Improving the ground reaction force prediction accuracy using one-axis plantar pressure: Expansion of input variable for neural network

Joo, Su-Bin,Oh, Seung Eel,Mun, Joung Hwan Elsevier 2016 Journal of biomechanics Vol.49 No.14

<P><B>Abstract</B></P> <P>In this study, we describe a method to predict 6-axis ground reaction forces based solely on plantar pressure (PP) data obtained from insole type measurement devices free of space limitations. Because only vertical force is calculable from PP data, a wavelet neural network derived from a non-linear mapping function was used to obtain 3-axis ground reaction force in medial-lateral (GRF<SUB>ML</SUB>), anterior-posterior (GRF<SUB>AP</SUB>) and vertical (GRF<SUB>V</SUB>) and 3-axis ground reaction moment in sagittal (GRF<SUB>S</SUB>), frontal (GRF<SUB>F</SUB>) and transverse (GRF<SUB>T</SUB>) data for the remaining axes and planes. As the prediction performance of nonlinear models depends strongly on input variables, in this study, three input variables – accumulated PP with respect to time, center of pressure (COP) pattern, and measurements of the opposite foot, which are calculable only with a PP device – were considered in order to improve prediction performance. To conduct this study, the golf swing motions of 80 subjects were characterized as unilateral movement and GRF patterns as functions of individual characteristics. The prediction model was verified with 5-fold cross-validation utilizing the measured values of two force plates. As a result, prediction model (correlation coefficient, <I>r=</I>0.73–0.97) utilized accumulated PP and PP data of the opposite foot and showed the highest prediction accuracy in left-foot GRF<SUB>V</SUB>, GRM<SUB>F</SUB>, GRM<SUB>T</SUB> and right-foot GRF<SUB>AP</SUB>, GRF<SUB>ML</SUB>, GRM<SUB>F</SUB>, GRM<SUB>T</SUB>. Likewise, another prediction model (<I>r</I>=0.83–0.98) utilized accumulated PP and COP patterns as input and showed the best accuracy in left-foot GRF<SUB>AP</SUB>, GRF<SUB>ML</SUB>, GRM<SUB>S</SUB> and right-foot GRF<SUB>V</SUB>, GRM<SUB>S</SUB>. New methods based on the findings of the present study are expected to help resolve problems such as spatial limitation and limited analyzable motions in existing GRF measurement processes.</P>

The pulsating pressure in the intake and exhaust manifold of a single cylinder engine by the various of engine revolutions

Chung, Han-Shik,Choi, Seuk-Cheun,Jong, Hyo-Min,Lee, Chi-Woo,Kim, Chi-Won The Korean Society of Marine Engineering 2004 한국마린엔지니어링학회지 Vol.28 No.1

In this research, a computer analysis has been developed for predicting the Pipe pressure of the intake and exhaust manifold in a small single cylinder engine. To get the boundary conditions for a numerical analysis one dimensional and unsteady gas dynamic calculation is performed by using the MOC(Method Of Characteristics). The main numerical parameters are engine revolutions. to calculate the Pulsating flow which the intake and exhaust valves are working. The distributions of the exhaust pipe pressures were influenced strongly to the cylinder pressures and the shapes of exhaust pressure variation were similar to the Inside of cylinder pressure As the engine revolutions are increased. the intake pressure was lower than ambient pressure. The amplitude of exhaust pressure had increased and the phase of cylinder pressure $P_c$ is delayed and the amplitude of cylinder pressure were increased.

