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.
| Original language | English |
|---|---|
| Title of host publication | MUM 2004 |
| Subtitle of host publication | Proceedings of the 3rd International Conference on Mobile and Ubiquitous Multimedia |
| Pages | 25-31 |
| DOIs | |
| Publication status | Published - 2004 |
| MoE publication type | A4 Article in a conference publication |
| Event | 3rd International Conference on Mobile and Ubiquitous Multimedia, MUM2004 - College Park, United States Duration: 27 Oct 2004 → 29 Oct 2004 |
Conference
| Conference | 3rd International Conference on Mobile and Ubiquitous Multimedia, MUM2004 |
|---|---|
| Country/Territory | United States |
| City | College Park |
| Period | 27/10/04 → 29/10/04 |