Bipart: Learning Block Structure for Activity Detection.
Bipart: Learning Block Structure for Activity Detection.
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DOI：
10.1109/tkde.2014.2300480
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发表时间：
2014-10-01
影响因子：
8.9
通讯作者：
Crouter SE
中科院分区：
文献类型：
--
作者：
Mu Y;Lo HZ;Ding W;Amaral K;Crouter SE
体力活动包括复杂的行为，通常以回合形式组织，可以包括一个连续的运动(例如锻炼)或许多零星的运动(例如家务)。每一回合可以被表示为对应于相同活动类型的特征向量块。本文介绍了一种通用的距离度量技术，它首先使用这种块表示来预测活动类型，然后在一个新的框架内使用预测的活动来估计能量消耗。这个距离度量被称为BiPart，它从训练集和测试集学习块级信息，将两者结合起来形成实现块级约束的投影空间。因此，BiPart提供了一个空间，可以提高所有分类器的Bout分类性能。我们还提出了一个能源支出估计框架，该框架利用活动分类来改进估计。在腰部安装的加速度计数据上的综合实验，将BiPart与许多类似的方法以及其他分类器进行了比较，证明了BiPart具有优越的活动识别能力，特别是在低信息的实验环境下。
Physical activity consists 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.