|Statistical Gait Analysis|
Statistical gait analysis (SGA) is a recently developed methodology, which performs a statistical characterization of gait by averaging spatial-temporal and surface-EMG-based parameters over numerous strides. Aim of this approach is to characterize the large variability of walking, based on the availability of a great amount of EMG and spatial-temporal data acquired during hundreds of strides during the same walking. This allows to provide deeper information about neuromotor system and its ability to adapting to everyday walking conditions. The research goal is to use SGA to quantify the physiological and pathological condition characterized by high degree of variability, such as muscular co-contraction and synergy and neurological disorders (cerebral palsy in children, Parkinson's disease…), with the aim of assisting and supporting rehabilitation programs.
|Laboratory: Movement Analysis Lab|
|Contact Person: Prof. Sandro Fioretti and Dott. Francesco Di Nardo|