The analysis of clinical signs such as facial modifications (e.g. blinking, chewing, smacking), limb automatisms, ictal head turning and hand movements (e.g. hand dystonia, tapping, grabbing) [1,2], may provide clues as to the cerebral networks underpinning the epilepsy. However, the study of these signs relies heavily on clinical experience and training. Given the importance of body motion patterns in the assessment of epilepsy, prior works have demonstrated that automated analysis of semiological patterns based on computer vision can support diagnosis by standard and objective assessment methods among evaluators [3,4].
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