The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Kentaro Morimoto - One of the best experts on this subject based on the ideXlab platform.
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estimation of thigh calf contact force during deep knee flexion by Multiple Regression Equation
Orthopaedic Proceedings, 2018Co-Authors: Michihiko Fukunaga, Kentaro MorimotoAbstract:Thigh-calf contact force is the force acting on posterior side of the thigh and calf during deep knee flexion. It has been reported the force is important to analyze the kinetics of a lower limb an...
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estimation of thigh calf contact force during deep knee flexion by Multiple Regression Equation
Journal of Bone and Joint Surgery-british Volume, 2017Co-Authors: Michihiko Fukunaga, Kentaro MorimotoAbstract:Thigh-calf contact force is the force acting on posterior side of the thigh and calf during deep knee flexion. It has been reported the force is important to analyze the kinetics of a lower limb and a knee joint. Some previous researches reported the measured thigh-calf contact force, however, the values varied among the reports. Furthermore, the reports indicated that there were large variations even in a single report. One of the reports tried to find the relationship between the magnitude of thigh-calf contact force and anthropometric measurement as height, weight or perimeter of the lower limb, however, there could not found clear correlations. We considered that the cause of the variations might be the difference of the posture. At heel-rise squatting posture, we can bend or stand upright the upper body. Therefore we tried to create the Equation to estimate the thigh-calf contact force by Multiple Regression analysis, using the anthropometric and posture parameters as explanatory variables. We performed the experiment to measure thigh-calf contact force, joint angles and anthropometric information. Test subjects were 10 healthy male. First we measured their height, weight, perimeter of the thigh and muscle mass of the legs and whole body. Muscle mass was measured by body composition meter (BC-118E, Tanita Co., Japan). Then, test subjects were asked to squat with their heels lifted and with putting the pressure distribution sensor between thigh and calf. And they bent their upper body forward and backward. The pressure sensor to be used was ConfroMat System (Nitta Co., Japan). After that, we measured the joint angles of the hip, knee and ankle, and the angle between the floor and upper body using the videos taken during the experiment. Then, we created the Equation to estimate the thigh-calf contact force by linear combination of the anthropometric values and joint angles. The coefficients were settled as to minimize the average error between measured and estimated values. Results are shown in Fig.1. Forces were normalized by the body weight of the test subjects. Because the horizontal axes show the measured and vertical axis show the estimated values, the estimation is accurate when the plots are near the 45-degree line. Average error was 0.11BW by using only physical values, 0.15BW by angles and 0.06BW using both values. And the maximum error was 0.69BW, 0.43BW and 0.32BW respectively. Thus we could estimate the thigh-calf contact force by Multiple Regressions, using both physical parameters and angles to indicate the posture. Using the Equation, we would be able to analyze the kinetics of a lower limb by physical and motion measurement. Our future work might be increasing the number of subjects to consider the appropriateness, because the test subjects of this study were very limited.
Michihiko Fukunaga - One of the best experts on this subject based on the ideXlab platform.
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estimation of thigh calf contact force during deep knee flexion by Multiple Regression Equation
Orthopaedic Proceedings, 2018Co-Authors: Michihiko Fukunaga, Kentaro MorimotoAbstract:Thigh-calf contact force is the force acting on posterior side of the thigh and calf during deep knee flexion. It has been reported the force is important to analyze the kinetics of a lower limb an...
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estimation of thigh calf contact force during deep knee flexion by Multiple Regression Equation
Journal of Bone and Joint Surgery-british Volume, 2017Co-Authors: Michihiko Fukunaga, Kentaro MorimotoAbstract:Thigh-calf contact force is the force acting on posterior side of the thigh and calf during deep knee flexion. It has been reported the force is important to analyze the kinetics of a lower limb and a knee joint. Some previous researches reported the measured thigh-calf contact force, however, the values varied among the reports. Furthermore, the reports indicated that there were large variations even in a single report. One of the reports tried to find the relationship between the magnitude of thigh-calf contact force and anthropometric measurement as height, weight or perimeter of the lower limb, however, there could not found clear correlations. We considered that the cause of the variations might be the difference of the posture. At heel-rise squatting posture, we can bend or stand upright the upper body. Therefore we tried to create the Equation to estimate the thigh-calf contact force by Multiple Regression analysis, using the anthropometric and posture parameters as explanatory variables. We performed the experiment to measure thigh-calf contact force, joint angles and anthropometric information. Test subjects were 10 healthy male. First we measured their height, weight, perimeter of the thigh and muscle mass of the legs and whole body. Muscle mass was measured by body composition meter (BC-118E, Tanita Co., Japan). Then, test subjects were asked to squat with their heels lifted and with putting the pressure distribution sensor between thigh and calf. And they bent their upper body forward and backward. The pressure sensor to be used was ConfroMat System (Nitta Co., Japan). After that, we measured the joint angles of the hip, knee and ankle, and the angle between the floor and upper body using the videos taken during the experiment. Then, we created the Equation to estimate the thigh-calf contact force by linear combination of the anthropometric values and joint angles. The coefficients were settled as to minimize the average error between measured and estimated values. Results are shown in Fig.1. Forces were normalized by the body weight of the test subjects. Because the horizontal axes show the measured and vertical axis show the estimated values, the estimation is accurate when the plots are near the 45-degree line. Average error was 0.11BW by using only physical values, 0.15BW by angles and 0.06BW using both values. And the maximum error was 0.69BW, 0.43BW and 0.32BW respectively. Thus we could estimate the thigh-calf contact force by Multiple Regressions, using both physical parameters and angles to indicate the posture. Using the Equation, we would be able to analyze the kinetics of a lower limb by physical and motion measurement. Our future work might be increasing the number of subjects to consider the appropriateness, because the test subjects of this study were very limited.
