Saeedi Ramyar, Sasani Keyvan, Norgaard Skyler, Gebremedhin Assefaw H
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1193-1196. doi: 10.1109/EMBC.2018.8512533.
Human activity recognition (HAR) is an important component in health-care systems. For example, it can enable context-aware applications such as elderly care and patient monitoring. Relying on a set of training data, supervised machine learning algorithms form the core intelligence of most existing HAR systems. Meanwhile, the accuracy of an HAR model highly depends on the similarity between the training and the operating context. Therefore, there is a need for developing machine learning algorithms that can easily adapt to the operating context at hand. In this paper, we propose a cross-subject transfer learning algorithm that links source and target subjects by constructing manifolds from feature-level representation of the source subject(s). Our algorithm assigns labels to the unlabeled data in the current context using the manifold learned from the source subject(s). The newly labeled data is used to develop a personalized HAR model for the current context (i.e., target subject). We demonstrate the efficacy of the algorithm using a publicly available dataset on HAR. We show that the proposed framework improves the accuracy of activity recognition by up to 24%.
人类活动识别（HAR）是医疗保健系统中的一个重要组成部分。例如，它可以实现诸如老年护理和患者监测等情境感知应用。大多数现有的HAR系统的核心智能依赖于一组训练数据，通过监督机器学习算法来实现。同时，HAR模型的准确性高度依赖于训练情境和操作情境之间的相似性。因此，需要开发能够轻松适应当前操作情境的机器学习算法。在本文中，我们提出了一种跨主体迁移学习算法，该算法通过从源主体的特征级表示构建流形来连接源主体和目标主体。我们的算法使用从源主体学到的流形为当前情境中的未标记数据分配标签。新标记的数据用于为当前情境（即目标主体）开发个性化的HAR模型。我们使用一个公开可用的HAR数据集证明了该算法的有效性。我们表明，所提出的框架将活动识别的准确率提高了24%。
