Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network

2021 ◽  
pp. 175433712110141
Author(s):  
Karim BenSiSaid ◽  
Noureddine Ababou ◽  
Amina Ababou ◽  
Daniel Roth ◽  
Sebastian von Mammen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Angular Displacement ◽  
Human Motion ◽  
Mean Error ◽  
Displacement Sensors ◽  
Marquardt Algorithm ◽  
Flexion Extension ◽  
Body Sensors ◽  
Artificial Neural

Human motion tracking is an active field of research driven by its diverse applications in areas such as health care, daily activity recognition, sports, etc. In sports applications, tracking rowing motion is performed to meet several goals, including prevention of injuries, improvement of performance or provision of virtual coaching. Different established approaches rely on on-body sensors to capture rowing motion. While on-body sensors are effective and straight forward to implement, they can disturb the athlete and negatively impact the training. In this paper, an approach is presented to track rowing motion without body-worn sensors or cameras. Instead, sensors were attached to an indoor rowing machine that tracked the motion of its sliding seat, lever handle and the force exercised on the seat. In particular, the motion was tracked by means of linear and angular displacement sensors, as well as force sensors placed underneath the seat. The respective variables were fed into an Artificial Neural Network (ANN) to predict the coordinates of the rower’s shoulder, which in turn, were used to geometrically infer the angles of the shoulder, elbow, hip and thoracolumbar flexion-extension. A successful ANN architecture was iteratively designed by using the Levenberg-Marquardt algorithm and varying the number of hidden neurons in one hidden layer. A comparison between ANN-predicted and experimentally obtained shoulder coordinates from an optical motion capture system showed a mean error of less than 4 cm, which led to an angle mean error value as low as 2.01°. A rigged avatar was used to visually verify the reproduced motion. The avatar animation was well-received by experts, especially considering the shoulder adduction-abduction in the frontal plane.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

scholarly journals Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 97 ◽  
Author(s):  
Kelvin López-Aguilar ◽  
Adalberto Benavides-Mendoza ◽  
Susana González-Morales ◽  
Antonio Juárez-Maldonado ◽  
Pamela Chiñas-Sánchez ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dry Matter ◽  
Fruit Yield ◽  
Fresh Fruit ◽  
Neural Network Modeling ◽  
Peat Moss ◽  
Tomato Crop ◽  
Marquardt Algorithm ◽  
Artificial Neural

Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

scholarly journals Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW

2019 ◽  
Vol 269 ◽  
pp. 04004
Author(s):  
Fuad Mahfudianto ◽  
Eakkachai Warinsiriruk ◽  
Sutep Joy-A-Ka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Back Propagation ◽  
Welding Current ◽  
Travel Speed ◽  
Mean Square ◽  
Wire Electrode ◽  
Marquardt Algorithm ◽  
Artificial Neural

A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).

A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2756-2760 ◽  
Author(s):  
Goh Lyn Dee ◽  
Norhisham Bakhary ◽  
Azlan Abdul Rahman ◽  
Baderul Hisham Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Conjugate Gradient ◽  
Learning Algorithms ◽  
Mode Shapes ◽  
Neural Network Learning ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.

scholarly journals The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

scholarly journals An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sehmus Fidan ◽  
Hasan Oktay ◽  
Suleyman Polat ◽  
Sarper Ozturk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Properties ◽  
Thermal Property ◽  
Building Materials ◽  
Performance Indicator ◽  
Mechanical And Thermal Properties ◽  
Activation Functions ◽  
Marquardt Algorithm ◽  
Artificial Neural

Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.

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