论文标题
深度上下文感知的移动活动识别和未知上下文发现的不确定性量化
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
论文作者
论文摘要
可穿戴计算中的活动识别面临两个关键挑战：i）活动特征可能依赖上下文，并且在不同的上下文或情况下变化； ii）未知的上下文和活动可能会不时发生，需要算法的灵活性和适应性。我们开发了将α-\ b {eta}网络与不确定性定量（UQ）相结合的深层模型的上下文感知混合物，以增强人类活动识别性能。我们通过以数据驱动方式来指导模型开发的方式来确定高级环境，从而提高准确性和F得分10％。为了确保培训稳定性，我们在公共和内部数据集中使用了基于聚类的预训练，通过未知上下文发现证明了准确性的提高。
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 α-\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.
