TY - GEN
T1 - Utilization of Postural Transitions in Sensor-based Human Activity Recognition
AU - Thu, Nguyen Thi Hoai
AU - Han, Dong Seog
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Sensor-based human activity recognition (HAR) has gained tremendous attention due to numerous applications that aim to monitor the movement and behaviour of humans. However, the occurrence of transitions between activities gives rise to many problems in HAR as they can affect the performance of the recognition system by causing fluctuations in the prediction. This paper proposes an HAR system using groups of similar postural transitions, discrete wavelet transform (DWT) and bidirectional long short-term memory (BiLSTM) to deal with the postural transitions, thereby improving the accuracy of the system. In this system, the transitions which have similar patterns are grouped into same groups, after that the essential features are extracted by using DWT before being fed into the BiLSTM network for activity classification task. Our experiment results indicate that the proposed model achieves competitive performance compared to a non-transition model.
AB - Sensor-based human activity recognition (HAR) has gained tremendous attention due to numerous applications that aim to monitor the movement and behaviour of humans. However, the occurrence of transitions between activities gives rise to many problems in HAR as they can affect the performance of the recognition system by causing fluctuations in the prediction. This paper proposes an HAR system using groups of similar postural transitions, discrete wavelet transform (DWT) and bidirectional long short-term memory (BiLSTM) to deal with the postural transitions, thereby improving the accuracy of the system. In this system, the transitions which have similar patterns are grouped into same groups, after that the essential features are extracted by using DWT before being fed into the BiLSTM network for activity classification task. Our experiment results indicate that the proposed model achieves competitive performance compared to a non-transition model.
KW - Human activity recognition
KW - bidirectional long short-term memory
KW - discrete wavelet transform
KW - transitions
UR - http://www.scopus.com/inward/record.url?scp=85084037569&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC48513.2020.9065250
DO - 10.1109/ICAIIC48513.2020.9065250
M3 - Conference contribution
AN - SCOPUS:85084037569
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 177
EP - 181
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -