Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
This addresses the issue of data quality degradation for researchers and practitioners using wearable wristbands to monitor emotional responses, but it is incremental as it builds on prior supervised detection work.
The paper tackled the problem of detecting motion artifacts in wrist-measured electrodermal activity data, finding that unsupervised learning algorithms perform competitively with supervised methods in both lab and real-world settings, with about 23 hours of data.
One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data. We also find, somewhat surprisingly, that incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms.