Harnessing Consumer Internet of Things Devices for Real-Time Health Monitoring via an Ensemble of Representation Learning Approach

In recent years, the next-generation photonics and wearable devices have been progressing quickly towards the era of the Internet of Things (IoT) and artificial intelligence (AI), to reach a higher level of intelligence, comfort, convenience, and connection. AI technology is becoming increasingly important in the domain of tele-exercise, permitting workout programs to be customized to users’ requirements and capabilities. AI is applied to analyze data gathered from wearable electronics and environmental sensors and offer personalized recommendations and responses to the customers. Furthermore, a rise in the number of healthcare wearable IoT choices is making it challenging for individuals, healthcare specialists, and doctors to find the correct smart device that best suits their needs or treatments. This paper presents a Consumer Devices-Driven Smart Health Monitoring Framework Using Independent Component Analysis and Ensemble Classifiers (CDSHMF-ICAEC) model. The aim is to explore the integration of AI-driven consumer electronics and wearable IoT devices for enhanced user experience and real-time health monitoring. Initially, the inter-quartile range (IQR) method is applied in the data pre-processing stage for scaling the input data. Moreover, the independent component analysis (ICA) technique is utilized for the feature selection process to recognize and retain the most significant attributes from a dataset. Furthermore, the ensemble classifiers of variational autoencoder (VAE), temporal convolutional network (TCN), and deep belief network (DBN) models are employed for classification. The experimentation of the CDSHMF-ICAEC method is performed under the Wearables dataset. The comparison study of the CDSHMF-ICAEC method portrayed a superior accuracy value of 98.57% over existing techniques.