PMID- 31536027
OWN - NLM
STAT- MEDLINE
DCOM- 20210423
LR  - 20221207
IS  - 2168-2208 (Electronic)
IS  - 2168-2194 (Linking)
VI  - 24
IP  - 5
DP  - 2020 May
TI  - Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on 
      Deep Belief Net.
PG  - 1310-1320
LID - 10.1109/JBHI.2019.2941535 [doi]
AB  - In this paper, deep belief net (DBN) was applied into the field of 
      wearable-sensor based Chinese sign language (CSL) recognition. Eight subjects 
      were involved in the study, and all of the subjects finished a five-day 
      experiment performing CSL on a target word set consisting of 150 CSL subwords. 
      During the experiment, surface electromyography (sEMG), accelerometer (ACC), and 
      gyroscope (GYRO) signals were collected from the participants. In order to obtain 
      the optimal structure of the network, three different sensor fusion strategies, 
      including data-level fusion, feature-level fusion, and decision-level fusion, 
      were explored. In addition, for the feature-level fusion strategy, two different 
      feature sources, which are hand-crafted features and network generated features, 
      and two different network structures, which are fully-connected net and DBN, were 
      also compared. The result showed that feature level fusion could achieve the best 
      recognition accuracy among the three fusion strategies, and feature-level fusion 
      with network generated features and DBN could achieve the best recognition 
      accuracy. The best recognition accuracy realized in this study was 95.1% for the 
      user-dependent test and 88.2% for the user-independent test. The significance of 
      the study is that it applied the deep learning method into the field of wearable 
      sensors-based CSL recognition, and according to our knowledge it's the first 
      study comparing human engineered features with the network generated features in 
      the correspondent field. The results from the study shed lights on the method of 
      using network-generated features during sensor fusion and CSL recognition.
FAU - Yu, Yi
AU  - Yu Y
FAU - Chen, Xiang
AU  - Chen X
FAU - Cao, Shuai
AU  - Cao S
FAU - Zhang, Xu
AU  - Zhang X
FAU - Chen, Xun
AU  - Chen X
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190916
PL  - United States
TA  - IEEE J Biomed Health Inform
JT  - IEEE journal of biomedical and health informatics
JID - 101604520
SB  - IM
MH  - Accelerometry/instrumentation/methods
MH  - Adult
MH  - Asian People
MH  - China
MH  - *Deep Learning
MH  - Electromyography/instrumentation
MH  - Forearm/physiology
MH  - Gestures
MH  - Humans
MH  - Male
MH  - Pattern Recognition, Automated/*methods
MH  - *Sign Language
MH  - *Signal Processing, Computer-Assisted
MH  - *Wearable Electronic Devices
MH  - Young Adult
EDAT- 2019/09/20 06:00
MHDA- 2021/04/24 06:00
CRDT- 2019/09/20 06:00
PHST- 2019/09/20 06:00 [pubmed]
PHST- 2021/04/24 06:00 [medline]
PHST- 2019/09/20 06:00 [entrez]
AID - 10.1109/JBHI.2019.2941535 [doi]
PST - ppublish
SO  - IEEE J Biomed Health Inform. 2020 May;24(5):1310-1320. doi: 
      10.1109/JBHI.2019.2941535. Epub 2019 Sep 16.