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Summary: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 18, NO. 4, AUGUST 2010 1
Multimodal Physical Activity Recognition by
Fusing Temporal and Cepstral Information
Ming Li, Student Member, IEEE, Viktor Rozgi´c Student Member, IEEE, Gautam Thatte, Student Member, IEEE,
Sangwon Lee, Adar Emken, Murali Annavaram, Urbashi Mitra, Fellow, IEEE, Donna Spruijt-Metz,
Shrikanth Narayanan, Fellow, IEEE
Abstract--A physical activity (PA) recognition algorithm for
a wearable wireless sensor network using both ambulatory
electrocardiogram (ECG) and accelerometer signals is proposed.
First, in the time domain, the cardiac activity mean and the
motion artifact noise of the ECG signal are modeled by a
Hermite polynomial expansion and principal component analysis,
respectively. A set of time domain accelerometer features is
also extracted. A support vector machine (SVM) is employed
for supervised classification using these time domain features.
Second, motivated by their potential for handling convolutional
noise, cepstral features extracted from ECG and accelerometer
signals based on a frame level analysis are modeled using
Gaussian mixture models (GMM). Third, to reduce the di-
mension of the tri-axial accelerometer cepstral features which
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