Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition
In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost. For example, in human activity recognition (HAR) and mobile health (mHealth) applications, monitoring performance should be achieved with a low cost to gather different sensory features, as maintaining sensors incur monetary, computation, and energy cost. We propose an adaptive feature selection method that dynamically selects features for prediction at any given time point. We formulate this problem as an $\ell_0$ minimization problem across time, and cast the combinatorial optimization problem into a stochastic optimization formulation. We then utilize a differentiable relaxation to make the problem amenable to gradient-based optimization. Our evaluations on four activity recognition datasets show that our method achieves a favorable trade-off between performance and the number of features used. Moreover, the dynamically selected features of our approach are shown to be interpretable and associated with the actual activity types.
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HAR
