Machine Learning Coffee Seminar: Aino Tietäväinen, University of Helsinki

2017-04-24 09:15:00 2017-04-24 10:00:00 Europe/Helsinki Machine Learning Coffee Seminar: Aino Tietäväinen, University of Helsinki Weekly seminars held jointly by Aalto University and the University of Helsinki. http://cs.aalto.fi/en/midcom-permalink-1e7201caaaef072201c11e78ec1e9280fbf53b653b6 Konemiehentie 2, 02150, Espoo

Weekly seminars held jointly by Aalto University and the University of Helsinki.

24.04.2017 / 09:15 - 10:00
seminar room T5, Konemiehentie 2, 02150, Espoo, FI

Helsinki region machine learning researchers will start our week by an exciting machine learning talk. The aim is to gather people from different fields of science with interest in machine learning. Porridge and coffee is served at 9:00 and the talk will begin at 9:15. The venue for this talk is seminar room T5, CS building.

Subscribe to the mailing list where seminar topics are announced beforehand.

Nintendo Wii Fit-Based Balance Testing to Detect Sleep Deprivation: Approximate Bayesian Computation Approach

Aino Tietäväinen
Department of Physics, University of Helsinki

Abstract:

Sleep deprivation deteriorates health and causes accidents. Measuring a person’s postural steadiness may be used to determine his/hers state of alertness. Posturographic measurements are easy to conduct: a person’s body sway is measured during upright stance on a balance board for 60 s. The Nintendo Wii Fit balance board is a portable and affordable alternative to expensive clinical force plates. Body sway may be modeled with a single-link inverted pendulum (Asai et al. 2009). The model parameters, such as time delay and noise intensity in the nervous system, are physiologically relevant. The pendulum is kept upright with controllers, that include stiffness and damping gain parameters. Level of control determines how often the active controller is ON. The model cannot be solved analytically in closed form. Therefore, inferring model parameters and their confidence limits is nontrivial. We used sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm to infer the model parameters. The inferred parameters may allow determining a person’s state of alertness.

See the next talks at the seminar webpage.

Please spread the news and join us for our weekly habit of beginning the week by an interesting machine learning talk!

Welcome!