In this paper we propose a set of Electromyogram (EMG) based features such as muscles total pressure, flexors pressure, tensors pressure, and gesture stiffness, for the purpose of identifying differences in performing the same gesture across three pottery constructions namely bowl, cylindrical vase, and spherical vase. In identifying these EMG-based features we have developed a tool for visualizing in real-time the signals generated from a Myo sensor along with the muscle activation level in 3D space. In order to do this, we have introduced an algorithm for estimating the activation level of each muscle based on the weighted sum of the 8 EMG signals captured by Myo. In particular, the weights are calculated as the distance of the muscle cross-sectional volumes at Myo plane level from each of the 8 Myo pods, multiplied by the muscle cross-section volume. Statistics estimated on an experimental dataset for the proposed features such as mean, variance, and percentiles, indicate that gestures such as “Raise clay” and “Form down cyclic clay” exhibit differences across the three vase types (i.e. bowl, cylinder, and sphere), although perceived as identical. More details can be found in conference publication [20]. A visualization of the methodology is shown below.