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In recent years, motion capture data (mocap) have been widely used in computer games, film production and sport sciences. The great success of animated and animation enhanced feature films, such as Avatar, provide compelling evidence for the values of mocap techniques. However, even with the most expensive commercial mocap systems, there are still instances where noise and missing data are inevitable.
This paper examines the motion refinement problem and presents an effective framework to solve it, demonstrated by extensive experiments on both synthetic and real data. The experiment shows that the proposed method outperforms all competitors not only in predicting missing values but also in de-noising most of the time.
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Last Friday a postman knocked at my parent’s house in Italy.
He carried a parchment, from The National Strength and Condition Association.
On it is written that my Master Degree Thesis won “The strength of young graduates contest” as second best Italian research in its field.
The study of 2015, is titled: “THE BIOMECHANICS EVALUATION IN STUDYING THE MOTION – COGNITION RELATIONSHIP” and can be summarised as follow:
using a system of 8 QTM cameras and a force plate, I measured the effect of different tasks upon the static balance in 20 young volunteers.
To do so, I asked them to perform four tasks in a randomised order, while I was recording their centre of pressure (with force plate) and centre of mass (with 3D motion capture system).
- Open Eyes (OE). The participants were instructed to hold a steady position, standing up with their feet together, for 30s.
- Closed Eyes (CE). Same position as OE, but participants were instructed to keep their eyes closed for 30s.
- Cognitive Dual Task (COGN-DT). Holding the same steady position, I asked them to countdown aloud, backwards in threes from a number that I randomly chose.
- Motor Dual Task (MOT-DT). Same position, but for this task volunteers were instructed to move their fingers (of the right hand) and touch their thumb alternately, for 30s.
What the result told us was that the COGN-DT was causing more perturbation, followed by the CE task.
Special thanks go to the people who helped me at the MotionLab in Naples (Giuseppe Sorrentino, Laura Mandolesi and Pasquale Varriale), and to my current supervisors (Alison McConnell, Tom Wainwright and James Gavin) who believed in me by giving me the opportunity to be here today.
Looking forward, with hope to collect more milestones.