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Sensors 2017, 17(10), 2328; https://doi.org/10.3390/s17102328

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models

1
Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
2
Speech Group, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
3
Bosch Sensortec GmbH, Gerhard-Kindler-Strasse 9, 72770 Reutlingen, Germany
*
Author to whom correspondence should be addressed.
Received: 6 September 2017 / Revised: 28 September 2017 / Accepted: 11 October 2017 / Published: 13 October 2017
(This article belongs to the Special Issue Annotation of User Data for Sensor-Based Systems)
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Abstract

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, ‘in the wild’ data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique. View Full-Text
Keywords: hierarchical hidden Markov models; segmentation; smart annotation; cyclic sensor data; semi-supervised learning; annotation cost; activity recognition; gait classification; inertial sensors; wearable sensors hierarchical hidden Markov models; segmentation; smart annotation; cyclic sensor data; semi-supervised learning; annotation cost; activity recognition; gait classification; inertial sensors; wearable sensors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Martindale, C.F.; Hoenig, F.; Strohrmann, C.; Eskofier, B.M. Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models. Sensors 2017, 17, 2328.

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