Discriminative Temporal Smoothing for Activity Recognition from Wearable Sensors

Abstract. This paper describes daily life activity recognition using wearable acceleration sensors attached to four different parts of the human body. The experimental data set consisted of signals recorded from 13 different subjects performing 17 daily activities. Furthermore, to attain more general activities, some of the most specific classes were combined for a total of 9 different activities. Simple time domain features were calculated from each sensor device. For the recognition task, we propose a novel sequential learning method that combines discriminative learning of individual input-output mappings using support vector machines (SVM) with generative learning to smooth temporal time-dependent activity sequences with a trained hidden Markov model (HMM) type transition probability matrix. The experiments show that the accuracy of the proposed method is superior to various conventional discriminative and generative methods alone, and it achieved a total recognition rate of 94% a...

• Type: Conference
• Year: 2007
• Where: UCS
• Authors: Jaakko Suutala, Susanna Pirttikangas, Juha Röning
• Keywords: Applied Computing, Combines Discriminative Learning, Daily Activities, Life Activity Recognition, UCS 2007