The Numerical Anylysis for Pressure Prediction in Intake and Exhaust Manifold of a Single Cylinder Engine

( Hyo Min Jeong ),( Han Shik Chung ),( Jong Soo Seo ),( Moung Gook Ji ),( Yong Hun Lee ),( Seuk Cheun Choi ) 경상대학교 해양산업연구소 2003 해양산업연구소보 Vol.16 No.-

Artificial intelligence-based blood pressure prediction using photoplethysmography signals

이용희,주용완,이준동 한국컴퓨터정보학회 2023 韓國컴퓨터情報學會論文誌 Vol.28 No.11

This paper presents a method for predicting blood pressure using the photoplethysmography signals. First, after measuring the optical blood flow signal, artifacts are removed through a preprocessing process, and a signal for learning is obtained. In addition, weight and height, which affect blood pressure, are measured as additional information. Next, a system is built to estimate systolic and diastolic blood pressure by learning the photoplethysmography signals, height, and weight as input variables through an artificial intelligence algorithm. The constructed system predicts the systolic and diastolic blood pressures using the inputs. The proposed method can continuously predict blood pressure in real time by receiving photoplethysmography signals that reflect the state of the heart and blood vessels, and the height and weight of the subject in an unconstrained method. In order to confirm the usefulness of the artificial intelligence-based blood pressure prediction system presented in this study, the usefulness of the results is verified by comparing the measured blood pressure with the predicted blood pressure.

Fluid Infiltration Effect on Breakdown Pressure in Laboratory Hydraulic Fracturing Tests

Melvin B. Diaz,정성규,이경원,김광염 대한지질공학회 2022 지질공학 Vol.32 No.3

Observations on the influence of the fluid infiltration on the breakdown pressure during laboratory hydraulic fracturing tests, along with an analysis of the applicability of the breakdown pressure prediction for cylindrical samples using Quasi-static and Linear Elastic Fracture Mechanics approaches were carried out. These approaches consider fluid infiltration through the so-called radius of fluid infiltration or crack radius, a parameter that is not a material property. Two sets of tests under pressurization rate controlled and injection rate controlled tests were used to evaluate the applicability of these methods. The difficulty of the estimation of the radius of fluid infiltration was solved by back calculating this parameter from an initial set of tests, and later, the obtained relationships were used to predict breakdown pressures for a second set of tests. The results showed better predictions for the injection rate than for the pressurization rate tests, with average errors of 3.4% and 18.6%, respectively. The larger error was attributed to differences in the testing conditions for the pressurization rate tests, which had different applied vertical pressures. On the other hand, for the tests carried out under constant injection rate, the Linear Elastic Fracture Mechanics solution reported lower errors compared to the Quasi-static solution, with values of 3% and 3.8%, respectively. Moreover, a sensitivity analysis illustrated the influence of the radius of fluid penetration or crack radius and the tensile strength on the breakdown pressure, suggesting a need for a careful estimation of these values. Then, the calculation of breakdown pressure considering fluid infiltration in cylindrical samples under triaxial conditions is possible, although larger data sets are desirable to validate and derive better relations.

Prediction of Vertical Pressure in a Trench As Influenced by Soil Arching

홍원표,Meng Leang Bov,김현명 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.7

A series of model tests was performed to investigate the effect of soil arching on the vertical pressure imposed on the bottom of a trench during backfilling. Soil arching can reduce some portion of the vertical pressure generated by the overburdening soil weight in a trench. To determine theoretically the vertical pressure based on the available formula, two unknown factors must be found: (1) the horizontal stress acting on the trench wall, which is related to the lateral earth pressure coefficient, and (2) the wall friction mobilized between the fill soil and the trench wall. However, appropriately determining those two factors is extremely difficult. Therefore, this paper proposes a novel method using the soil arching coefficient to capture the vertical pressure. The soil arching coefficient is here defined as a constant value that depends on neither the internal friction angle of the backfill soil nor the width of the trench. Qualitative and quantitative tests show good agreement between the vertical pressure predicted by the proposed method and experimental data measured in both laboratory and field tests.

