An Emergency Response System to Support Early Stage Dementia Patients

Dementia patients living alone in the communities without much support from near and dear ones find it chal-lenging to receive instant help when needed including during emergencies. This work focuses on designing an end - to-end response system primarily to support early-stage Alzheimer's dementia (AD) patients living alone in their homes, in case of emergencies, including medical emergencies. The system aided with pervasive technologies can integrate AD patients, informal caregivers, and formal caregivers to support AD patients in need. Informal caregivers act as first responders to attend to patients and selecting appropriate informal caregivers based on certain predefined parameters is an important component of this system. This work has used single and ensemble Machine Learning (ML) algorithms for binary (to check if informal caregiver is available) and multiclass (to select the most suitable informal caregiver) classification.