Machine Learning for Sensing Applications: A Tutorial

The developments in microsensor fabrication over the past few decades have contributed to the availability of a wide range of sensors with varying degrees of performance and cost. Many of the recent waves of technological developments such as the Internet-of-Things or wearables rely on such sensors. With the increasing availability of on-board and remote computing power, the trend is to go beyond the simple quantification of events and (re)create context from sensor data using statistical signal processing, or as commonly known, machine learning. Within the scope of this tutorial, we highlight the applications of machine learning in sensing and introduce the fundamental stages for creating data-driven models based on simple machine learning algorithms. We focus on algorithms that are simple to implement, provide accurate results, and yet remain understandable to the human developer. The ability to follow how a data-driven model functions is essential in many engineering applications where a trade-off between accuracy and reliability is often acceptable. We provide case studies that utilize the presented material to solve different real-life applications. These examples demonstrate the importance of choosing appropriate features, selecting algorithms, and finally, a study on figuring out the environmental conditions from sensor data.