Date: 2018-01
• Subjects: Artificial Neural Networks -- Cloud Centric Internet of Things -- Smart gym -- Mobile healthcare -- Temporal data; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.018
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An intelligent framework for assessing real-time health condition of person during workouts is proposed. Artificial Neural Network is used as a prediction model for determining probabilistic vulnerability to health during exercising. Probabilistic Health vulnerability is visualized using color-coded mechanism for healthcare enhancement. Graphical abstract: Abstract: The Internet of Things (IoT) Technology has the potential to capture real-time health related parameters everywhere. Henceforth, in this paper, the Cloud Centric IoT (CCIoT) Technology is utilized to assess the health related attributes of a trainee during exercise sessions in a gymnasium. The proposed system have the capabilities to predict the probabilistic vulnerability to health parameters of a trainee during workouts. For this purpose, back-propagation based Artificial Neural Network (ANN) technique is used as a prediction model, layered into three stages, i.e. Monitoring, Learning, and Prediction. Also, the probabilistic vulnerability is represented in real-time using color-coded technique, depicting the health state of the trainee. The proposed system has been validated using an experiment in which five people were monitored for six days at different gymnasiums. Results are compared with different state-of-the-art techniques for determining the overall effectiveness of the proposed system.