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Klassificering av engagemangsnivå hos en samtalsdeltagare med hjälp av maskininlärning
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Classification of interlocutor engagement using machine learning (English)
Abstract [en]

The work presented in this study is based on the long-term goal of developing a social robot that can be involved in leading a conversation in a language café. In detail, the study has investigated whether it is possible to classify involvement with a conversation participant based on its facial expression and gaze two factors that previous studies have shown to be central to human engagement. To perform the assessment, the software Openface has extracted said parameters from a previous field study which has then been processed with the machine learning model Support Vector Machine. After a lot of hyperparameter tuning, the final model managed to predict engagement on a three-point scale with 54.5% accuracy.

Furthermore, the study has also examined the potential of the new technological paradigm that the social robot represents. The potential has been analyzed on the basis of Dosi’s four dimensions: technological possibilities, appropriability of innovation, cumulativeness of technical advances and properties of the knowledge base. The analysis clarifies that the paradigm has the potential to revolutionize a number of industries as a result of its technological opportunities and worldwide stakeholders, but also faces challenges in the form of technical and ethical difficulties.

Abstract [sv]

Arbetet som presenteras i den här studien grundar sig i det långsiktiga målet att utveckla en social robot som kan vara med och leda samtalssessioner på ett språkcafé. I detalj har studien undersökt om det går att klassificera engagemang hos en samtalsdeltagare utifrån dess ansiktsuttryck och blickriktning – två faktorer som tidigare studier visat sig vara centrala för människans engagemang. För att utföra bedömningen har mjukvaran Openface extraherat nämnda parametrar från en tidigare fältstudie vilka sedan har processats med maskininlärningsmodellen Support Vector Machine. Efter gedigna försök att finna optimala värden på hyperparametrar till modellen lyckades den slutligen predicera engagemang på en tregradig skala med 54,5% accuracy.

Vidare har studien också undersökt potentialen för det nya teknologiska paradigmet som den sociala roboten utgör. Potentialen har analyserats med utgångspunkt i Dosis fyra dimensioner: teknologiska möjligheter, möjliga vinster från innovation, kumulativ höjd på teknologiska framsteg och egenskaper i kunskapsbasen. Analysen klargör att paradigmet har förutsättningar att revolutionera ett flertal industrier till följd av dess teknologiska möjligheter och världsomfattande intressenter, men står också inför utmaningar i form av tekniska och etiska svårigheter.

Place, publisher, year, edition, pages
2019. , p. 12
Series
TRITA-EECS-EX ; 2019:287
Keywords [en]
Engagement, Facial Action Units, Machine learning, OpenFace, Paradigm, Social robot, Support Vector Machine.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262046OAI: oai:DiVA.org:kth-262046DiVA, id: diva2:1360751
Examiners
Available from: 2019-11-07 Created: 2019-10-14 Last updated: 2019-11-07Bibliographically approved

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