Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

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A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

Journal of Energy Resources Technology ◽

10.1115/1.4045543 ◽

2019 ◽

Vol 142 (5) ◽

Author(s):

Byeongho Yu ◽

Dongsu Kim ◽

Heejin Cho ◽

Pedro Mago

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Nonlinear System ◽

Prediction Model ◽

Thermal Load ◽

Ann Model ◽

Load Prediction ◽

Artificial Neural ◽

Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

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Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

Atmosphere ◽

10.3390/atmos11121303 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1303

Author(s):

Wei-Ting Hung ◽

Cheng-Hsuan (Sarah) Lu ◽

Stefano Alessandrini ◽

Rajesh Kumar ◽

Chin-An Lin

Keyword(s):

Machine Learning ◽

New York ◽

New York State ◽

Vertical Mixing ◽

Model Performance ◽

Machine Learning Techniques ◽

Joint Analysis ◽

Ann Model ◽

York State ◽

Ann Models

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

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Modeling of methane emissions using artificial neural network approach

Journal of the Serbian Chemical Society ◽

10.2298/jsc020414110s ◽

2015 ◽

Vol 80 (3) ◽

pp. 421-433 ◽

Cited By ~ 4

Author(s):

Lidija Stamenkovic ◽

Davor Antanasijevic ◽

Mirjana Ristic ◽

Aleksandra Peric-Grujic ◽

Viktor Pocajt

Keyword(s):

Neural Network ◽

National Level ◽

Model Performance ◽

General Regression Neural Network ◽

Annual Data ◽

Ann Model ◽

Neural Network Approach ◽

Ch4 Emissions ◽

Artificial Neural ◽

Mlr Model

The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN) and a General Regression Neural Network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies.

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The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

BALTIC FORESTRY ◽

10.46490/bf422 ◽

2021 ◽

Vol 27 (2) ◽

Author(s):

Şükrü Özşahin ◽

Hilal Singer

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Model Performance ◽

Coefficient Of Determination ◽

Densified Wood ◽

Percentage Error ◽

Angle Of Incidence ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

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Deterministic Modeling to Predict the Natural Gas Density Using Artificial Neural Networks

10.2118/204608-ms ◽

2021 ◽

Author(s):

Mariam Shreif ◽

Shams Kalam ◽

Mohammad Rasheed Khan ◽

Rizwan Ahmed Khan

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Natural Gas ◽

Fuzzy Inference ◽

Ann Model ◽

Data Set ◽

Inference System ◽

Gas Density ◽

Neuro Fuzzy ◽

Artificial Neural

Abstract During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.

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Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-5079 ◽

2019 ◽

Author(s):

Ananda Kumar ◽

B Maheshwara Babu ◽

U Satish Kumar ◽

G.V Srinivasa Reddy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Groundwater Level ◽

Model Performance ◽

Substantial Improvement ◽

Ann Model ◽

Groundwater Level Fluctuations ◽

Calibration And Validation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model.

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Ensemble machine learning approach for classification of IoT devices in smart home

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-020-01241-0 ◽

2021 ◽

Author(s):

Ivan Cvitić ◽

Dragan Peraković ◽

Marko Periša ◽

Brij Gupta

Keyword(s):

Machine Learning ◽

Smart Home ◽

Technological Development ◽

Model Performance ◽

Classification Model ◽

Supervised Machine Learning ◽

Positive Ratio ◽

Ensemble Machine Learning ◽

Industrial Iot ◽

Iot Devices

AbstractThe emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

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Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-2-365-2005 ◽

2005 ◽

Vol 2 (1) ◽

pp. 365-415 ◽

Cited By ~ 32

Author(s):

