在复杂环境中稀疏分布的边缘计算设备中使用蓝牙技术进行室内定位的图形三边化
Yashar Kiarashi1, Soheil Saghafi1, Barun Das1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究介绍了一种低成本的边缘计算系统,使用蓝牙低能 (BLE) 信号来跟踪轻度认知障碍 (MCI) 患者的室内活动. 该系统准确评估空间导航,有助于监测认知健康和治疗反应.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 认知科学 认知科学
背景情况:
- 空间导航模式为认知健康提供了洞察力.
- 轻度认知障碍 (MCI) 影响空间能力.
- 现有的室内跟踪方法在复杂的环境中可能缺乏可扩展性或准确性.
研究的目的:
- 开发和评估一个低成本,可扩展,开源的边缘计算系统来跟踪室内运动.
- 通过空间导航分析评估患有MCI的参与者的认知健康和治疗反应.
- 为了克服由于传感器分布稀疏的室内定位方面的挑战.
主要方法:
- 在一个1700平方米的设施中,采用39个蓝牙低能耗 (BLE) 信标和雾服务器实现了一个边缘计算系统.
- 开发一个图形三边化方法,考虑时间信标命中密度,以解决稀疏的边缘设备覆盖范围.
- 参与者携带的边缘计算系统对BLE信号接收的分析.
主要成果:
- 在多个参与者中实现了4.4米的平均定位误差.
- 在整个研究区域的区域级定位中达到超过85%的准确性.
- 在临床环境中证明了系统的有效性,信号强度变化和间歇性信标检测.
结论:
- 一个普通的医疗设施可以转变为一个智能空间,用于自动运动评估.
- 开发的系统可以客观地监测空间导航,这可能反映了MCI患者的健康状况或治疗疗效.
- 图形三边化方法提供了一个强大的解决方案,用于室内定位在稀有仪器的环境.
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