Towards Less Supervision in Activity Recognition from Wearable Sensors
Towards Less Supervision in Activity Recognition from Wearable Sensors
复制标题
减少对可穿戴传感器活动识别的监管
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
B. Schiele
中科院分区：
文献类型：
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作者：
Tâm Huynh;B. Schiele
近年来，活动识别因其在上下文感知可穿戴计算中的潜力和实用性而引起了广泛关注。然而，大多数活动识别方法依赖于监督学习技术，限制了它们在现实场景中的适用性以及它们对大量活动和训练数据的可扩展性。关于分类器的选择，最先进的活动识别算法可以大致分为两组，一组使用生成模型，另一组使用其他判别方法。本文提出了一种活动识别方法，该方法将生成模型与判别分类器以集成的方式结合起来。该算法的生成部分允许提取和学习活动数据中的结构，而无需任何标记或监督。然后，判别部分使用训练数据的一小部分但已标记的子集来训练判别分类器。在实验中，我们表明，即使仅使用训练数据的子集进行训练，该方案也可以获得高识别率。还分析和讨论了标记工作和识别性能之间的权衡。
Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity recognition rely on supervised learning techniques lim iting their applicability in real-world scenarios and their scalability to large amounts of activities and training data. State-of-the-art activity recognition algorithms can roughly be divided in two groups concerning the choice of the classifier, one group using generative models and the other discriminative approaches. This paper presents a method for activity recognition which combines a generative model with a discriminative classifier in an integrated approach. The generative part of the algorithm allows to extract and learn structure in activity data without any labeling or supervision. The discriminant part then uses a small but labeled subset of the training data to train a discriminant classifier. In experiments we show that this scheme enables to attain high recognition rates even though only a subset of the training data is used for training. Also the tradeoff between labeling effort and recognition performance is analyzed and discussed.