N. J. de Vos ◽

T. H. M. Rientjes

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Goodness Of Fit ◽

Model Performance ◽

Performance Measure ◽

Rainfall Runoff ◽

Ann Model ◽

Trade Offs ◽

Hourly Data ◽

Artificial Neural

Abstract. The application of Artificial Neural Networks (ANNs) on rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling in the Geer catchment (Belgium) using both daily and hourly data. The good daily forecast results indicate that ANNs can be considered alternatives for traditional rainfall-runoff modelling approaches. However, investigation of the forecasts based on hourly data reveal a constraint that has hitherto been neglected by hydrologists. A timing error occurs due to a dominating autoregressive component that is introduced by using previous runoff values as ANN model input. The reason for the popular practice of using these previous runoff data is that this information indirectly represents the hydrological state of the catchment. Two possible solutions to this timing problem are discussed. Firstly, several alternatives for representation of the hydrological state are presented: moving averages over the previous discharge and over the previous rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these various hydrological state representators produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the use of a combination of multiple measures of model performance during ANN training is suggested, since not all differences between modelled and observed hydrograph characteristics such as timing, volume, and absolute values can be adequately expressed by a single performance measure. The possible undervaluation of timing errors by the commonly-used squared-error-based functions is a clear example of this inability.

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Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

Vietnam Journal of Computer Science ◽

10.1142/s2196888819500246 ◽

2019 ◽

Vol 06 (04) ◽

pp. 439-455 ◽

Cited By ~ 1

Author(s):

Nahian Ahmed ◽

Nazmul Alam Diptu ◽

M. Sakil Khan Shadhin ◽

M. Abrar Fahim Jaki ◽

M. Ferdous Hasan ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Satellite Image ◽

Population Estimation ◽

Data Driven ◽

Gradient Boosting ◽

Ann Model ◽

Data Driven Approach ◽

Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

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Comparison of two pixelated insoles using in-shoe pressure sensors to determine percent offloading: case studies

Journal of Wound Care ◽

10.12968/jowc.2020.29.sup2c.s18 ◽

2020 ◽

Vol 29 (Sup2c) ◽

pp. S18-S26

Author(s):

Harry Penny ◽

Son Tran ◽

Laura Sansosti ◽

Steven Pettineo ◽

Andrew Bloom ◽

...

Keyword(s):

Edge Effect ◽

Plantar Pressure ◽

Pressure Sensors ◽

Metatarsal Head ◽

Percentage Reduction ◽

Total Contact Cast ◽

First Metatarsal ◽

Plantar Pressures ◽

Significant Edge ◽

Pressure Changes

Objective: The gold standard for offloading neuropathic forefoot and midfoot wounds is the total contact cast (TCC). However, in practice TCC is rarely used and is contraindicated in patients with fluctuating oedema, poor perfusion, lack of adequate tissue oxygenation and morbid obesity. It can also be too restrictive for patients, inevitably resulting in treatment rejection and delayed healing. This paper examines the role of shoe-based offloading devices as an alternative in reducing plantar pressure and optimising the healing of neuropathic ulcers. Method: Healthy subjects were recruited and fitted for two types of pixelated insoles: PegAssist (PA) insole system (Darco International, US) and FORS-15 (FORS) offloading insole (Saluber, Italy). An area of discreet, elevated high pressure was created by adding a 1/4-inch-thick felt pad to the plantar skin under the first metatarsal head. Subjects walked barefoot in surgical shoes with standard insoles (Condition 1), barefoot in pixelated insoles (Condition 2), barefoot with pixels removed (Condition 3). Dynamic plantar pressures were measured using F-Scan and the results were analysed to determine plantar pressure changes in each condition. Results: Using PA, the percentage reduction of plantar pressure (kPa) under the first metatarsal between Condition 1 and Condition 2 was 10.54±15.81% (p=0.022), between Condition 2 and Condition 3 was 40.13±11.11% (p<0.001), and between Condition 1 and Condition 3 was 46.67±12.95 % (p<0.001). Using FORS, the percentage reduction between Condition 1 and Condition 2 was 24.25±23.33% (p=0.0029), between Condition 2 and Condition 3 was 23.61±19.45% (p<0.001), and between Condition 1 and Condition 3 was 43.39±18.70% (p<0.001). A notable difference in the findings between the two insoles was the presence of a significant edge effect associated with PA, indicating that the offloading was not entirely successful. No edge effect was detected with FORS. Conclusion: Our current analysis shows that pixelated insoles exhibit potential for supplemental offloading in surgical shoes. These devices could provide an alternative way for physicians to offload plantar wounds and expedite closure for patients that cannot tolerate a TCC or other restrictive devices.

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