Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy activity samples to be predicted at shallower layers, without executing deeper layers, hence leading to notable adaptiveness in terms of accuracy-speed trade-off under varying resource demands. However, prior most works typically optimize all the classifiers equally on all types of activity data. As a result, deeper classifiers will only see hard samples during test phase, which renders the model suboptimal due to the training-test data distribution mismatch. Such issue has been rarely explored in the context of activity recognition. In this paper, to close the gap, we propose to organize all these classifiers as a dynamic-depth network and jointly optimize them in a similar gradient-boosting manner. Specifically, a gradient-rescaling is employed to bound the gradients of parameters at different depths, that makes such training procedure more stable. Particularly, we perform a prediction reweighting to emphasize current deep classifier while weakening the ensemble of its previous classifiers, so as to relieve the shortage of training data at deeper classifiers. Comprehensive experiments on multiple HAR benchmarks including UCI-HAR, PAMAP2, UniMiB-SHAR, and USC-HAD verify that it is state-of-the-art in accuracy and speed. A real implementation is measured on an ARM-based mobile device.