Spatial navigation of indoor space usage patterns reveals important cues about the cognitive health of individuals. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth Low Energy (BLE) and Inertial Measurement Unit sensors (IMU) for tracking indoor movements for a large indoor facility (over 1600 m^2) that was designed to facilitate therapeutic activities for individuals with Mild Cognitive Impairment. The facility is instrumented with 39 edge computing systems with an on-premise fog server, and subjects carry BLE beacon and IMU sensors on-body. We proposed an adaptive trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle inconsistent coverage of edge devices in large spaces with varying signal strength that leads to intermittent detection of beacons. The proposed BLE-based localization is further enhanced by fusing with an IMU-based tracking method using a dead-reckoning technique. Our experiment results, achieved in a real clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of the individual patients' movements.

翻译：室内空间使用模式的空间导航揭示了关于个体认知健康的重要线索。本文提出了一种低成本、可扩展的开源边缘计算系统，该系统利用低功耗蓝牙（BLE）和惯性测量单元传感器（IMU），针对一个专为轻度认知障碍患者设计治疗活动的大型室内设施（面积超过1600平方米）进行室内运动追踪。该设施安装了39个边缘计算系统并配备本地雾服务器，受试者随身携带BLE信标和IMU传感器。我们提出了一种自适应三边定位方法，该方法通过考虑BLE信标向周围边缘设备发送信号的时序密度，来应对大型空间中边缘设备覆盖不一致及信号强度变化导致的信标间歇性检测问题。通过融合基于IMU的航位推算追踪技术，进一步提升了所提出的BLE定位方法的性能。在实际临床环境中的实验结果表明，普通医疗设施可被改造为能够自动评估个体患者运动状态的智能空间。