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MLSurf: Surfer Motion Characterization Using Machine Learning Techniques
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Karakterisering av surfmanövrer med maskininlärning (Swedish)
Abstract [en]

Wave surfing is a popular sport that requires minimal financial investment, while it can still be enjoyable from the very first attempt. At the same time, the demand for smart devices that enhance the experience of doing sports by analyzing and evaluating the activities is growing. For surf sport, there are some solutions that are able to collect statistics about activities being done during a surf session, but none of them is able to recognize specific maneuvers that are performed during wave riding.

The goal of this Master Thesis is to improve a currently existing surf activity monitoring solution by extending it with the ability to identify the two most common surf maneuvers during a wave riding session, namely cutback and snap. The solution is using the user’s smartphone to collect IMU sensor data and feed it to a classification pipeline.

The implemented algorithm takes raw sensor data as an input, performs various preprocessing steps, splits the input stream into segments, extracts features from these segments and feed them into a hierarchical classification tree. The implemented pipeline is able to classify non-maneuver, cutback and snap segments with 78% accuracy on a self-collected dataset.

Abstract [sv]

Vågsurfing är en populär sport som kräver minimala finansiella investeringar, medan det kan vara roligt från första början. Samtidigt växer efterfrågan på smarta enheter som förbättrar sportupplevelsen genom att analysera och utvärdera aktiviteterna. För surfsport finns det några lösningar som kan samla in data om aktiviteter som utförs under en surfsession, men ingen av dem kan känna igen specifika manövrar som utförs under vågsurfing.

Målet med denna uppsats är att förbättra en befintlig lösning för surfaktivitetsövervakning genom att utöka den med förmågan att identifiera de två vanligaste surfmanövren nämligen cutback och snap. Lösningen använder användarens smartphone för att samla IMU-sensordata och mata dem till en klassificeringspipeline.

Den implementerade algoritmen tar råa sensordata som input, utför olika förbehandlingssteg, segmenterar input-strömmen, extraherar funktioner från dessa segment och matar in dem i ett hierarkiskt klassificeringsträd. Den implementerade pipeline kan klassificera non-maneuver-, cutback- och snapsegment med 78% noggrannhet på ett självsamlat dataset.

Place, publisher, year, edition, pages
2019. , p. 75
Series
TRITA-EECS-EX ; 2019:642
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264921OAI: oai:DiVA.org:kth-264921DiVA, id: diva2:1375911
External cooperation
Fraunhofer AICOS Porto, Portugal
Supervisors
Examiners
Available from: 2020-01-17 Created: 2019-12-06 Last updated: 2020-01-17Bibliographically approved

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