The Coaching - Machine Learning interface - Indoor rowing
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If you have a question about this talk, please contact Henrik Lieng.
Human rowing coaches, who are experienced in describing how athletes move,
could deputise machines to provide similar feedback if they could teach them
what to look for. Ubiquitous and precise kinetic sensors, along with
probabilistic inference algorithms, have strengthened the foundations of
motor-skill based interactive systems. However, these systems still leave us
uncertain over how to feel about a particular performance.
I will discuss a framework for developing machines that rate athletes along
natural and emotive scales such as “the importance of improving how much
`suspension’ they achieve”. After presenting a model of the criteria rowing
coaches use to describe their judgements of indoor rowing techniques, I will
propose an iterative scheme for single criteria that resolves multiple
judgesÂ’ perspectives on a set of performances into a consensus of ratings for each
performance. I will share guidelines for collecting data on indoor rowing
techniques for machine learning, before presenting initial evidence in support
of training Bayesian models to predict human ratings. I will evaluate
generative filters and linear regression for four criteria, showing they give
more useful predictions than 1) 10% to 50% of the human coaches, 2) randomly
rating performances (1×10-6 < p < 0.38) and 3) performance indicators based on
Newtonian mechanics (1×10-3 < p < 0.6).
This talk is part of the Rainbow Group Seminars series.
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