Prof. Růžena Bajcsy presents Personalized model of kinematics and dynamics of physical activities
On 2018-01-18 16:00:00 at KN:E-301(Šrámkova posluchárna)
32. Prague Computer Science Seminar
Low physical agility and sedentary lifestyle can cause serious health problems,
even with the younger population. It is equally obvious that every human’s
anatomy and physiology is different. We are therefore developing personalized
models of kinematics and dynamics of an individual during physical activities,
using system identification methodology. This has been facilitated by the
development of various noninvasive and relatively affordable sensors for
measuring position, velocity, acceleration, forces, electric signals, blood
oxygenation etc. We are using advanced computational tools from the fields of
robotics, control theory, and optimization theory.
Our particular experimental paradigm is based on the sit-to-stand exercise. We
process all the measurements, interpret them, and generate individual
predictive
models of the physical performance of the individual. Finally, we design
interventions, i.e. assistive devices to help the individual function better in
daily activities. The validity of our approach and its predictive performance
was tested on a group of subjects from the UC Berkeley and UC San Francisco
Medical School.
Low physical agility and sedentary lifestyle can cause serious health problems,
even with the younger population. It is equally obvious that every human’s
anatomy and physiology is different. We are therefore developing personalized
models of kinematics and dynamics of an individual during physical activities,
using system identification methodology. This has been facilitated by the
development of various noninvasive and relatively affordable sensors for
measuring position, velocity, acceleration, forces, electric signals, blood
oxygenation etc. We are using advanced computational tools from the fields of
robotics, control theory, and optimization theory.
Our particular experimental paradigm is based on the sit-to-stand exercise. We
process all the measurements, interpret them, and generate individual
predictive
models of the physical performance of the individual. Finally, we design
interventions, i.e. assistive devices to help the individual function better in
daily activities. The validity of our approach and its predictive performance
was tested on a group of subjects from the UC Berkeley and UC San Francisco
Medical School.
External www: http://praguecomputerscience.cz/index.php?l=en&p=32