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  • 1. Hedemalm, Emil
    Interaktiv Projektionskartläggning2014Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The world of interaction is ever expanding and novel approaches for dealing with image-based gesture and input recognition are wanted. To get a glimpse into the various problems and solutions present in the field, a configurable image filter pipeline framework based on OpenCV is presented. Aiming at real-time applications, the framework is written entirely in C++. Both OpenCV-based functions and custom filters are presented in order to solve various problems. The subjects of image analysis and human-computer interaction are explored, including a few specific use-cases. Pipeline configurations are presented for each use-case, including details such as processing time, the order in which filters are applied, settings used and problems that were encountered. The field of augmented reality is investigated and a few exemplary end-user applications are developed and presented for this purpose. Lastly, a discussion on the results is held, which answers problems with possible solutions and suggests further work where necessary.

  • 2.
    Hedemalm, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Online Transportation Mode Recognition and an Application to Promote Greener Transportation2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    It is now widely accepted that human behaviour accounts for a large portion of total global emissions, and thus influences climate change to a large extent. Changing human behaviour when it comes to mode of transportation is one component which could make a difference in the long term. In order to achieve behavioural change, we investigate the use of a persuasive multiplayer game. Transportation mode recognition is used within the game to provide bonuses and penalties to users based on their daily choices regarding transportation. To easily identify modes of transportation, an approach to transport recognition based on accelerometer and gyroscope data is analysed and extended. Preliminary results from the machine learning tests show that the classification true-positive rate for recognizing 10 different classes can reach up to 95% when using a history set (66% without). Preliminary results from testers of the game indicate that using games may be successful in causing positive change in user behaviour.

  • 3.
    Hedemalm, Emil
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kor, Ah-Lian
    Leeds Beckett University.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Pattinson, Colin
    School of Computing, Creative Technologies & Engineering, Leeds Beckett University.
    Promoting green transportation via persuasive games2017In: International SEEDS Conference 2017: Sustainable Ecological Engineering Design for Society – 13th & 14th September 2017, 2017Conference paper (Refereed)
    Abstract [en]

    It is now widely accepted that human behaviour accounts for a large portion of total global emissions, and thus influences climate change to a large extent (IPCC, 2014). Changing human behaviour when it comes to mode of transportation is one component which could make a difference in the long term. In order to achieve behavioural change, we investigate the use of a persuasive multiplayer game. Transportation mode recognition is used within the game to provide bonuses and penalties to users based on their daily choices regarding transportation. Preliminary results from testers of the game indicate that using games may be successful in causing positive change in user behaviour.

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