Continuous and objective assessment is essential for accurate monitoring of the progression of neurodegenerative conditions such as Friedreich ataxia. However, current clinical assessments predominantly rely on the ability of the affected individual to complete specific clinical tests which may not capture the intricate kinematic details associated with ataxia Moreover, such testing often consists of a level of subjectivity of the assessing clinician. In this paper, we propose an objective measuring instrument, in the form of a spoon, equipped with the Internet-of-Things (IoT) based system and relevant machine learning techniques to quantitatively assess impairment levels while engaged in routine daily activity. In a clinical study involving individuals diagnosed with Friedreich ataxia, movement patterns during a simulated eating task were captured and kinematic biomarkers were extracted that were consistent with the frequently-used clinical rating scales. Multivariate analysis of these biomarkers allows us to accurately classify individuals with Friedreich ataxia and control subjects to an accuracy of 91%. Furthermore, the kinematic information captured from the spoon can be used to introduce an alternative assessment scheme with a greater sensitivity to ataxic movements and with less inter-rater discrepancy.