TY - GEN
T1 - A wearable, self-calibrating, wireless sensor network for body motion processing
AU - Kwang, Yong Lim
AU - Goh, Francis Young Koon
AU - Dong, Wei
AU - Kim, Doang Nguyen
AU - Chen, I. Ming
AU - Song, Huat Yeo
AU - Duh, Henry Been Lirn
AU - Chung, Gon Kim
PY - 2008
Y1 - 2008
N2 - A novel self-calibrating sensing technology using miniature linear encoders and Inertial/magnetic Measurement Unit (IMU) provides the accuracy, fast response and robustness required by many body motion processing applications. Our sensor unit consists of an accelerometer, a 3-axis magnetic sensor, 2 gyroscopes and a miniature linear encoder. The fusion of data from the sensors is accomplished by extracting the gravity related term from the accelerometer and consistently calibrating the gyroscopes and linear encoder when the sensor unit is under static conditions. Using the fused sensors, we developed a complete motion processing system that consists of a gateway where the human kinematics modeling is embedded. A time divided multiple access wireless architecture is adopted to synchronize the sensor network at 100Hz. Experimental results show that the combination of the IMU and linear encoder produces a low RMS error of 3.5° and correlation coefficient of 99.01%. A video showing the capture a performer's upper body motion is also realized.
AB - A novel self-calibrating sensing technology using miniature linear encoders and Inertial/magnetic Measurement Unit (IMU) provides the accuracy, fast response and robustness required by many body motion processing applications. Our sensor unit consists of an accelerometer, a 3-axis magnetic sensor, 2 gyroscopes and a miniature linear encoder. The fusion of data from the sensors is accomplished by extracting the gravity related term from the accelerometer and consistently calibrating the gyroscopes and linear encoder when the sensor unit is under static conditions. Using the fused sensors, we developed a complete motion processing system that consists of a gateway where the human kinematics modeling is embedded. A time divided multiple access wireless architecture is adopted to synchronize the sensor network at 100Hz. Experimental results show that the combination of the IMU and linear encoder produces a low RMS error of 3.5° and correlation coefficient of 99.01%. A video showing the capture a performer's upper body motion is also realized.
UR - http://www.scopus.com/inward/record.url?scp=51649083340&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2008.4543338
DO - 10.1109/ROBOT.2008.4543338
M3 - Conference article published in proceeding or book
AN - SCOPUS:51649083340
SN - 9781424416479
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1017
EP - 1022
BT - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008
T2 - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Y2 - 19 May 2008 through 23 May 2008
ER -