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Seokhan Lee,권영근,Nojin Lee,Keun-Joo Bae,Jihyun Kim,Seokwon Park,Yang Hyun Kim,Kyung-Hwan Cho 대한가정의학회 2019 Korean Journal of Family Medicine Vol.40 No.3
Background: Knee osteoarthritis is highly prevalent, especially among the elderly. However, its risk factors have not been well identified, especially in the Korean population. This study aimed to assess the epidemiologic characteris-tics and risk factors of knee osteoarthritis in the Korean population.Methods: Data of 2,280 (1,295 women) participants of the 2013 Korea National Health and Nutrition Examination Survey aged ≥50 years who underwent knee radiography were analyzed. According to the American College of Rheumatology clinical/radiographic classification criteria, knee osteoarthritis was defined as knee pain and radio-graphic knee osteoarthritis. The association between risk factors and knee osteoarthritis was analyzed using the chi-square test and binominal logistic regression.Results: The participants had an average age of 62.6 years; 56.8% of them were women. The average body mass in-dex was 24.2 kg/m2, and 296 (13%) participants were diagnosed with knee osteoarthritis. After adjustment for mul-tiple risk factors, age of ≥65 years (adjusted odds ratio [OR], 2.552; 95% confidence interval [CI], 1.868–3.486), fe-male sex (OR, 2.050; 95% CI, 1.275–3.295), obesity (body mass index, ≥25.0 kg/m2; OR, 1.563; 95% CI, 1.191–2.051), hypertension (OR, 1.394; 95% CI, 1.052–1.846), low educational level (lower than or equal to elementary school: OR, 4.761; 95% CI, 2.131–10.635; middle school: OR, 3.184; 95% CI, 1.375–7.369), and low strength exercise frequen-cy (<2 times/wk; OR, 1.829; 95% CI, 1.202–2.784) increased the risk of knee osteoarthritis.Conclusion: Old age, sex, obesity, hypertension, low educational level, and low strength exercise frequency were found to be risk factors for knee osteoarthritis.
Estimation of Human Height and Position using a Single Camera
Seokhan Lee,Jongsoo Choi 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
In this paper, we propose a single view-based technique for real-time estimation of human height and position. Unlike other existing single view-based methods, our method allows real-time acquisition of motion trajectory of a moving object as well as its height with high accuracy. Moreover, as the projective camera mapping is estimated simply by using a marker, our method is applicable even in the absence of geometric cues. The accuracy and robustness of our technique is verified on the experimental results of several real video sequences from outdoor environments.