TY - JOUR
T1 - Sign language translation system based on micro-inertial measurement units and ZigBee network
AU - Liu, Lianqing
AU - Iwata, Hiroyasu
AU - Shi, Guangyi
AU - li, Zhi
AU - tu, Keke
AU - Jia, Songtao
AU - Cui, Qinghu
AU - Jin, Yufeng
N1 - Funding Information:
The work was partially performed at the Peking University School of Software and Microelectronics Engineering Wuxi Campus. This paper is partially sponsored by Natural Science Foundation of Jiangsu Province SKB201021611, Shenzhen JC200903160368A and National 863 Program 2009AA04Z311.
PY - 2013/10
Y1 - 2013/10
N2 - Chinese sign language has been proved as an effective communication tool for deaf people. In this paper, we present a novel translation system, which can capture human gestures through micro-inertial measurement units (IMUs) and translate the gestures into specific meanings accordingly. Each micro-IMU consists of a 3D accelerometer and gyroscope. A micro-controller and ZigBee network were used to acquire data simultaneously and wirelessly. Ten types of basic Chinese sign language movements including what, how, work, today, happy, please, book, body, clothes and and were collected and stored to form a motion-sensing database. A discrete cosine transform (DCT) was performed to extract the effective features from the original data, while a hidden Markov model (HMM) was used to train the database in order to form an HMM classifier. Testing samples were used to test the HMM classifier. Different sign languages were recognized through the HMM classifier and subsequent translation processes were performed. Experimental results showed that the correct recognition rate ranges from 95% to 100% for the 10 sign language movements, and the overall correction rate is 98%. With more micro-electro-mechanical system (MEMS) sensing motes adding to the interpretation system, the performance will be enhanced.
AB - Chinese sign language has been proved as an effective communication tool for deaf people. In this paper, we present a novel translation system, which can capture human gestures through micro-inertial measurement units (IMUs) and translate the gestures into specific meanings accordingly. Each micro-IMU consists of a 3D accelerometer and gyroscope. A micro-controller and ZigBee network were used to acquire data simultaneously and wirelessly. Ten types of basic Chinese sign language movements including what, how, work, today, happy, please, book, body, clothes and and were collected and stored to form a motion-sensing database. A discrete cosine transform (DCT) was performed to extract the effective features from the original data, while a hidden Markov model (HMM) was used to train the database in order to form an HMM classifier. Testing samples were used to test the HMM classifier. Different sign languages were recognized through the HMM classifier and subsequent translation processes were performed. Experimental results showed that the correct recognition rate ranges from 95% to 100% for the 10 sign language movements, and the overall correction rate is 98%. With more micro-electro-mechanical system (MEMS) sensing motes adding to the interpretation system, the performance will be enhanced.
KW - HMM
KW - MEMS
KW - ZigBee network
KW - sign language translation
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U2 - 10.1177/0142331212470962
DO - 10.1177/0142331212470962
M3 - Article
AN - SCOPUS:84884175150
SN - 0142-3312
VL - 35
SP - 901
EP - 909
JO - Transactions of the Institute of Measurement and Control
JF - Transactions of the Institute of Measurement and Control
IS - 7
ER -