Applying Machine Learning for Sensor Data Analysis in Interactive Systems: Common Pitfalls of Pragmatic Use and Ways to Avoid Them

With the widespread proliferation of (miniaturized) sensing facilities and the massive growth and popularity of the field of machine learning (ML) research, new frontiers in automated sensor data analysis have been explored that lead to paradigm shifts in many application domains. In fact, many practitioners now employ and rely more and more on ML methods as integral part of their sensor data analysis workflows—thereby not necessarily being ML experts or having an interest in becoming one. The availability of toolkits that can readily be used by practitioners has led to immense popularity and widespread adoption and, in essence, pragmatic use of ML methods. ML having become mainstream helps pushing the core agenda of practitioners, yet it comes with the danger of misusing methods and as such running the risk of leading to misguiding if not flawed results.

Based on years of observations in the ubiquitous and interactive computing domain that extensively relies on sensors and automated sensor data analysis, and on having taught and worked with numerous students in the field, in this article I advocate a considerate use of ML methods by practitioners, i.e., non-ML experts, and elaborate on pitfalls of an overly pragmatic use of ML techniques. The article not only identifies and illustrates the most common issues, it also offers ways and practical guidelines to avoid these, which shall help practitioners to benefit from employing ML in their core research domains and applications.