Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier.
The aim of this work is to develop an automatic computer method to distinguish between asymptomatic (AS) and osteoarthritis (OA) knee gait patterns using 3-D ground reaction force (GRF) measurements. GRF features are first extracted from the force vector variations as a function of time and then classified by the nearest neighbor rule. We investigated two different features: the coefficients of a polynomial expansion and the coefficients of a wavelet decomposition. We also analyzed the impact of each GRF component (vertical, anteroposterior, and medial lateral) on classification. The best discrimination rate (91%) was achieved with the wavelet decomposition using the anteroposterior and the medial lateral components. These results demonstrate the validity of the representation and the classifier for automatic classification of AS and OA knee gait patterns. They also highlight the relevance of the anteroposterior and medial lateral force components in gait pattern classification.
Mezghani
Neila
N
Laboratoire de Recherche en Imagerie et Orthopédie, Ecole de Technologie Supérieure of Montreal, Centre de Recherche du CHUM, Hôpital Notre-Dame, 1560, rue Sherbrooke Est, local Y-1615, Montréal, QC H2L 4M1, Canada. neila.mezghani@etsmtl.ca
Husse
Sabine
S
Boivin
Karine
K
Turcot
Katia
K
Aissaoui
Rachid
R
Hagemeister
Nicola
N
de Guise
Jacques A
JA
D000465:Algorithms; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005684:Gait; D006801:Humans; D007719:Knee Joint / Q000503:physiopathology*; D020370:Osteoarthritis, Knee / Q000175:diagnosis* / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010808:Physical Examination / Q000379:methods*