Active Learning for WBAN-based Health Monitoring

Cho-Chun Chiu, Tuan Nguyen, Ting He, Shiqiang Wang, Beom-Su Kim, Ki-Il Kim
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Abstract

We consider a novel active learning problem motivated by the need of learning machine learning models for health monitoring in wireless body area network (WBAN). Due to the limited resources at body sensors, collecting each unlabeled sample in WBAN incurs a nontrivial cost. Moreover, training health monitoring models typically requires labels indicating the patient's health state that need to be generated by healthcare professionals, which cannot be obtained at the same pace as data collection. These challenges make our problem fundamentally different from classical active learning, where unlabeled samples are free and labels can be queried in real time. To handle these challenges, we propose a two-phased active learning method, consisting of an online phase where a coreset construction algorithm is proposed to select a subset of unlabeled samples based on their noisy predictions, and an offline phase where the selected samples are labeled to train the target model. The samples selected by our algorithm are proved to yield a guaranteed error in approximating the full dataset in evaluating the loss function. Our evaluation based on real health monitoring data and our own experimentation demonstrates that our solution can drastically save the data curation cost without sacrificing the quality of the target model.
基于 WBAN 的健康监测的主动学习
我们考虑了一个新颖的主动学习问题,其动机是需要为无线体域网络(WBAN)的健康监测学习机器学习模型。由于人体传感器的资源有限,在 WBAN 中收集每个无标签样本都会产生不小的成本。此外,训练健康监测模型通常需要由医疗保健专业人员生成表明病人健康状况的标签,而这些标签无法与数据收集同步获得。这些挑战使得我们的问题与传统的主动学习有着本质区别,因为传统的主动学习是免费的,而且标签可以实时查询。为了应对这些挑战,我们提出了一种两阶段主动学习方法,包括一个在线阶段和一个离线阶段,前者提出了一种核心集构建算法,根据噪声预测选择未标记样本的子集,后者则对所选样本进行标记以训练目标模型。事实证明,我们的算法所选择的样本在评估损失函数时能保证误差不接近整个数据集。我们基于真实的健康监测数据和自己的实验进行了评估,结果表明我们的解决方案可以在不影响目标模型质量的前提下大幅节省数据整理成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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