The development of predictive tools has been commonly utilized as the most effective manner to prevent illnesses that strike suddenly. Within this context, investigations linking fine human motor control with brain stroke risk factors are considered to have a high potential but they are still in an early stage of research. The present paper analyses neuromuscular features of oscillatory movements based on the Omega-Lognormal model of the Kinematic Theory. On a database of oscillatory movements from 120 subjects, we demonstrate that the proposed features differ significantly between subjects with and without brain stroke risk factors. This promising result motivates the development of predictive tools based on the Omega-Lognormal model.