While abnormal patterns of cardiac electrophysiological activation are at the origin of important cardiovascular diseases (e.g. arrhythmia, asynchrony), the only clinically available method to observe detailed left ventricular endocardial surface activation pattern is through invasive catheter mapping. However this electrophysiological activation controls the onset of the mechanical contraction, therefore important information about the electrophysiology could be deduced from the detailed observation of the resulting motion patterns. In this article, we present the study of this inverse cardiac electro-kinematic relationship. The objective is to predict the activation pattern knowing the cardiac motion from the analysis of cardiac image sequences. To achieve this, we propose to create a rich patientspecific database of synthetic time series of cardiac images using simulations of a personalized cardiac electromechanical model, in order to study this complex relationship between electrical activity and kinematic patterns in the context of this specific patient. We use this database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from global and regional kinematic descriptors based on displacements or strains and their derivatives. Finally, we use this learning to estimate the patient's electrical activation times using the acquired clinical images. Experiments on the inverse electro-kinematic learning are demonstrated on synthetic sequences and are evaluated on clinical data with promising results. The error calculated between our prediction and the invasive intracardiac mapping ground truth is relatively small (around 10 ms for ischemic patients and 20 ms for non-ischemic patient). This approach suggests the possibility of non-invasive electrophysiological pattern estimation using cardiac motion imaging.