Deep machine learning for Sensing, Analysis, and Interpretation in IoT Healthcare

The introduction of the Internet of Things (IoT) has brought about a much-needed upgrade in healthcare industry worldwide. As the world population continues to increase, data generated in these industries are now collected and transmitted seamlessly over the internet, leading to a more efficient system while reducing healthcare costs. The data generation method requires the use of sensors incorporated into smart devices placed at the wrist, ankle, or other parts of the body. Most smart watches, for example, are equipped with photoplethysmography technology used to monitor the heart rate. The ever-increasing amount of data generated in healthcare through IoT means that analysis and interpretation no longer need to be done manually. New methods and algorithms are required to make meaningful sense of the data; hence the idea of deep machine learning is incorporated into the system. Deep machine learning is a branch of artificial intelligence that involves multiple layers of connected networks, consisting of numbers of neurons to extract features from input data and make predictions off such features. This chapter proposes using deep learning algorithms to train systems capable of making predictive inferences from healthcare data transmitted over the internet. Several deep learning algorithms can be implemented depending on the type of data to be analysed. Artificial neural network, which mimics the human brain’s biological functioning, forms the basis of neural networks and depends on the activation function used to predict continuous, binary, and multiclass labels. A convolutional neural network can also be used when dealing with image data. The objective of this study is to develop healthcare further using intelligent deep learning algorithms. This will help health workers and patients remotely monitor chronic diseases’ health conditions to general fitness attributes. Eventually, our proposed system will be synchronized with IoT-based health monitoring tools and provide results to doctors and health workers, in general, to adequately diagnose and assist in the treatment of a variety of medical conditions.

Recommended citation: O.J. Odeyemi, S.O. Owoeye, K.I. Adenuga and C.B. Emele. (2022). Deep machine learning for Sensing, Analysis, and Interpretation in IoT Healthcare. Reinvention of health applications with IoT: 1-16.
Download Paper