Telecommunication Advances in Cognitive HCI: D2D Cloud Networks and Body
Sensor Data Analytics
Abstract
Recent advancements in Cognitive Technical Systems (CTS) have
significantly transformed human-computer interaction (HCI) by
introducing efficient and natural operating principles. These systems
heavily rely on data from multiple sensors, which are integrated using
fusion algorithms to enhance their functionality. This study proposes a
novel cognitive HCI approach, leveraging body sensor data analytics
through machine learning within a Mobile Health Communication D2D cloud
framework. The core of this research involves employing a Boltzmann
Perceptron Basis Encoder Neural Network to analyze various datasets
collected from body sensors within the D2D cloud network. The
experimental analysis evaluates the efficacy of this approach across
different performance metrics, highlighting its superiority over
existing methods. The proposed technique demonstrates enhanced
efficiency in processing and interpreting monitored data, contributing
to advancements in personalized healthcare and interactive computing
environments.