One-Step Gait Pattern Analysis of Hip Osteoarthritis Patients Based on Dynamic Time Warping through Ground Reaction Force

• Published in: Applied sciences 2023-04, Vol.13 (8), p.4665
• Main Authors: Ahn, Sohyun, Choi, Wiha, Jeong, Hieyong, Oh, Sehoon, Jung, Tae-Du
• Format: Article
• Language: English
• Subjects: abnormal detection; Abnormalities; Algorithms; Arthritis; Artificial intelligence; Body mass index; Data analysis; Data mining; Diagnosis; Discriminant analysis; dynamic time warping; Force plates; Gait; Gender; Health aspects; Heels; hip osteoarthritis; Human subjects; Joint diseases; Machine learning; Methods; one-step gait pattern; Osteoarthritis; Pain; Patients; Pattern analysis; Supervised learning; Surgery; Time series; Walking; Warping
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13084665

Osteoarthritis (OA) of the hip is a degenerative joint disease, which means it causes gradual damage to the joint, and its incidence rate continues to increase worldwide. Degenerative osteoarthritis can cause significant pain and gait disturbance in walking, affecting daily life. A diagnosis method for hip OA includes questioning and various walking movements to find abnormalities of gait patterns based on human observation. However, when multiple gait tests are performed to notice the gait, it can cause pain continuously, even during the examination. Suppose hip OA could be diagnosed with only a one-step gait; both patients and medical doctors would be benefited because the diagnosis time can be reduced and the burden on the patient is decreased dramatically. Therefore, in this paper, we aimed to propose a method to recognize the abnormality of the hip OA patient with a one-step gait pattern based on a dynamic time warping (DTW) algorithm through three directional ground reaction forces (GRFs). After a force plate measured three directional GRFs, the data of twenty-three hip OA patients and eighteen healthy people were classified using supervised machine learning algorithms. The results of the classification showed high accuracy and reliability. Then, the DTW algorithm was applied to compare the data of patients and healthy people to find out when patients may feel pain during the gait. By applying the DTW algorithm, it was possible to find out in which gait phase the patient’s gait showed the difference, such as when the heel first contacted the ground, in the middle of walking, or when the toe came off the ground. Through the results, the data of the one-step gait on the force plate enabled us to classify patients and healthy people with a high accuracy of over 70%, recognize the abnormal gait pattern, and determine how to relieve the pain during the gait.