This study addresses the characterization of normal gait and pathological deviations caused by neurological diseases. We focus on the angular knee kinematics in the sagittal plane and we propose to exploit Hidden Markov Models to build a statistical model of normal gait. Such model provides a log-likelihood score that quantifies gait quality. Hence allowing to assess deviations of pathological cycles from normal gait. Our approach allows a refined characterization of motor impairments of three different patients' groups. In particular, it detects the affected lower limb in Hemiparetic patients. Comparatively to the Gait Variable Score and a Dynamic Time Warping-based metric, our results show that our statistical method is more effective for finely quantifying pathological deviations. Finally, we show the potential use of our methodology to assess therapeutic impacts during gait rehabilitation, which represents a promising avenue for improving patient care.