Automated classification of pre-defined movement patterns: A comparison
between GNSS and UWB technology
- URL: http://arxiv.org/abs/2303.07107v1
- Date: Fri, 10 Mar 2023 14:46:42 GMT
- Title: Automated classification of pre-defined movement patterns: A comparison
between GNSS and UWB technology
- Authors: Rodi Laanen, Maedeh Nasri, Richard van Dijk, Mitra Baratchi, Alexander
Koutamanis and Carolien Rieffe
- Abstract summary: Real-time location systems (RTLS) allow for collecting data from human movement patterns.
The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
- Score: 55.41644538483948
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advanced real-time location systems (RTLS) allow for collecting
spatio-temporal data from human movement behaviours. Tracking individuals in
small areas such as schoolyards or nursing homes might impose difficulties for
RTLS in terms of positioning accuracy. However, to date, few studies have
investigated the performance of different localisation systems regarding the
classification of human movement patterns in small areas. The current study
aims to design and evaluate an automated framework to classify human movement
trajectories obtained from two different RTLS: Global Navigation Satellite
System (GNSS) and Ultra-wideband (UWB), in areas of approximately 100 square
meters. Specifically, we designed a versatile framework which takes GNSS or UWB
data as input, extracts features from these data and classifies them according
to the annotated spatial patterns. The automated framework contains three
choices for applying noise removal: (i) no noise removal, (ii) Savitzky Golay
filter on the raw location data or (iii) Savitzky Golay filter on the extracted
features, as well as three choices regarding the classification algorithm:
Decision Tree (DT), Random Forest (RF) or Support Vector Machine (SVM). We
integrated different stages within the framework with the Sequential
Model-Based Algorithm Configuration (SMAC) to perform automated hyperparameter
optimisation. The best performance is achieved with a pipeline consisting of
noise removal applied to the raw location data with an RF model for the GNSS
and no noise removal with an SVM model for the UWB. We further demonstrate
through statistical analysis that the UWB achieves significantly higher results
than the GNSS in classifying movement patterns.
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