Cognitive Training and Stress Detection in MCI Frail Older People through Wearable Sensors and Machine Learning

Personalised training of motor and cognitive abilities is fundamental to help older people maintain a good quality of life, especially in case of frailty conditions. However, the training activity can increase the stress level, especially in persons affected by a chronic stress condition. Wearable technologies and m-health solutions can support the person, the medical specialist, and long-term care facilities to efficiently implement personalised therapy solutions by monitoring the stress level of each subject during the motor and cognitive training. In this paper we present a comprehensive work on this topic, starting from a pilot study involving a group of frail older adults suffering from Mild Cognitive Impairment (MCI) who actively participated in cognitive and motor rehabilitation sessions equipped with wearable physiological sensors and a mobile application for physiological monitoring. We analyse the collected data to investigate the stress response of frail older subjects during the therapy, and how the cognitive training is positively affected by physical exercise. Then, we evaluated a stress detection system based on several machine learning algorithms in order to highlight their performances on the real dataset we collected. However, stress detection algorithms generally provide only the identification of a stressful/non stressful event, which is not sufficient to personalise the therapy. Therefore, we propose a mobile system architecture for online stress monitoring able to infer the stress level during a session. The obtained result is then used as input for a Decision Support System (DSS) in order to support the medical user in the definition of a personalised therapy for frail older adults.