We present a new scheme for Alzheimer's Disease (AD) automatic assessment, based on Archimedes spiral, drawn on a digitizing tablet. We propose to enrich spiral images generated from the raw sequence of pen coordinates with dynamic information (pressure, altitude, velocity) represented with a semi-global encoding in RGB images. By exploiting Transfer Learning, such hybrid images are given as input to a deep network for an automatic high-level feature extraction. Experiments on 30 AD patients and 45 Healthy Controls (HC) showed that the hybrid representations allow a considerable improvement of classification performance, compared to those obtained on raw spiral images. We reach, with SVM classifiers, an accuracy of 79% with pressure, 76% with velocity, and 70.5% with altitude. The analysis with PCA of internal features of the deep network, showed that dynamic information included in images explain a much higher amount of variance compared to raw images. Moreover, our study demonstrates the need for a semi-global description of dynamic parameters, for a better discrimination of AD and HC classes. This description allows uncovering specific trends on the dynamics for both classes. Finally, combining the decisions of the three SVMs leads to 81.5% of accuracy.Clinical Relevance- This work proposes a decision-aid tool for detecting AD at an early stage, based on a non-invasive simple graphic task, executed on a Wacom digitizer. This task can be considered in the battery of usual clinical tests.