An AIoT-enabled Autonomous Dementia Monitoring System

TL;DR

This paper presents an AIoT system that uses machine learning models to monitor and predict dementia-related activities in elderly patients within smart homes, achieving high accuracy in activity inference and trend prediction.

Contribution

It introduces an integrated AIoT framework employing RF and LSTM models for real-time activity monitoring and disease trend forecasting in dementia care.

Findings

RF activity inference accuracy > 99%

RF abnormal activity detection accuracy > 94%

LSTM effectively predicts future activity trends

Abstract

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.