论文标题
支持AIOT的自主痴呆监测系统
An AIoT-enabled Autonomous Dementia Monitoring System
论文作者
论文摘要
提出了一种在智能家居中监测的老年痴呆症患者的自主人工互联网（AIOT）系统。该系统主要根据传感器数据的活性推断实现两个功能，这些功能是实时活动异常监测和与疾病相关活动的趋势预测。具体而言，CASAS数据集用于训练一个随机森林（RF）模型进行活动推断。然后，通过活动推理的输出数据训练的另一个RF模型用于异常活动监测。特别是，由于其准确性，时间效率，灵活性和可解释性之间的均衡贸易折扣，因此选择了RF。此外，长期记忆（LSTM）可用于预测患者疾病相关的活动趋势。因此，设计用于活动推断和异常活动检测的两个RF分类器的精度分别大于99％和94％。此外，以睡眠时间为例，LSTM模型实现了准确且明显的未来趋势预测。
An autonomous Artificial Internet of Things (AIoT) system for elderly dementia patients monitoring in a smart home is presented. The system mainly implements two functions based on the activity inference of the sensor data, which are real time abnormal activity monitoring and trend prediction of disease related activities. Specifically, CASAS dataset is employed to train a Random Forest (RF) model for activity inference. Then, another RF model trained by the output data of activity inference is used for abnormal activity monitoring. Particularly, RF is chosen for these tasks because of its balanced trade offs between accuracy, time efficiency, flexibility, and interpretability. Moreover, Long Short Term Memory (LSTM) is utilised to forecast the disease related activity trend of a patient. Consequently, the accuracy of two RF classifiers designed for activity inference and abnormal activity detection is greater than 99 percent and 94 percent, respectively. Furthermore, using the duration of sleep as an example, the LSTM model achieves accurate and evident future trends prediction.
