TY - JOUR
T1 - Dynamic detection of window starting positions and its implementation within an activity recognition framework
AU - Ni, Qin
AU - Patterson, Timothy
AU - Cleland, Ian
AU - Nugent, Chris
PY - 2016/7/5
Y1 - 2016/7/5
N2 - Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition.
AB - Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition.
KW - Data segmentation
KW - Activities of Daily Living (ADLs)
KW - Change detection
KW - Feature selection
KW - Activity recognition
UR - https://pure.ulster.ac.uk/en/publications/dynamic-detection-of-window-starting-positions-and-its-implementa-3
U2 - 10.1016/j.jbi.2016.07.005
DO - 10.1016/j.jbi.2016.07.005
M3 - Article
SN - 1532-0480
VL - 62
SP - 171
EP - 180
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
ER -