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A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems
IEEE Transactions on Mobile Computing ( IF 7.9 ) Pub Date : 2020-03-01 , DOI: 10.1109/tmc.2018.2878673
Ramyar Saeedi , Assefaw H. Gebremedhin

Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training data to detect events of interest. However, in the dynamic environment in which wearables operate, the accuracy of a computational model drops whenever changes in configuration or context of the system occur. In this paper, using transfer learning as an organizing principle, we propose a novel design framework to enable autonomous reconfiguration of wearable systems. More specifically, we focus on the cases where the specifications of sensor(s) or the subject vary compared to what is available in the training data. We develop two new algorithms for data mapping (the mapping is between the training data and the data for the current operating setting). The first data mapping algorithm combines effective methods for finding signal similarity with network-based clustering, while the second algorithm is based on finding signal motifs. The data mapping algorithms constitute the centerpiece of the transfer learning phase in our framework. We demonstrate the efficacy of the data mapping algorithms using two publicly available datasets on human activity recognition. We show that the data mapping algorithms are up to two orders of magnitude faster compared to a brute-force approach. We also show that the proposed framework overall improves activity recognition accuracy by up to 15 percent for the first dataset and by up to 32 percent for the second dataset.

中文翻译:

用于可穿戴系统自主重新配置的信号级迁移学习框架

机器学习算法构成了可穿戴设备的核心智能,传统上从一组训练数据中推导出一个计算模型来检测感兴趣的事件。然而,在可穿戴设备运行的动态环境中,只要系统配置或环境发生变化,计算模型的准确性就会下降。在本文中,使用迁移学习作为组织原则,我们提出了一种新颖的设计框架,以实现可穿戴系统的自主重新配置。更具体地说,我们关注传感器或主题的规格与训练数据中可用的规格不同的情况。我们开发了两种新的数据映射算法(映射在训练数据和当前操作设置的数据之间)。第一种数据映射算法将寻找信号相似性的有效方法与基于网络的聚类相结合,而第二种算法基于寻找信号基序。数据映射算法构成了我们框架中迁移学习阶段的核心。我们使用两个公开可用的人类活动识别数据集证明了数据映射算法的有效性。我们表明,与蛮力方法相比,数据映射算法快了两个数量级。我们还表明,所提出的框架总体上将第一个数据集的活动识别准确度提高了 15%,第二个数据集的活动识别准确度提高了 32%。数据映射算法构成了我们框架中迁移学习阶段的核心。我们使用两个公开可用的人类活动识别数据集证明了数据映射算法的有效性。我们表明,与蛮力方法相比,数据映射算法快了两个数量级。我们还表明,所提出的框架总体上将第一个数据集的活动识别准确度提高了 15%,第二个数据集的活动识别准确度提高了 32%。数据映射算法构成了我们框架中迁移学习阶段的核心。我们使用两个公开可用的人类活动识别数据集证明了数据映射算法的有效性。我们表明,与蛮力方法相比,数据映射算法快了两个数量级。我们还表明,所提出的框架总体上将第一个数据集的活动识别准确度提高了 15%,第二个数据集的活动识别准确度提高了 32%。
更新日期:2020-03-01
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