Evaluations of injuries and the assessment of residual dexterity is carried out by clinicians by means of standardized tests. Several dexterity tests have been developed and all of them require the test administrator to sit in front of the subject and visually assess the motions performed. Automatic tools can reduce the time needed for the evaluation and can provide additional useful information. For many of the available dexterity tests, automated evaluation methods have been developed. However, for the Minnesota dexterity test, despite being widely used, just one automated solution based on multiple depth sensor cameras was developed. This study aims to provide a wearable and flexible method to automatically evaluate the outcomes of Minnesota dexterity tests. The proposed methodology consists of a parametric model that is capable of processing acceleration data collected with an IMU attached to the centre of mass of the subject’s dominant hand. The developed model was successfully validated against a subject study with ten participants.