E-Coaching

Machine Learning in e-Coaching, with a focus on health informatics, machine learning algorithms, collection and distribution of personal e-Health data and automatic generation of personalized recommendations for Lifestyle Diseases.

Currently, I am pursuing my Ph.D. from the University of Agder at the Faculty of Engineering and Science, Department for Information and Communication Technologies with a specialization in e-Health. I am doing expertise in Artificial Intelligence, Persuasive Computing, Human-Computer Interaction (HCI), Domain Ontology, IoT and Data Mining. I completed both my bachelor's and masters in computer science and engineering from India with major publications. In my Ph.D., I am doing research in the 'Smart e-Coach based Recommendation System' with IoT, machine learning algorithms, data analytics, cloud infrastructure, and secure data handling. I am proficient in Java (OCJP), Advanced Java (OCWCD), Spring, ORM, RPC, Web services (SOAP and REST), Python Libraries, Matlab, SQL, and NoSQL. I have 9+ years of experience in IT Consultation as a solution designer & developer with more than 3.4 years of deputation in Europe (Denmark, and The Netherlands) for serving leading Telecommunication (TDC-YouSee), Energy (Vattenfall), and Healthcare (J&J) clients.

About the Project

My project is about “Machine Learning in e-Coaching, with a focus on health informatics, machine learning algorithms, collection and distribution of personal e-Health data and automatic generation of personalized recommendations for Lifestyle Diseases”. Chronic illness associated with modifiable lifestyle factors will be accountable for the highest death worldwide. Health behavior change should be given priority to avoid serious damages. An e-Coach system can empower people to manage a healthy lifestyle with early risk predictions and appropriate individualized recommendations. Research in e-Health can provide methods to improve personal healthcare with ICT. An eHealth virtual coaching recommendation system can guide people and convey the appropriate recommendations in context with enough time to prevent, ameliorate or improve the living with non-communicable diseases. This Ph.D. research aims to design, develop, test and evaluate the performance of an intelligent e-Coach system for automatic generation of meaningful, observational and empirical evidence-based, context-specific, personalized recommendations to achieve personal wellness goals, addressing obesity as a study case. The digital e-Coaching system will capture physiological, behavioral (sleep, diet, exercise) and contextual data from secure wearable sensors, manual interactions, feedback, and customized questionnaires over time, to train a machine learning model for behaviour analysis, early prediction of wellness trends and risks. Once risk prediction is done, the e-Coaching system will reinforce recommendations for target individuals in order to improve their well-being, health and physical performance by determining and recommending optimal individual lifestyle factors.