Hands-free gesture control with a capacitive textile neckband. Hirsch, M., Cheng, J., Reiss, A., Sundholm, M., Lukowicz, P., & Amft, O. In ISWC 2014: Proceedings of 18th International Symposium on Wearable Computers, pages 55--58, 2014. ACM.
doi  abstract   bibtex   
We present a novel sensing modality for hands-free gesture controlled user interfaces, based on active capacitive sensing. Four capacitive electrodes are integrated into a textile neckband, allowing continuous unobtrusive head movement monitoring. We explore the capability of the proposed system for recognising head gestures and postures. A study involving 12 subjects was carried out, recording data from 15 head gestures and 19 different postures. We present a quantitative evaluation based on this dataset, achieving an overall accuracy of 79.1% for head gesture recognition and 40.4% for distinguishing between head postures (69.9% when merging the most adjacent positions), respectively. These results indicate that our approach is promising for hands-free control interfaces. An example application scenario of this technology is the control of an electric wheelchair for people with motor impairments, where recognised gestures or postures can be mapped to control commands.
@InProceedings{Hirsch2014-P_ISWC,
  Title                    = {Hands-free gesture control with a capacitive textile neckband},
  Author                   = {Marco Hirsch and Jingyuan Cheng and Attila Reiss and Mathias Sundholm and Paul Lukowicz and Oliver Amft},
  Booktitle                = {ISWC 2014: Proceedings of 18th International Symposium on Wearable Computers},
  Year                     = {2014},
  Pages                    = {55--58},
  Publisher                = {ACM},

  Abstract                 = {We present a novel sensing modality for hands-free gesture controlled user interfaces, based on active capacitive sensing. Four capacitive electrodes are integrated into a textile neckband, allowing continuous unobtrusive head movement monitoring. We explore the capability of the proposed system for recognising head gestures and postures. A study involving 12 subjects was carried out, recording data from 15 head gestures and 19 different postures. We present a quantitative evaluation based on this dataset, achieving an overall accuracy of 79.1\% for head gesture recognition and 40.4\% for distinguishing between head postures (69.9\% when merging the most adjacent positions), respectively. These results indicate that our approach is promising for hands-free control interfaces. An example application scenario of this technology is the control of an electric wheelchair for people with motor impairments, where recognised gestures or postures can be mapped to control commands.},
  Doi                      = {10.1145/2634317.2634328},
  File                     = {Hirsch2014-P_ISWC.pdf:Hirsch2014-P_ISWC.pdf:PDF},
  Owner                    = {oamft},
  Timestamp                = {2014/07/02}
}

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