ScholarWorks@중앙대학교

초록

The This study included 33 male and female college students aged 20 or older located in Seoul. Those who had been diagnosed with musculoskeletal disorders in the past 6 months or had experience with musculoskeletal surgery were excluded from this study. Non-face-to-face AI Home training while wearing an insole made with customized artificial intelligence/11 people (9 men, 2 women), not exercising/11 people (6 men, 5 women), not wearing an insole/11 people (male) The subjects of the study were selected by dividing them into 3 groups (5 people, 6 women). The data from the research results were analyzed using the SPSS (Version 20.0) statistical program, and the general characteristics of the research subjects were analyzed using frequency analysis and descriptive statistics. The mean (M) and standard deviation (SD) of all variables were calculated. To determine the degree of improvement in weight distribution index and detection angle of the exercise program, the differences between before and after within each group were analyzed using a paired t-test. To identify the relationship between each variable, analysis was performed using Pearson's Correlation. The significance level for all statistical analyzes was set at α=.05. Looking at the results before and after walking in the insole wearing group, a significant difference was found in the front and rear weight distribution index. Also, in the group that wore the insole and performed home training, the weight distribution index of the contact left and right feet, There was a significant difference in the weight distribution index of the right foot in Midstance and the weight distribution index of the left and right feet in Propulsion. There was also a significant difference in plantar pressure between the left and right weight distribution indices. This showed that there was a change in gait and plantar pressure in the group that wore customized insoles compared to the group that did not wear them.

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