Identification and Evaluation of Behavioral Symptoms in Dementia Using Passive Radio Sensing and Machine Learning

Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

“…Instead of relying on the use of ratings by clinicians or selfreports from patients, the goal is to use objective and quantifiable behavioral data combined with artificial intelligence analyses to predict disease vulnerability or progression. Passively acquired daily locomotor data (i.e., not involving manual annotation) through room sensing technologies or wearables (i.e., digital phenotyping) have been started to be used to predict disease progression in elderly populations [23][24][25] or in individuals at risk for self-reported anxiety, depression or stress (for a review see ref. 21 ).…”