ANN model for users’ perception on IOT based smart healthcare monitoring devices and its impact with the effect of COVID 19

Purpose: COVID-19 was indeed a global epidemic that revolutionized the way of life, especially health-care services. The way health care will be delivered will undergo a dramatic change in the future. The aim is to analyse the increasing usage of health care systems along with digital technology and IoT especially during pandemic. Design Methodology Approach: This research paper deals with users’ perception and their recommendation status of IoT-based smart health-care monitoring devices based on their perception, experience and level of importance to enhance the quality of life. An effective artificial neural networking (ANN)-based predictive model is designed to classify the user’s perception of usage of IoT-based smart health-care monitoring wearables based on their experience and knowledge. Findings: The model developed has 96.7% accuracy. Among the various predictors chosen as inputs for the model, the findings indicate that self-comfort and trusted data from the device are of high priority. The present study focused only on some common factors derived from previous studies. Research Limitations Implications: Although the performance of the proposed system was noticed to be good, the size of the sample is also limited to a few responses. Implications for future research and practices are discussed. Originality Value: This is a novel study that aims to develop an ANN model on analyzing the user’s perception of IoT-based smart health-care wearables with the effect of COVID-19 pandemic. This paper elaborates on the ongoing efforts to restart the health-care services for survivability in the new normal situations.