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Open AccessArticle

Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach

Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg, Germany
Embedded Systems Group, Computer Science Institute, University of Freiburg, 79110 Freiburg, Germany
Ubiquitous Computing Group, Faculty of Science and Technology, University of Siegen, 57076 Siegen, Germany
Author to whom correspondence should be addressed.
This article is an expanded version of the original conference paper [1] and includes a new, in-depth review of related work, as well as additional classification results for some supervised methods on the same datasets and a performance comparison of all methods.
Informatics 2018, 5(2), 16;
Received: 28 February 2018 / Revised: 23 March 2018 / Accepted: 26 March 2018 / Published: 29 March 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user’s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods. View Full-Text
Keywords: human activity recognition; authoring; guidance; manual workflows; laboratory processes human activity recognition; authoring; guidance; manual workflows; laboratory processes
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MDPI and ACS Style

Böttcher, S.; Scholl, P.M.; Van Laerhoven, K. Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach. Informatics 2018, 5, 16.

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