Article
Version 1
Preserved in Portico This version is not peer-reviewed
Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities
Version 1
: Received: 12 December 2023 / Approved: 13 December 2023 / Online: 14 December 2023 (04:18:58 CET)
A peer-reviewed article of this Preprint also exists.
Garcia, C.; Inoue, S. Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities. Sensors 2024, 24, 319. Garcia, C.; Inoue, S. Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities. Sensors 2024, 24, 319.
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 sensor data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different sensors by relabeling to the sensors with less samples resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority class are used to measure signal pattern to find matching sensors to relabel. By matching sensors between classes, two variation of relabeling are implemented specifically full and partial matching. The performance is evaluated using real world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. 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 sensor data with our proposed relabeling method for data augmentation, we achieved higher minority class F1-score as compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected sensor data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.
Keywords
oversampling; augmentation; machine learning; signal measurement; signal pattern; relabeling; indoor localization; beacon; nursing care
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
