Arousal level classification of the aging adult from electro-dermal activity: From hardware development to software architecture

The fast aging of the population around the world makes ambient intelligence-based assistive technologies essential for sustainable and efficient health-care systems. Aging adults who decide to live alone at home need constant monitoring to control their health status and quality of life. This work introduces a new wearable device that continuously monitors the emotional state of the elderly. Electro-dermal activity (EDA) is used to classify the aging adults into two groups: sleepiness and stressed. The results report a performance of more than 83% of accuracy by using exclusively time and magnitude features from EDA events.