Lorenzo Hermez

Motor impairments caused by neurological diseases have an important impact on gait, particularly on the coordination between left and right lower limbs. Deviation from normal gait is often measured to assess this impact on gross motor functions, and to monitor the progress of patients during rehabilitation. The concept of gait dissimilarity map is introduced to represent bilateral raw gait signals, while accounting for their respective spatiotemporal dynamics. A model of gait for the healthy population is constructed through Singular Value Decomposition, considering both lower limbs. The obtained eigenvectors synthesize the symmetry present in gait. Then, by projecting the dissimilarity maps of patients with gait disorders on the space formed by such eigenvectors, we compute their associated Eigen-Gait Asymmetry Index (EGAI) relatively to an average normal gait reference vector. For the knee joint in the sagittal plane, EGAI values of patients are higher (9.73 ±2.16) than those of healthy controls (3.86 ±0.9), reflecting the asymmetry induced by neurological diseases. Patients with hemiparesis show the highest EGAI (10.4 ±1.8), followed by patients with paraparesis (9.9 ±1.8) and patients with tetraparesis (8.6 ±2.5). Indeed, patients with hemiparesis show a more asymmetrical gait since only one side of the body is affected. EGAI for hip, ankle and pelvis joints in the sagittal plane show similar trends. Our innovative method characterizes bilateral gait, enriching traditional unilateral assessments. Our method yields a comprehensive score reflecting both asymmetry and gait deviations, aiming to provide clinicians with an effective and precise monitoring tool.

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.