Print Email Facebook Twitter Estimating Intentions to Speak Using Body Postures in Social Interactions Title Estimating Intentions to Speak Using Body Postures in Social Interactions: Leveraging Different Machine Learning Techniques for Accurate Estimation of Intentions to Speak In-the-Wild Author Tang, Luning (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Hung, H.S. (mentor) Elnouty, A.W.F.A.M. (graduation committee) Degree granting institution Delft University of Technology Corporate name Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Everyone has the intention to speak sometimes. Allowing agents to estimate people's intention of speaking can increase conversation efficiency and engagement. The intention of speaking can be expressed by multiple modalities as social cues. In order to add value to existing accelerometer-based research, this research aims to build a model on body postures and explore how it performs on both successful and unsuccessful intention cases. The time segments of successful intentions are automatically generated and the segments of unsuccessful intentions are annotated in a small time period. The model uses poses extracted from the successful intention segments and evaluated on both successful and unsuccessful cases. It is shown that body posture is an effective modality to predict the intention while there are problems like visibility based on camera angles and lack of context while combining data from multiple angles. More modalities are to be added to enhance the model's generalisability and reliability. To reference this document use: http://resolver.tudelft.nl/uuid:decda83a-ae40-4851-8e5d-e79a149abd25 Part of collection Student theses Document type bachelor thesis Rights © 2023 Luning Tang Files PDF CSE3000_Final_Paper_5_.pdf 1.37 MB Close viewer /islandora/object/uuid:decda83a-ae40-4851-8e5d-e79a149abd25/datastream/OBJ/view