  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}-\b{eta} network
coupled with uncertainty quantification (UQ) based upon maximum entropy to
enhance human activity recognition performance. We improve accuracy and F score
by 10% by identifying high-level contexts in a data-driven way to guide model
development. In order to ensure training stability, we have used a
clustering-based pre-training in both public and in-house datasets,
demonstrating improved accuracy through unknown context discovery.
