3. A model of the real world.. IT MAY BE ARGUED THAT ACTIVITIES, INTERACTIONS AND COGNITION IS WHERE THE BIGGEST BANG FOR THE BUCK IS….
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8. Capturing Interactions…. Detailed But Human Intensive and Can miss Dynamic Events Data Driven Too Coarse Not cognitively grounded Laxmisan et al. 2007 Malhotra et al. 2007 Alwan et al. 2006 Ostbye et al. 2003
29. Methodology For Choosing Exercises Cognitive task analysis Suturing->{setting the needle->passing suture->tying} Matching observational Parameters in the real world And virtual world Monitor progress through mechanism that work in an ambient manner Adapt gaming scores to our needs
In 1960, Rudolf E. Kalman published his famous paper describing a recursive solution to the discrete linear filtering problem. The Kalman filter addresses the general problem of trying to estimate the state x of a discrete-time controlled process that is governed by the linear stochastic difference equation, with a measurement z. The Kalman filter estimates a process by using a form of feedback control: the filter estimates the process state at some time and then obtains feedback in the form of (noisy) measurements. As such, the equations for the Kalman filter fall into two groups: time update equations and measurement update equations. The time update equations are responsible for projecting forward the current state and error covariance estimates to obtain the a priori estimates for the next time step. The measurement update equations are responsible for the feedback, i.e. for incorporating a new measurement into the a priori estimate to obtain an improved a posteriori estimate. If the process to be estimated or the measurement relationship to the process is non-linear, a filter that linearizes about the current mean and covariance will be needed. This filter is referred to as an extended Kalman filter.
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