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code archive 6555 disk0/00/00/07/54 2020-01-06 13:16:00 2024-07-22 09:30:59 2020-01-06 13:16:00 data_collection show Dehesa Javier J.Dehesa@bath.ac.uk 0000-0001-7692-2413 University of Bath TRUE Supervisor Vidler Andrew andrew.vidler@ninjatheory.com 0000-0001-6342-0844 Ninja Theory Supervisor Lutteroth Christof C.Lutteroth@bath.ac.uk 0000-0003-0634-7569 University of Bath Supervisor Padget Julian J.A.Padget@bath.ac.uk 0000-0003-1314-2094 University of Bath Ninja Theory Ltd FB0010 FB0040 FB0090 dept_compsci animation, gesture recognition, virtual reality, human-computer interaction, machine learning Gesture recognition data is stored in CSV files where each row contains the position and orientation of both hands and the gesture being performed on each frame. Animation data is stored in CSV files where each row contains the position and orientation of each joint of the skeleton. User studies data is stored in JASP files encoding the answers of each participant to each question in the study. This is the data repository for the paper "Touché: Data-Driven Interactive Sword Fighting in Virtual Reality" by Javier Dehesa, Andrew Vidler, Christof Lutteroth and Julian Padget, presented at CHI 2020 conference in Honolulu, HI, USA. See the publication for details. The archives gesture_recognition_data.zip and gesture_recognition_code.zip contain respectively the data and code for the gesture recognition component. Similarly, the archives animation_data.zip and animation_code.zip contain respectively the data and code for the animation component. Instructions about how to use these are provided within them. The archive user_studies.zip contains information about our user studies. The file questionnaire_study.jasp and interactive_study.jasp contain the data and analysis of the questionnaire and interactive studies respectively. They can be consulted with the open source tool JASP (https://jasp-stats.org/). The video questionnaire_conditions.mp4 shows the full videos used as the three conditions for the questionnaire study. 2020-01-06 University of Bath public https://www.youtube.com/embed/CO9wH1xo2tI RightsHolder University of Bath Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/L016540/1 EPSRC Centre for Doctoral Training in Digital Entertainment cent_dig_ent Gesture recognition data was collected with VR hardware in a custom-made virtual scenario, where the subject was presented with a signal indicating a gesture to perform, which they then did while pressing a button on the hand controller. Animation data was motion captured with specialised equipment within the facilities of Ninja Theory, Ltd. User studies data was collected through online forms, filled after each condition of each of the studies. The user studies data was preprocessed for convenience to produce an accessible JASP file. The preprocessing simply translated the raw text of the questions into short identifiers and mapped Likert points (like "strongly agree/disagree") to numerical values. For this publication, free-text comment data was removed from the dataset for anonymisation purposes. Gesture recognition data was captured with an Oculus Rift kit. Animimation data was captured with Vicon Bonita hardware. en 1 10.15125/BATH-00754 https://doi.org/10.1145/3313831.3376714 pub open