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穿戴式传感器数据分类用于识别缺失的传输序列使用树学习树
Kambatty Bojan Gurumoorthy1, Arun Sekar Rajasekaran1, Kaliraj Kalirajan1
1Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了协同传感器数据传输方案 (CSDTS),以确保可穿戴传感器 (WS) 的持续数据流. 该计划汇总数据并使用分类树来防止数据丢失,改善远程健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 数据传输 数据传输
背景情况:
- 来自可穿戴传感器 (WS) 的连续数据对于远程健康监测至关重要.
- 由于故障或重叠的间隔而导致的数据中断可能会损害诊断的准确性.
- 现有的方法很难保持不间断的数据序列.
研究的目的:
- 为可穿戴传感器引入协同传感器数据传输方案 (CSDTS).
- 确保连续的数据序列,以改善远程健康分析.
- 为了最大限度地减少数据丢失和减少数据传输等待时间.
主要方法:
- 数据聚合考虑重叠和不重叠的传感器间隔.
- 按先到先得服务的顺序通信来进行数据传输.
- 对连续或离散传输序列进行预验证的分类树学习.
- 同步积累和传输间隔,并匹配传感器数据密度.
主要成果:
- 协同传感器数据传输方案 (CSDTS) 通过有效汇总传感器数据来减少数据丢失.
- 使用分类树进行预验证,识别和管理缺失的数据序列.
- 在交替数据积累后传输离散的数据序列,防止丢失并减少等待时间.
结论:
- 该CSDTS有效地从可穿戴传感器生成连续的数据序列.
- 该方案通过防止数据丢失,提高了远程健康监测的可靠性.
- 该方法优化了数据传输,从而使患者和老年人护理分析更有效.
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