The impact of data aggregation: Advocating for individualized analysis in wearable sensor research

Abstract

The use of wearable sensors to record physiological data is becoming increasingly common, both in research and in daily life. User studies collect data using wrist bands, chest straps, and headbands to measure heart rate, skin conductance, and brain activity, to name a few. These readings can then be used to classify a range of physical and cognitive functions, such as cognitive and physical workload, cognitive and physical fatigue, stress, and attention. However, while physiological data is highly individualized, many researchers use machine learning classification methods that combine the participants' data into one dataset. In this paper, we demonstrate the negative impact this has on results and show that the individualized nature of physiological data requires individualized analysis and classification.