## 5 Conclusions  
Using machine learning algorithms, we classified the one-step gait data of hip OA patients and healthy people. Then, we analyzed the patients' one-step gait data by finding the difference between each patient's gait and that of healthy people using the DTW algorithm. With the classification results through machine learning, we concluded that it could be discriminated with only a one-step gait. This finding could shorten the time compared to conventional diagnosis, even for patients with pain, reducing the burden on the patient and allowing doctors to follow up more quickly. Therefore, it was adequate for both doctors and patients. In addition, in the DTW matrix, it was possible to check at which stage the patient's data differs significantly from that of healthy people. As a result of the analysis, the gait patterns of patients with hip OA were slightly similar to or significantly different from those of healthy people according to their abnormalities so that we could see various data different from those of healthy people. If doctors see a patient's data using this method, it can be used for examination and gait patterns. It is also possible to numerically compare the degree of recovery through rehabilitation training to see if the training is going well. Several studies have shown that the pain and function reported by patients following surgery are better in patients who receive early referral and treatment before the joint's functional limitation and pain are severe [20,21]. Accordingly, if a one-step gait reduces the burden on the patient and allows for a quick diagnosis of hip OA, inducing early treatment, prevention, and early management is possible before the symptoms worsen, which will help the patient's recovery. The values allowed us to know in which phase there was a difference. Conceptualization, H.J., S.O. and T.-D.J. ; methodology, S.A. and W.C.; software, S.A.; validation, S.A., W.C., H.J., S.O. and T.-D.J. ; formal analysis, S.A.; investigation, S.A. and W.C.; resources, S.O. and T.-D.J. ; data curation, W.C.; writing--original draft preparation, S.A. and H.J. ; writing--review and editing, H.J. ; visualization, S.A.; supervision, H.J., S.O. and T.-D.J. ; project administration, H.J. ; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (20220596, Development of Digital Flow-through Aquaculture System). All subjects gave their informed consent for inclusion before participating in the study. The study was conducted under the Declaration of Helsinki. The Ethics Committee of the Clinical Trial Center at the Kyungpook National University Hospital in the Republic of Korea approved the protocol. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: You can download the dataset at [[https://drive.google.com/drive/folders/laijKxEalrzbDUV-qvCAPVbcmUfoDB1](https://drive.google.com/drive/folders/laijKxEalrzbDUV-qvCAPVbcmUfoDB1) mx?usp=share_link]([https://drive.google.com/drive/folders/laijKxEalrzbDUV-qvCAPVbcmUfoDB1](https://drive.google.com/drive/folders/laijKxEalrzbDUV-qvCAPVbcmUfoDB1) mx?usp=share_link) (accessed on 5 April 2023). The authors would like to thank the BK21 Plus program at Chonnam National University through the National Research Foundation, funded by the Ministry of Education of Korea. This research was supported by the project for Development of Customized Rehabilitation Protocol based on Gait Analysis in Patients with Total Hip Arthroplasty (No. 2018-05-008), and by the Basic Science Research Program through the National Research Foundation (NRF) of Korea grant, funded by the Ministry of Education (NRF-2021R1I1A3055210). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: