Van Laerhoven, Kristof
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Van Laerhoven, Kristof
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Publication Access status: Open Access , A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensorsResearch into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-thewild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels. Our study illustrates that different labeling methodologies directly impact the annotations' quality, as well as the capabilities of a deep learning classifier trained with the data. We noticed that in situ methods produce less but more precise labels than recall methods. Furthermore, we combined an activity diary with a visualization tool that enables the participant to inspect and label their activity data. Due to the introduction of such a tool were able to decrease missing annotations and increase the annotation consistency, and therefore the F1-Score of the deep learning model by up to 8% (ranging between 82.1 and 90.4 % F1-Score). Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they could introduce, and the consequences of their usage on human activity recognition studies as well as possible solutions.Source Type:Article2 9 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , IBSync: Intra-body synchronization and implicit contextualization of wearable devices using artificial ECG landmarksWith a smaller form factor and a larger set of applications, body-worn devices have evolved into a collection of simultaneously deployed hardware units, rather than into a single all-round wearable. The sensor data, logged by such devices across the user's body, contains a wealth of information but is often difficult to synchronize. Especially the application of machine learning techniques, e.g., for activity recognition, suffers from the inaccuracy of the devices' internal clocks. In recent years, intra-body communication emerged as a promising alternative to the traditional wired and wireless communication techniques. Distributed wearable systems will notably benefit from its advantages, such as a superior energy efficiency. However, due to the absence of commercially available platforms, applications using this innovative technique remain rare and underinvestigated. With IBSync, we present a novel concept in which artificial landmark signals are received by body-worn devices on touching, approaching, or passing certain areas, surfaces, or objects with embedded transmitter beacons. The landmark signals enable both the wearables' intentional or incidental synchronization as well as the implicit contextualization using supplementary information about the beacons' situational context. For the detection of the landmarks, we propose to repurpose the on-board ECG sensor front-end available in recent commercial wearable devices. Evaluated on a total of 215 min of recordings from two devices, we demonstrate the concept's feasibility and a promising synchronization error of 0.80±1.79 samples or 6.25±14.00 ms at a device's sampling rate of 128 Hz.Source Type:Article3 7
