Abstract
The temporal modes from Principle Component Analysis (PCA) are an effective tool for compressing pre-segmented information. This paper estimates a dynamic model for these temporal modes based on a sequential set of pre-segmented data. In particular, we analyze the gait of a man walking in place on a tread mill. We then apply it to a system where an occasional frame comes through covered with noise, and in such a situation we use a Kalman-Filter to identify the temporal modes, and it provides estimates with smoother, more realistic, dynamics than those of non-dynamic estimates such as least-squares. Afterward, we break our code by showing that the estimates and models are only as good as the data set with which they were trained.
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