AN ANALYTICAL STUDY AND VISUALISATION OF HUMAN ACTIVITY AND CONTENT-BASED RECOMMENDATION SYSTEM BY APPLYING ML AUTOMATION

In this smart emerging world, modern day equipment, like wearable devices, not only provides functionality or advancements in lifestyles but also becoming a trending fashion choice. Most of the devices which are wearable provides basic functionalities like display time or date. But implementation of more smart features like displaying messages, phone call or even medical activity recognition can lead the productivity in dense and holds a potential to create a product demanded by a huge number of customers. Smart wearable devices connected to internet approaches the methodology and required application and implementation of secure IoT environment and cloud infrastructure. Compared to other internet connected devices, wearable devices like smart watches are designed to be capable of monitoring activity for 24 hours a day. Mostly they are designed as durable and water resistance with addition of appropriate sensors for required functionalities and detection. In this paper, we are proposing a model for identifying requirements of activity and inactivity recognition by implementing on a secure and smartly designed cloud infrastructure. Here we are also defining a new measurement of heart-rate data applying various machine learning methods.