基于决策的框架,促进 EDGE 计算在智能医疗保健中的应用

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Simranjit Singh, Mohit Sajwan, Sonal Kukreja
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引用次数: 0

摘要

在过去几年里,随着人口和健康问题的增加,人们需要高效的健康监测解决方案,帮助患者持续监测自己的健康状况,以便在最初阶段就意识到任何健康风险。传感和智能技术的进步有助于监测人类行为,从而预测健康风险。在这项工作中,我们在边缘设备上使用随机森林、SVM、决策树、长短期记忆(LSTM)和门控循环单元(GRU),提出了一种基于决策的动态活动预测系统。我们使用 MHealth 数据集的加速度、转弯率和磁场等特征来训练模型,以预测从各种传感器收集到的站立、攀爬、跑步和慢跑等活动。我们的框架根据实时数据大小和边缘设备能力,在机器学习(ML)和深度学习(DL)算法之间进行动态选择,以确保最佳性能和资源利用率。对所提模型的结果进行了比较和分析。实验结果表明,在所有机器学习方法中,随机森林的总体准确率最高,达到 98%;而在深度学习算法中,LSTM 和 GRU 的准确率最高,均达到 98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Decision-based framework to facilitate EDGE computing in smart health care

Decision-based framework to facilitate EDGE computing in smart health care

In the past few years, with the increase in population and health concerns, there has been a need for efficient health monitoring solutions that can help patients monitor their health consistently to be aware of any health risks at the initial stage. The advancement in sensing and smart technologies helps monitor human behaviors to predict health risks. In this work, a dynamic decision-based activity prediction system is proposed using Random Forest, SVM, Decision Trees, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on an edge device. We train the models using features from the MHealth dataset, such as acceleration, rate of turn, and magnetic field, to predict activities such as standing, climbing, running, and jogging, collected from various sensors. Our framework dynamically selects between machine learning (ML) and deep learning (DL) algorithms based on real-time data size and edge device capabilities, ensuring optimal performance and resource utilization. The results for the proposed models are compared and analyzed. The experimental results indicate that among all machine learning methods, Random Forest achieves the highest overall accuracy at 98%, while in deep learning algorithms, both LSTM and GRU reach a maximum accuracy of 98%.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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