A feature combination approach for the detection of early morning bathroom activities with wireless sensors
Nuri F. Ince, Cheol-Hong Min, Ahmed H. Tewfik · 2007
Abstract—In this paper we investigate the use of wearable accelerometers and wireless home sensors for the detection of early morning daily activities. In particular we focus on the detection of brushing, washing face and shaving activities by using a wireless accelerometer sensor attached to the right wrist of subjects to collect the hand movement data. First the system localizes the subject in the bathroom with a wireless home motion sensor. This is followed by extraction of time and frequency domain features of accelerometer data. These features are input to an ensemble of Gaussian mixture models which represent each activity we focus on and then post processed by a finite state machine for classification. We show promising experimental results from 7 subjects while completing washing face, shaving and brushing activities. The proposed system achieved 93.5%, 92.5 % and 95.6% classification accuracy in the recognition of these three tasks respectively. T I.