A Hybrid Multi-classifier to Characterize and Interpret Hemiparetic Patients Gait Coordination

Abstract

The characterization of inter-segmental coordination patterns in hemi paretic gait is interesting to improve the management of hemiparetic patients. Indeed, the analysis of the coordination patterns can help clinician to establish patient diagnosis and to choose a treatment. The coordination patterns used in this article were obtained from the Continuous Relative Phase (CRP) measure in the sagittal plane. The CRP correlates angle positions and velocity of two segments, i.e. parts of the patient leg, over each phase of the gait cycle. Thigh-shank and shank-foot CRPs were measured for 66 hemiparetic patients, 27 healthy subjects and 14 patients pre and post treatment. CRPs signals are classified using a multi-classifier. This classification permits to discriminate gait patterns for hemiparetic and healthy subjects. The multi-classifier is based on a structural and a statistical approaches used in parallel. The structural part of the proposed hybrid method keeps links between the data issued from CRPs and the statistical part converts CRPs into spatial scalar parameters. Then, using a similarity measure this approach permits to quantify the global gait coordination improvement of patients after a therapeutic treatment. The proposed approach uses only interpretable parameters in order to let the classification results be physically understandable.