Our partners from the Aristotle University of Thessaloniki Electrical and Computer Engineering Department (AUTH-MUG) have published a new article in the IEEE Journal of Biomedical and Health Informatics discussing how modeling wrist micromovements can contribute to identify potential differences in the eating behaviour of healthy individuals and persons facing Parkinson’s disease.
Research suggests that eating and swallowing difficulties affect about a 90% of all people with Parkinson’s. The i-PROGNOSIS study of eating behaviour among early and advanced patients with Parkinson’s disease is further exploring meal-related behavioural differences people living with Parkinson’s (both early and advanced patients) and healthy individuals. Participants to this study have received smartwatches for real-life monitoring of their eating behaviours in their home environment.
The newly published paper ‘Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data’ introduces the intelligent systems that will eventually be able to support the identification of these Parkinson’s-induced differences. The article presents a state-of-the-art algorithm that enables the automatic detection of food intake moments that occur during the course of a meal by processing the inertial signals of a common smartwatch.