Rt Withers - One of the best experts on this subject based on the ideXlab platform.
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Predicting the resting metabolic rate of 30–60-year-old Australian males
European Journal of Clinical Nutrition, 2002Co-Authors: Ge Van Der Ploeg, Rt WithersAbstract:Objectives: This study: (a) generated Regression Equations for predicting the resting metabolic rate (RMR) of 30–60-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validated RMR prediction Equations which are currently used in Australia against our measured and predicted values. Design: A power analysis demonstrated that 41 subjects would enable the detection of (α=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the Equations of other investigators. Subjects: Forty-one males (X̄±s.d.:, 44.8±8.6 y; 83.50±11.32 kg; 179.1±5.0 cm) were recruited for this study. Interventions: The following variables were measured: skinfold thicknesses; RMR using open circuit indirect calorimetry; and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A Multiple Regression Equation using mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model ( r =0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses ( r= 0.805, s.e.e.=441 kJ/day). Conclusions: Cross-validation analyses emphasised that Equations need to be generated from a large database for the prediction of the RMR of 30–60-y-old Australian males. Sponsorship: Australian Research Council (small grants scheme).
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Predicting the resting metabolic rate of young Australian males
European Journal of Clinical Nutrition, 2001Co-Authors: Ge Van Der Ploeg, Rt Withers, Sm Gunn, Ac Modra, Jp Keeves, Be ChattertonAbstract:Objectives: The aims of this study were: (a) to generate Regression Equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validate RMR prediction Equations, which are frequently used in Australia, against our measured and predicted values. Design: A power analysis demonstrated that 38 subjects would enable us to detect (α=0.05, power=0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the Equations of other investigators. Subjects: Thirty-eight males (X̄±s.d.: 24.3±3.3 y; 85.04±13.82 kg; 180.6±8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. Interventions: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A Multiple Regression Equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/day. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model ( R =0.893, SEE=433 kJ/day) and that predicted from skinfold thicknesses ( R =0.886, SEE=440 kJ/day). The Regression Equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518–600 kJ/day ( P
Ge Van Der Ploeg - One of the best experts on this subject based on the ideXlab platform.
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Predicting the resting metabolic rate of 30–60-year-old Australian males
European Journal of Clinical Nutrition, 2002Co-Authors: Ge Van Der Ploeg, Rt WithersAbstract:Objectives: This study: (a) generated Regression Equations for predicting the resting metabolic rate (RMR) of 30–60-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validated RMR prediction Equations which are currently used in Australia against our measured and predicted values. Design: A power analysis demonstrated that 41 subjects would enable the detection of (α=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the Equations of other investigators. Subjects: Forty-one males (X̄±s.d.:, 44.8±8.6 y; 83.50±11.32 kg; 179.1±5.0 cm) were recruited for this study. Interventions: The following variables were measured: skinfold thicknesses; RMR using open circuit indirect calorimetry; and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A Multiple Regression Equation using mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model ( r =0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses ( r= 0.805, s.e.e.=441 kJ/day). Conclusions: Cross-validation analyses emphasised that Equations need to be generated from a large database for the prediction of the RMR of 30–60-y-old Australian males. Sponsorship: Australian Research Council (small grants scheme).
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Predicting the resting metabolic rate of young Australian males
European Journal of Clinical Nutrition, 2001Co-Authors: Ge Van Der Ploeg, Rt Withers, Sm Gunn, Ac Modra, Jp Keeves, Be ChattertonAbstract:Objectives: The aims of this study were: (a) to generate Regression Equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validate RMR prediction Equations, which are frequently used in Australia, against our measured and predicted values. Design: A power analysis demonstrated that 38 subjects would enable us to detect (α=0.05, power=0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the Equations of other investigators. Subjects: Thirty-eight males (X̄±s.d.: 24.3±3.3 y; 85.04±13.82 kg; 180.6±8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. Interventions: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A Multiple Regression Equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/day. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model ( R =0.893, SEE=433 kJ/day) and that predicted from skinfold thicknesses ( R =0.886, SEE=440 kJ/day). The Regression Equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518–600 kJ/day ( P
Wenji Zhao - One of the best experts on this subject based on the ideXlab platform.
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a damage assessment model of oil spill accident combining historical data and satellite remote sensing information a case study in penglai 19 3 oil spill accident of china
Marine Pollution Bulletin, 2015Co-Authors: Zhuowei Hu, Lin Dong, Wenji ZhaoAbstract:Abstract Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A Multiple Regression Equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment.