The Pressure Prediction in Intake and Exhaust Manifold of a Single Cylinder Engine with Various Exhaust Pipe Diameters

( Han Shik Chung ),( Hyo Min Jeong ),( Kwang Young Lee ),( Young Heung Chun ),( Cheol Jae Lee ),( Seuk Cheun Choi ) 경상대학교 해양산업연구소 2003 해양산업연구소보 Vol.16 No.-

Investigation of Impact of Vapor Pressure on Hybrid Streamflow Prediction Modeling

Hasan Törehan Babacan,Ömer Yüksek,Fatih Saka 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.2

In this study, daily streamflow prediction models have been developed for Aksu Stream, in the Eastern Black Sea Basin of Turkey. To reach at this aim, hybrid artificial intelligence models have been developed, by using a new parameter, vapor pressure. Vapor pressure efficiency has been investigated for hybrid streamflow prediction models. Streamflow prediction models have been developed by using Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), and their hybrid models. Hybridization of streamflow prediction models has been made with Wavelet Transform (WT). 10 yearly daily hydrological (discharge (m³/s)), meteorological (precipitation (mm), vapor pressure (hPA)) data, and seasonality coefficient have been used as input data of streamflow prediction models. In the selection of the best streamflow prediction model, 14 different day-delayed input combinations have been established by using 10 yearly data. As a result of the study, the highest flow forecast performance model has been determined as Wavelet Artificial Neural Network (WANN) in the study area. In the WANN model, the vapor pressure parameter was found to reduce the error by about 18.5% and improve the forecast performance. This study has concluded that, vapor pressure may be used in the future studies as a new parameter for streamflow prediction models.

동요하는 탱크의 내부 변동압력 추정에 관한 연구

이승건(Seung-Keon Lee),서영석(Young-seok Sea) 한국항해항만학회 2003 한국항해항만학회지 Vol.27 No.5

강제 횡동요 실험을 통하여 동요하는 탱크의 내부압력을 측정하고, 탱크의 내부압력을 추정하는 간단한 이론식을 제시하였다. 청수로 채워진 사각 탱크를 동요실험에 사용하였으며, 탱크의 안쪽 벽과 바닥에 압력게이지를 설치하여 내부압력의 시간변화량을 측정하였다. 측정된 탱크의 내부압력의 실험값과 이론식을 이용한 계산값을 비교하였다. 횡동요 하는 탱크내부의 압력을 구하기 위해 압력중심을 고려하여 추정하는 방법을 연구하였다. The inner liquid pressure of an airtight tank in rolling motions is investigated by means of forced oscillation tests, and the simple method to estimate the inner liquid pressure is proposed. A rectangular solid tank, which is fully filled with water, was used in the forced oscillation test of rolling motion. The inner pressure variations in time were measured at several points on the inner walls of tank. Measured pressures are compared with the calculated ones, and estimation methods of the inner liquid pressure of the tank in rolling motion are studied based on the considerations of the origin of pressure.

Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand

Trakulpanitkit Avika,Tunthanathip Thara 대한중환자의학회 2023 Acute and Critical Care Vol.38 No.3

Background: Hydrocephalus (HCP) is one of the most significant concerns in neurosurgical patients because it can cause increased intracranial pressure (ICP), resulting in mortality and morbidity. To date, machine learning (ML) has been helpful in predicting continuous outcomes. The primary objective of the present study was to identify the factors correlated with ICP, while the secondary objective was to compare the predictive performances among linear, non-linear, and ML regression models for ICP prediction. Methods: A total of 412 patients with various types of HCP who had undergone ventriculostomy was retrospectively included in the present study, and intraoperative ICP was recorded following ventricular catheter insertion. Several clinical factors and imaging parameters were analyzed for the relationship with ICP by linear correlation. The predictive performance of ICP was compared among linear, non-linear, and ML regression models. Results: Optic nerve sheath diameter (ONSD) had a moderately positive correlation with ICP (r=0.530, P<0.001), while several ventricular indexes were not statistically significant in correlation with ICP. For prediction of ICP, random forest (RF) and extreme gradient boosting (XGBoost) algorithms had low mean absolute error and root mean square error values and high R2 values compared to linear and non-linear regression when the predictive model included ONSD and ventricular indexes. Conclusions: The XGBoost and RF algorithms are advantageous for predicting preoperative ICP and establishing prognoses for HCP patients. Furthermore, ML-based prediction could be used as a non-invasive method.