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Resource efficient travel mode recognition
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Resurseffektiv transportlägesigenkänning (Swedish)
Abstract [en]

In this report we attempt to provide insights to how a resource efficient solution for transportation mode recognition can be implemented on a smartphone using the accelerometer and magnetometer as sensors for data collection. The proposed system uses a hierarchical classification process where instances are first classified as vehicles or non-vehicles, then as wheel or rail vehicles, and lastly as belonging to one of the transportation modes: bus, car, motorcycle, subway, or train. A virtual gyroscope is implemented as a low-power source of simulated gyroscope data. Features are extracted from the accelerometer, magnetometer and virtual gyroscope readings that are sampled at 30 Hz, before they are classified using machine learning algorithms from the WEKA machine learning library.

An Android application was developed to classify real-time data, and the resource consumption of the application was measured using the Trepn profiler application. The proposed system achieves an overall accuracy of 82.7% and a vehicular accuracy of 84.9% using a 5 second window with 75% overlap while having an average power consumption of 8.5 mW. 

Abstract [sv]

I denna rapport försöker vi ge insikter om hur en resurseffektiv lösning för transportlägesigenkänning kan implementeras på en smartphone genom att använda accelerometern och magnetometern som sensorer för datainsamling. Det föreslagna systemet använder en hierarkisk klassificeringsprocess där instanser först klassificeras som fordon eller icke-fordon, sedan som hjul- eller järnvägsfordon, och slutligen som tillhörande ett av transportsätten: buss, bil, motorcykel, tunnelbana eller tåg. Ett virtuellt gyroskop implementeras som en lågenergi källa till simulerad gyroskopdata. Olika särdrag extraheras från accelerometer, magnetometer och virtuella gyroskopläsningar som samlas in vid 30 Hz, innan de klassificeras med hjälp av maskininlärningsalgoritmer från WEKA-maskinlärningsbiblioteket. En Android-applikation har utvecklats för att klassificera realtidsdata, och programmets resursförbrukning mättes med hjälp av Trepn profiler-applikationen. Det föreslagna systemet uppnår en övergripande noggrannhet av 82.7% och en fordonsnoggrannhet av 84.9% genom att använda ett 5 sekunders fönster med 75% överlappning med en genomsnittlig energiförbrukning av 8.5 mW.

Place, publisher, year, edition, pages
2017. , p. 87
Keywords [en]
transportation mode recognition, hierarchical classification, smartphone sensors
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-217897OAI: oai:DiVA.org:kth-217897DiVA, id: diva2:1158218
External cooperation
Bontouch AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-11-20 Created: 2017-11-18 Last updated: 2018-01-13Bibliographically approved

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