TY - GEN
T1 - Towards multi-classification of human motions using Micro IMU and SVM training process
AU - Shi, Guangyi
AU - Zou, Yuexian
AU - Li, Wen J.
AU - Jin, Yufeng
AU - Guan, Pei
PY - 2009
Y1 - 2009
N2 - This paper introduces a novel approach for human motion recognition via motion feature vectors collected by A Micro Inertial Measurement Unit (μIMU). First, μIMU that is 56×23×15mm3 in size was built. The unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU), which can transmit human motion information through a serial port to a computer. Second, a human motion database was setup by recording the motion data from the μIMU. The motions include fall, walk, stand, run and step upstairs. Third, Support Vector Machine (SVM) training process was used for human motion multi-classification. FFT was used for feature generation and optimal parameter searching process was done for the best SVM kernel function. Experimental results showed that for the given 5 different motions, the total correct recognition rate is 92%, of which the fall motion can be classified from others with 100% recognition rate. © 2009 Trans Tech Publications, Switzerland.
AB - This paper introduces a novel approach for human motion recognition via motion feature vectors collected by A Micro Inertial Measurement Unit (μIMU). First, μIMU that is 56×23×15mm3 in size was built. The unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU), which can transmit human motion information through a serial port to a computer. Second, a human motion database was setup by recording the motion data from the μIMU. The motions include fall, walk, stand, run and step upstairs. Third, Support Vector Machine (SVM) training process was used for human motion multi-classification. FFT was used for feature generation and optimal parameter searching process was done for the best SVM kernel function. Experimental results showed that for the given 5 different motions, the total correct recognition rate is 92%, of which the fall motion can be classified from others with 100% recognition rate. © 2009 Trans Tech Publications, Switzerland.
KW - MEMS
KW - Multi-classification
KW - SVM
KW - UIMU
UR - http://www.scopus.com/inward/record.url?scp=71849091927&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-71849091927&origin=recordpage
U2 - 10.4028/www.scientific.net/AMR.60-61.189
DO - 10.4028/www.scientific.net/AMR.60-61.189
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0878493395
SN - 9780878493395
VL - 60-61
T3 - Advanced Materials Research
SP - 189
EP - 193
BT - Micro and Nano Technology - 1st International Conference Society of Micro/Nano Technology, CSMNT
T2 - Micro and Nano Technology - 1st International Conference Society of Micro/Nano Technology, CSMNT
Y2 - 19 November 2008 through 22 November 2008
ER -