Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models

Journal article
(Online publication)

Publication Details

Author(s): Martindale C, Hönig FT, Strohrmann C, Eskofier B
Journal: Sensors
Publication year: 2017
Volume: 17
Journal issue: 10
ISSN: 1424-8220
Language: English


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.

FAU Authors / FAU Editors

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Hönig, Florian Thomas
Professur für Informatik (Mustererkennung)
Martindale, Christine
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)

How to cite

Martindale, C., Hönig, F.T., Strohrmann, C., & Eskofier, B. (2017). Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models. Sensors, 17(10).

Martindale, Christine, et al. "Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models." Sensors 17.10 (2017).


Last updated on 2019-21-07 at 07:28