A Calibration-Free Head Gesture Recognition System with Online Capability

NC Wöhler; U Grossekathöfer; A Dierker; Marc Hanheide; S Kopp; T Hermann

doi:10.1109/ICPR.2010.929

A Calibration-Free Head Gesture Recognition System with Online Capability

NC Wöhler, U Grossekathöfer, A Dierker, Marc Hanheide, S Kopp, T Hermann

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification, since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification rates of 85-95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show first promising attempts towards online recognition.

Original language	English
Title of host publication	2010 20th International Conference on Pattern Recognition (ICPR)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3814-3817
Number of pages	4
ISBN (Print)	978-1-4244-7542-1
DOIs	https://doi.org/10.1109/ICPR.2010.929
Publication status	Published - 26 Aug 2010
Event	International Conference on Pattern Recognition (ICPR 2010), 20th - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010

Conference

Conference	International Conference on Pattern Recognition (ICPR 2010), 20th
Country/Territory	Turkey
City	Istanbul
Period	23/08/10 → 26/08/10

Access to Document

10.1109/ICPR.2010.929

Cite this

@inproceedings{4b4eea9bf13f4f4f8665ad709f1b8cff,

title = "A Calibration-Free Head Gesture Recognition System with Online Capability",

abstract = "In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification, since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification rates of 85-95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show first promising attempts towards online recognition.",

author = "NC W{\"o}hler and U Grossekath{\"o}fer and A Dierker and Marc Hanheide and S Kopp and T Hermann",

year = "2010",

month = aug,

day = "26",

doi = "10.1109/ICPR.2010.929",

language = "English",

isbn = "978-1-4244-7542-1",

pages = "3814--3817",

booktitle = "2010 20th International Conference on Pattern Recognition (ICPR)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "International Conference on Pattern Recognition (ICPR 2010), 20th ; Conference date: 23-08-2010 Through 26-08-2010",

}

Wöhler, NC, Grossekathöfer, U, Dierker, A, Hanheide, M, Kopp, S & Hermann, T 2010, A Calibration-Free Head Gesture Recognition System with Online Capability. in 2010 20th International Conference on Pattern Recognition (ICPR) . Institute of Electrical and Electronics Engineers (IEEE), pp. 3814-3817, International Conference on Pattern Recognition (ICPR 2010), 20th, Istanbul, Turkey, 23/08/10. https://doi.org/10.1109/ICPR.2010.929

TY - GEN

T1 - A Calibration-Free Head Gesture Recognition System with Online Capability

AU - Wöhler, NC

AU - Grossekathöfer, U

AU - Dierker, A

AU - Hanheide, Marc

AU - Kopp, S

AU - Hermann, T

PY - 2010/8/26

Y1 - 2010/8/26

N2 - In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification, since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification rates of 85-95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show first promising attempts towards online recognition.

AB - In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification, since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification rates of 85-95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show first promising attempts towards online recognition.

U2 - 10.1109/ICPR.2010.929

DO - 10.1109/ICPR.2010.929

M3 - Conference contribution

SN - 978-1-4244-7542-1

SP - 3814

EP - 3817

BT - 2010 20th International Conference on Pattern Recognition (ICPR)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - International Conference on Pattern Recognition (ICPR 2010), 20th

Y2 - 23 August 2010 through 26 August 2010

ER -

A Calibration-Free Head Gesture Recognition System with Online Capability

Abstract

Conference

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