TY - GEN
T1 - A recurrent neural network with non-gesture rejection model for recognizing gestures with smartphone sensors
AU - Lee, Myeong Chun
AU - Cho, Sung Bae
PY - 2013
Y1 - 2013
N2 - Gesture recognition provides a new interface to user. Various methods for the gesture recognition are feasible in smartphone environment since a number of sensors attached are gradually increasing. In this paper, we propose a gesture recognition method using smartphone accelerometer sensors. The high false-positive rate is definite if the gesture sequence data are increased. We have modified BLSTM (Bidirectional Long Short-Term Memory) recurrent neural network with non-gesture rejection model to deal with the problem. A BLSTM model classifies the input into the gesture and non-gesture classes, and the specific BLSTM models for the gestures further classify it into one of twenty gestures. 24,850 sequence data are used for the experiment, and it consists of 11,885 gesture sequences and 12,965 non-gesture sequences. The proposed method shows higher accuracy than the standard BLSTM.
AB - Gesture recognition provides a new interface to user. Various methods for the gesture recognition are feasible in smartphone environment since a number of sensors attached are gradually increasing. In this paper, we propose a gesture recognition method using smartphone accelerometer sensors. The high false-positive rate is definite if the gesture sequence data are increased. We have modified BLSTM (Bidirectional Long Short-Term Memory) recurrent neural network with non-gesture rejection model to deal with the problem. A BLSTM model classifies the input into the gesture and non-gesture classes, and the specific BLSTM models for the gestures further classify it into one of twenty gestures. 24,850 sequence data are used for the experiment, and it consists of 11,885 gesture sequences and 12,965 non-gesture sequences. The proposed method shows higher accuracy than the standard BLSTM.
UR - http://www.scopus.com/inward/record.url?scp=84893371556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893371556&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-45062-4_4
DO - 10.1007/978-3-642-45062-4_4
M3 - Conference contribution
AN - SCOPUS:84893371556
SN - 9783642450617
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 46
BT - Pattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings
T2 - 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013
Y2 - 10 December 2013 through 14 December 2013
ER -