Modeling and Comprehension of Human Movements: Personal Digital Bodyguards for e-Security, e-Health and e-Learning

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VISION: In five years, the ubiquity of hand-held tablets and cell phones, along with their increased computing power and ergonomic data capture performances, will make it possible to convert these devices into Personalized Digital Bodyguards (PDBs). PDBs will protect people’s sensitive data with signature verification, provide equipment use security with writer authentication (e-security) and perform word spotting to monitor user fine motor control, which can detect stress, aging and health problems (e-health). In the hands of children, these tools will help them to learn and master their fine motricity and become better writers and students (e-learning). BACKGROUND: Our team developed the kinematic theory of rapid human movements and its lognormal models ¸¸ to explain many basic phenomena reported in classical studies on human motor control and to study several factors involved in fine motricity. We have created several software packages over the years to extract the lognormal parameters from various pen tip velocity curves under a range of experimental conditions. These tools allow a researcher to study the state of a writer’s neuromuscular system. BASIC HYPOTHESIS: the lognormality of the neuromuscular impulse response is a basic feature reflecting the behaviour of people who are in perfect control of their movements. As a corollary, motor control learning can be interpreted as a migration toward an ideal representation of perfectly mastered movements. In turn, aging and health problems should reveal a progressive departure from lognormality. In between these two progressive processes, for most of their lives, humans take advantage of lognormality to control their movements. GOALS: This research program focuses on three specific goals: 1. Theory: Provide new experimental support to the kinematic theory using quantitative Magnetic Resonance Imaging (qMRI), and propose original ideas for ways to use the lognormality principle. 2. Technique: Define, implement and test an ergonomic research platform that takes advantage of new input devices for handwriting acquisition, powerful algorithms working on a server in a cloud environment, and Graphic Processing Units to investigate lognormality. 3. E-application: Exploit, expand and disseminate our know-how about lognormality for innovative e-applications in biometrical (e-security), biomedical (e-health) and neurocognitive (e-learning) engineering by defining new evaluation metrics in collaboration with leading experts all over the world. EXPECTED IMPACT: As well as training many post-doc fellows and Ph.D students, the whole program will lead to major theoretical breakthroughs in neuromotor modeling, setting the stage for us to design efficient PDBs that will put individual security and protection choices in people’s hands for a better e-society.