“Initially, I worked on a model designed to authenticate a signature,” Professor Plamondon explains. “Every individual has a unique way of signing. We had to devise an intelligent-signal process in order to recognize an individual’s signature. This led me to reflect on what, precisely, is a signature: a set of fast, successive pen strokes.”

The researcher developed a neuromuscular model that mirrors and segments the speed of a pen stroke using logarithmic functions. With his team of students, he developed algorithms that can be used to analyse movements made by a hand, an arm, a forearm, a head – even eyes. Professor Plamondon believes this neuromuscular model could become universal. Neurodegenerative diseases like Alzheimer’s and Parkinson’s, developmental problems, attention deficit disorders or even simply aging, create a distance between a subject and lognormality. The models developed by Professor Plamondon’s team enable them to analyse the differences in speed between lines drawn by people suffering from a disease and those sketched by healthy individuals.

“Our work has also indicated differences in signatures by people at risk of suffering strokes,” Professor Plamondon adds.

This research is leading to the creation of very dependable and non-invasive tools used to assess patients’ health. The team, in fact, is also developing applications for tablets and smartphones.