PDF(Pubmed)
Abstract：
In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.
摘要：
在这项研究中,我们提出了一种基于蓝牙低功耗（BLE）技术在护理和护理人员室内定位中重新标记数据的机器学习增强方法。室内定位用于监控护理人员对患者的护理帮助，并深入了解工作量管理。然而,当可用于训练的数据量有限时，提高准确性是一项挑战。在本文中,我们提出了一种数据增强方法，通过重新标记到样本较少的位置来重用来自不同信标的接收信号强度（RSS），解决数据不平衡问题。少数和多数类别之间的标准偏差和Kullback-Leibler分歧用于测量信号模式，以找到要重新标记的匹配信标。通过匹配类之间的信标，实现了重新标记的两种变体，特别是完全和部分匹配。使用我们在安装有25个BLE信标的护理设施中收集的五天的真实世界数据集来评估性能。随机森林模型用于位置识别，并使用加权F1分数比较性能，以说明班级不平衡。通过使用我们提出的用于数据增强的重新标记方法来增加信标数据，与随机抽样的增强相比，我们获得了更高的少数民族F1分数，合成少数过采样技术(SMOTE)和自适应合成采样(ADASYN).我们提出的方法通过利用多数类样本来利用收集的信标数据。完全匹配显示相对于原始基线总体加权F1得分6至8%的改善。
