This　research　introduces　innovative　methodologies　to　differentiate　between　the　gait　patterns　of　individuals　with　knee　osteoarthritis　(OA)　and　those　without　symptoms,　a　distinction　achieved　through　the　analysis　of　kinematic　data　dynamics.　The　employed　methodology　involves　the　reconstruction　of　phase　space　representations　from　the　measured　translations　and　rotations　of　the　knee,　thereby　revealing　the　signatures　of　the　underlying　gait　system　through　deterministic　learning　theory.　The　extraction　of　features　is　then　carried　out　using　phase　space　distances　that　amplify　the　differences　between　asymptomatic　and　OA　knee　patterns.　In　particular,　a　three-dimensional　(3D)　phase　space　model　is　developed　in　tandem　with　Euclidean　distance　metrics　that　are　sensitive　to　the　dynamic　changes　induced　by　the　disease.　The　feature　set　derived　from　this　process　is　subsequently　used　as　input　for　classification　models　that　label　new　gait　trial　data.　Experimental　trials　conducted　on　data　from　19　patients　with　OA　and　28　asymptomatic　subjects　yielded　an　accuracy　of　95.7%　and　97.9%　in　two-fold　and　leave-one-subject-out　cross-validation　respectively,　thereby　demonstrating　the　effectiveness　of　these　methods　for　characterization　and　diagnosis.　Comparative　evaluations　further　underscore　the　superior　performance　of　these　methods　over　existing　ones.　In　essence,　this　study　lays　the　groundwork　for　a　dynamic　framework　that　enables　the　data-driven　quantification　and　detection　of　the　impact　of　osteoarthritis　on　human　walking　patterns.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.