http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
김판권,오유근,Choongsoo S. Shin 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.10
We investigated 3D kinematic and kinetic changes of knee and ankle during downhill walking as the slope angle increased and evaluated biomechanical injury risk factors related to non-contact ACL injury. Fifteen male subjects performed level walking and 15° and 25° downhill walking. For the kinetic and kinematic parameters, one-way ANOVA and post-hoc tests were performed at a significance level of 0.05. This study revealed significant differences in 3D knee and ankle kinematics and kinetics among 0°, 15° and 25° downhill walking. The peak posterior ground reaction force, the peak knee anterior force and the knee valgus moment (0° vs. 15°: p < 0.05; 0° vs. 25°:p < 0.05) in the early stance phase increased as the slope angle increased. The peak knee internal tibial rotation moments in the late stance also increased (0° vs. 15°: p < 0.05; 0° vs. 25°: p < 0.05; 15° and 25°: p < 0.05) as the slope angle increased. These results showed the risk for ACL injuries may be increased during downhill walking with a greater slope angle.
김판권(Pankwon Kim),이진규(Jinkyu Lee),연규필(Kyupil Yeon),신충수(Choongsoo S. Shin) 한국자료분석학회 2018 Journal of the Korean Data Analysis Society Vol.20 No.1
본 연구의 목적은 족저압을 이용하여 평지보행과 평지에서 계단하강으로 전환하는 보행을 구분하는 검출 알고리즘을 개발하고 검증하는 것이다. 피실험자는 각각 평지보행과 평지에서 계단하강 전환보행을 수행하였다. 족저압을 기초로 하여 각 동작 별로 발의 접촉시간, 초기 접촉시 내외측 압력중심, 전후방 압력중심, 내외측 압력중심의 이동범위, 전후방 압력중심의 이동범위를 계산하여 보행환경을 판별하는 설명변수로 활용하였다. 각 설명변수에 대한 상자그림과 대응표본 t-검정을 수행한 결과, 발의 접촉시간, 초기 접촉 시 내외측 및 전후방 압력중심, 그리고 전후방 압력중심의 이동범위가 보행환경 간 유의한 평균 차이를 보였다(p<0.001). 이러한 설명변수들을 이용하여 평지보행과 계단하강 전환보행을 판별하는 로지스틱 회귀모형을 생성하고 정확도, 민감도, 특이도 등을 평가하였다. 이 모형에 의한 보행환경 분류 결과는 정확도, 민감도, 특이도가 각각 98.2%, 100% 그리고 96.4%로 나타났다. 결론적으로 로지스틱 회귀모형을 이용하여 생성한 모델은 높은 정확도로 평지보행과 평지에서 계단하강 전환보행을 구분해 낼 수 있었다. 본 연구에서 개발한 보행환경 구분 모형을 추후 보행보조장치에 적용하면 보행환경 식별 능력을 향상시킬 수 있을 것으로 기대된다. The purpose of this study was to develop and validate a detection algorithm that distinguishes between the level walking (LW) and the transition walking from level to stair descent (TW-LSD) based on plantar pressure. Subjects performed LW and TW-LSD. Based on the plantar pressure, the stance time, vertical ground reaction force, anteroposterior (AP) and mediolateral (ML) center of pressure (COP) at initial contact (IC) and AP/ML range of COP were calculated for explanatory variables. After analyzing the results of boxplot and paired t-test for the explanatory variables, the stance time, the ML and AP COP at IC, and AP range of COP were significantly different between LW and TW-LSD (all, p<0.001). Logistic regression models were generated using response variables and explanatory variables. Accuracy, sensitivity, and specificity were 98.2%, 100%, and 96.4%, respectively, as the result of walking condition classification using this model. In conclusion, the model generated by the logistic regression method could distinguish between the LW and TW-LSD with high accuracy.
김판권(Pankwon Kim),이진규(Jinkyu Lee),신충수(Choongsoo S. Shin) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
The purpose of this study was to develop a detection algorithm for classification of level walking (LW) and transition walking from level-to-stair (TW-LS) based on plantar pressure. Thirteen subjects performed LW, transition walking from level to stair ascent (TW-LSA) and transition walking from level to stair descent (TW-LSD). The stance time, vertical ground reaction force (vGRF), anteroposterior (AP) and mediolateral (ML) center of pressure (COP) at initial contact (IC) and AP/ ML range of COP were calculated based on plantar pressure during stance phase. The data were evaluated statistically by one-way analysis of variance (ANOVA) and post-hoc comparison was performed at significance level of 0.05. The stance time, AP COP at IC and AP range of COP were significantly different in the comparison among three walking conditions (all, p<0.001). The ML COP at IC was significantly different when TWLSA and TW-LSD were compared by LW (both, p<0.05). The peak vGRF was significantly different when LW and TW-LSD were compared by TW-LSA (both, p<0.05). The multinomial logistic regression models were developed with various combinations of parameters based on comparison results. The accuracy of the model was the highest when the combination of stance time, AP COP at IC and AP range of COP were used. As the result, the probability of classifying LW and TW-LS was 0.952. In conclusion, the multinomial logistic regression model based on plantar pressure can be used for classifying LW and TW-LS with high accuracy.