Consumer healthcare Internet of Things (IoT) devices are gaining popularity in our homes and hospitals. These devices provide continuous monitoring at a low cost and can be used to augment high-precision medical equipment. However, major challenges remain in applying pre-trained global models for anomaly detection on smart health monitoring, for a diverse set of individuals that they provide care for. In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices. We develop a rigorous methodology that relies on automated IoT experimentation. We use a rich real-world dataset from in-home patient monitoring from 44 households of People Living With Dementia (PLWD) over two years. Our results indicate that anomalies can be identified with accuracy up to 99% and mean training times as low as 0.88 seconds. While all models achieve high accuracy when trained on the same patient, their accuracy degrades when evaluated on different patients.

翻译：消费者医疗物联网（IoT）设备在家庭和医院中日益普及。这些设备以低成本提供持续监测功能，可用于增强高精度医疗设备。然而，在智能健康监测中应用预训练全局模型对多样化个体进行异常检测仍面临重大挑战。本文提出PRISM，一种基于边缘计算的系统，用于家庭智能医疗设备的实验研究。我们开发了一套严格的方法论，依赖于自动化物联网实验。利用来自44个痴呆症患者家庭两年间居家监测的丰富真实数据集。实验结果表明，异常检测准确率可达99%，平均训练时间低至0.88秒。虽然所有模型在同患者数据上训练时均表现出高准确率，但跨患者评估时准确率显著下降。