Physical activity consists of complex behavior, typically structured in bouts which can consist of one continuous movement (e.g., exercise) or many sporadic movements (e.g., household chores). Each bout can be represented as a block of feature vectors corresponding to the same activity type. This paper introduces a general distance metric technique to use this block representation to first predict activity type, and then uses the predicted activity to estimate energy expenditure within a novel framework. This distance metric, dubbed Bipart, learns block-level information from both training and test sets, combining both to form a projection space which materializes block-level constraints. Thus, Bipart provides a space which can improve the bout classification performance of all classifiers. We also propose an energy expenditure estimation framework which leverages activity classification in order to improve estimates. Comprehensive experiments on waist-mounted accelerometer data, comparing Bipart against many similar methods as well as other classifiers, demonstrate the superior activity recognition of Bipart, especially in low-information experimental settings.

中文翻译：

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Bipart：学习用于活动检测的块结构

身体活动由复杂的行为组成，通常以回合的形式构成，可以由一个连续的运动（例如，锻炼）或许多零星的运动（例如，家务）组成。每个回合可以表示为与相同活动类型对应的特征向量块。本文介绍了一种通用的距离度量技术，首先使用这种块表示来预测活动类型，然后使用预测的活动来估计新框架内的能量消耗。这个被称为 Bipart 的距离度量从训练集和测试集学习块级信息，将两者结合形成一个投影空间，实现块级约束。因此，Bipart 提供了一个空间，可以提高所有分类器的分类性能。我们还提出了一个能量消耗估算框架，该框架利用活动分类来改进估算。腰部加速度计数据的综合实验，将 Bipart 与许多类似方法以及其他分类器进行比较，证明了 Bipart 的卓越活动识别，尤其是在低信息实验设置中。