title: Detection of activities by wireless sensors for daily life surveillance: eating and drinking.
abstract: This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody's (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb's three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.
authors.affiliation: Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576.
authors.forename: Sen
authors.initials: S
authors.lastname: Zhang
authors.forename: Marcelo H
authors.initials: MH
authors.lastname: Ang
authors.forename: Wendong
authors.initials: W
authors.lastname: Xiao
authors.forename: Chen Khong
authors.initials: CK
authors.lastname: Tham