The project aims to study new touch and gesture interaction with novel methodologies. We combine finger biomechanics measurements, eye-movement tracking and experience measurement to get an understanding how users experience new interaction technologies. In experience measurement we utilize a novel quantitative-qualitative experience mapping methods. The data is modeled with Bayesian network, which can reveal relationships between different attributes and parameters via backwards reasoning. This provides novel possibilities to study human experience in interaction. Bayesian networks can be used not only to connect and statistically model single attributes and variables but to construct an entire network that combine all the variables and attributes into one large ensemble. This makes it possible to find and understand completely new connections and form an integrated theory of human interaction experience. This is a collaborative project between MVPR and Visual Cognition Research Group at University of Helsinki.


Toni Kuronen E-mail Office: 2418 Research Assistant
Tuomas Eerola E-mail Office: 2422 Tel: +358 40 139 3405 Project Manager, Post-Doctoral Researcher
Lasse Lensu E-mail Office: 2413 Tel: +358 40 759 1720 Professor
Heikki Kälviäinen E-mail Office: 2409 Tel: +358 40 586 7552 Project Leader, Professor


Hand tracking in high-speed camera videos

By Ville Hiltunen, Master's Thesis, Lappeenranta University of Technology, 2013.

The thesis focused on 2D-based hand tracking in high-speed camera videos.

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Comparison of general object trackers for hand tracking in high-speed videos

By Ville Hiltunen, Tuomas Eerola, Lasse Lensu, and Heikki Kälviäinen, In the 22nd International Conference on Pattern Recognition (ICPR 2014), 2014.

We provided the first solid comparison of state-of-the-art general object trackers on hand tracking with a primary focus on grey-scale high-speed videos.

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Post-processing and Analysis of Tracked Hand Trajectories

By Toni Kuronen, Master's Thesis, Lappeenranta University of Technology, 2014.

The thesis focused on high-speed hand tracking and trajectory filtering for studying human-computer interaction.

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High-Speed Hand Tracking for Studying Human-Computer Interaction

By Toni Kuronen, Tuomas Eerola, Lasse Lensu, Jari Takatalo, Jukka Häkkinen, and Heikki Kälviäinen, In the 19th Scandinavian Conference on Image Analysis (SCIA 2015), 2015.

In this paper, state-of-the-art tracking and trajectory filtering algorithms are compared based on their suitability for the finger tracking during human-computer interaction task.

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Simultaneous Camera Calibration and Temporal Alignment of 2D and 3D Trajectories

By Joni Herttuainen, Tuomas Eerola, Lasse Lensu, and Heikki Kälviäinen, In International Conference on Computer Vision Theory and Applications (VISAPP 2017), 2017.

We present a method that given the 2D and 3D motion trajectories recorded with a camera and 3D sensor, automatically calibrates the camera with respect to the 3D sensor coordinates and aligns the trajectories with respect to time.

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Multi-Camera Finger Tracking and 3D Trajectory Reconstruction for HCI Studies

By Vadim Lyubanenko, Toni Kuronen, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, and Jukka Häkkinen, In the Advanced Concepts for Intelligent Vision Systems (ACIVS 2017), 2017.

The paper proposes a framework for multi-camera finger movement measurements in 3D for human-computer interaction studies.

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  1. MVPR High-speed hand and finger tracking database (MVPR-HSHandDB01)


Visual Cognition Research Group, University of Helsinki