Activity recognition pipeline
Classifying multidimensional time series sensor data is inherently more complex than classifying traditional nominal data, as we saw in the previous chapters. First, each observation is temporally connected to the previous and following observations, making it very difficult to apply a straightforward classification of a single set of observations only. Second, the data obtained by sensors at different time points is stochastic, that is, unpredictable due to the influence of sensor noise, environmental disturbances, and many other factors. Moreover, an activity can consist of various sub-activities executed in a different manner and each person performs the activity a bit differently, which results in high intraclass ...
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