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Research into the detection of human activities from wearable sensors is ahighly active field, benefiting numerous applications, from ambulatorymonitoring of healthcare patients via fitness coaching to streamlining manualwork processes. We present an empirical study that compares 4 differentcommonly used annotation methods utilized in user studies that focus onin-the-wild data. These methods can be grouped in user-driven, in situannotations - which are performed before or during the activity is recorded -and recall methods - where participants annotate their data in hindsight at theend of the day. Our study illustrates that different labeling methodologiesdirectly impact the annotations' quality, as well as the capabilities of a deeplearning classifier trained with the data respectively. We noticed that in situmethods produce less but more precise labels than recall methods. Furthermore,we combined an activity diary with a visualization tool that enables theparticipant to inspect and label their activity data. Due to the introductionof such a tool were able to decrease missing annotations and increase theannotation consistency, and therefore the F1-score of the deep learning modelby up to 8% (ranging between 82.1 and 90.4% F1-score). Furthermore, we discussthe advantages and disadvantages of the methods compared in our study, thebiases they may could introduce and the consequences of their usage on humanactivity recognition studies and as well as possible solutions.

A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors