DocumentCode :
3657055
Title :
Detecting changes of transportation-mode by using classification data
Author :
Angel J. Lopez;Daniel Ochoa;Sidharta Gautama
Author_Institution :
Department of Telecomunications and Information Processing, Ghent University, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
2078
Lastpage :
2083
Abstract :
Several techniques aim to classify human activity using data from sensors e.g., GPS, accelerometer, Wi-Fi and GSM. The sensor data allow inferring transportation modes as car, bus, walk, and bike. Despite some techniques show improvements in accuracy, researchers constantly deal with issues such as over-segmentation and low precision in trip reporting. Journeys are over-segmented due to the ambiguous situations, for instance: traffic lights, traffic jam, bus stops and weak signal reception. Thereby, current techniques report high misclassification errors. We present a method for detecting changes of transportation mode on a multimodal journey, where the input data regard to the classification of human activities. We use a space transformation for extracting features that identify a transition between two transportation modes. The data are collected from the Google API for Human Activity Classification through a crowdsourcing-based application for smartphones. Results show improvements on precision and accuracy in comparison to initial classification data outcomes. Therefore, our approach reduces the over-segmentation for multimodal journeys.
Keywords :
"Transportation","Accuracy","Legged locomotion","Global Positioning System","Accelerometers","Sensors","Feature extraction"
Publisher :
ieee
Conference_Titel :
Information Fusion (Fusion), 2015 18th International Conference on
Type :
conf
Filename :
7266810
Link To Document :
بازگشت