  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.
