Measuring the performance of knee implants is a difficult task, since most of the evaluations include subjective components.
MIC promoted the development of a gait analysis system based on automatic image processing. A set-up with six synchronized cameras was used (see figure below) to record gait from pre-operatory, post-operatory and healthy subjects. Our team developed algorithms to identify the markers during the gait and perform anatomical measurements that characterize the patient’s gait patterns.
Arrangement of cameras around the recording area.
A cheap and easy-to-build set-up, and a new polar-based gait representation resulted from this project, where 166 subjects were analyzed. Gait patterns were extracted with artificial neural networks, and the study demonstrated the convenience of knee implants, since post-operatory patients showed patterns closer to a healthy gait, than pre-operatory ones.
Placement of markers on bony landmarks, and scenario for gait analysis.