An AIoT-enabled Autonomous Dementia Monitoring System
This addresses monitoring challenges for elderly dementia patients in smart homes, but it is incremental as it applies existing methods (Random Forest and LSTM) to a specific dataset.
The researchers developed an AIoT system for monitoring dementia patients in smart homes, achieving over 99% accuracy for activity inference and 94% for abnormal activity detection using Random Forest models, with LSTM providing trend predictions for disease-related activities.
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.