Abstract
Accelerometer-based gesture control is proposed as a complementary
interaction modality for small handheld devices to enable a variety of
multimedia applications. The motivation for experimenting with gesture
interaction is justified by the personal and public domain prototype
applications developed. The challenges related to developing
user-dependent and independent gesture control are presented. In this
article, we experiment with methods for user-dependent gesture
recognition with a low number of training repetitions, and for feasible
user-independent gesture recognition from a moderately large set of
gestures. The user-dependent gesture recognition performance of the
continuous Hidden Markov Model (HMM) is better when compared to discrete
HMM with three gesture repetitions in a training set. With continuous
HMM, a recognition accuracy level of 95% is obtained with or without
tilt normalization, while for discrete HMM a best recognition accuracy
of 90% is obtained. The user-independent gesture recognition performance
with continuous HMM of 89% is considerably better compared to tests
with discrete HMM, when both are obtained with cross-validation from
2,520 gestures. An important result is that the effect of using tilt
normalization notably increases the user-independent gesture recognition
performance by 10- 15% depending on the method used. The chosen methods
show great potential for gesture-based interaction in multimedia
applications.
Original language | English |
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Pages (from-to) | 92 - 112 |
Number of pages | 21 |
Journal | Journal of Mobile Multimedia |
Volume | 1 |
Issue number | 2 |
Publication status | Published - 2005 |
MoE publication type | A1 Journal article-refereed |