Activity recognition in wearable computing faces two key challenges: i) activity
characteristics may be context-dependent and change under different contexts or situations;
ii) unknown contexts and activities may occur from time to time, requiring flexibility and
adaptability of the algorithm. We develop a context-aware mixture of deep models termed
the $\alpha $-$\beta $ network coupled with uncertainty quantification (UQ) based upon
maximum entropy to enhance human activity recognition performance. We improve …

Activity recognition in wearable computing faces two key challenges: i) the incorporation of
the dependency of activities on user contexts is very important; ii) unknown contexts and
activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
We develop a context-aware mixture of deep models termed the α-β network to enhance
human activity recognition performance (accuracy and F score) by 10% through identifying
high-level contexts in a data-driven way to guide model development. Furthermore, we …