Enabling fast and effortless customisation in accelerometer based gesture interaction

  • Authors:

  • Jani Mäntyjärvi;Juha Kela;Panu Korpipää;Sanna Kallio

  • Affiliations:

  • VTT Electronics, Finland;VTT Electronics, Finland;VTT Electronics, Finland;VTT Electronics, Finland

  • Venue:
  • Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia
  • Year:
  • 2004

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Abstract

Accelerometer based gesture control is proposed as a complementary interaction modality for handheld devices. Predetermined gesture commands or freely trainable by the user can be used for controlling functions also in other devices. To support versatility of gesture commands in various types of personal device applications gestures should be customisable, easy and quick to train. In this paper we experiment with a procedure for training/recognizing customised accelerometer based gestures with minimum amount of user effort in training. Discrete Hidden Markov Models (HMM) are applied. Recognition results are presented for an external device, a DVD player gesture commands. A procedure based on adding noise-distorted signal duplicates to training set is applied and it is shown to increase the recognition accuracy while decreasing user effort in training. For a set of eight gestures, each trained with two original gestures and with two Gaussian noise-distorted duplicates, the average recognition accuracy was 97%, and with two original gestures and with four noise-distorted duplicates, the average recognition accuracy was 98%, cross-validated from a total data set of 240 gestures. Use of procedure facilitates quick and effortless customisation in accelerometer based interaction.