Neural network for dynamic human motion prediction

Bataineh, Mohammad; Marler, Timothy; Abdel‐Malek, Karim; Arora, Jasbir S.

doi:10.1016/j.eswa.2015.11.020

Cited by 37 publications

(9 citation statements)

References 25 publications

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“…Given a set of independent and dependent variables, a trained model will generalize the interactions between the variables and predict the associated dependent variables using a new set of independent variables (Zhu et al 2020). One of the ML is artificial neural networks (ANN) which are widely used to model complex relationships between inputs and outputs (Ardestani et al 2014;Bataineh et al 2016;Liu et al 2009). However, an ANN often overfit the data, and the capability to generalize weakens when dealing with small data samples (Ardestani et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

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Correlations between socioeconomic factors and poverty in regression models do not reflect actual relationships, especially when data exhibit patterns of spatial heterogeneity. Spatial regression models can estimate the relationships between socioeconomic factors and poverty in defined geographical areas, explaining the imbalanced distribution of poverty, but the relationships between these factors and poverty are not always linear however, and conventional simple linear local regression models do not accurately capture these nonlinear relationships. To fill this gap, we used a local regression method, geographically weighted random forest regression (GW-RFR), that integrates a spatial weight matrix (SWM) and random forest (RF). The GW-RFR evaluates the spatial variations in the nonlinear relationships between variables. A county-level poverty data set of China was employed to estimate the performance of the GW-RFR against the random forest (RF). In this poverty application, the value of was 0.128 higher than that of the RF, the NRMSE value was 1.6% lower than the RF, and the MAE value was 0.295 lower than the RF. These results showed that the relationship between poverty factors and poverty varies with space at the county level in China, and the GW-RFR was suitable for dealing with nonlinear relationships in local regression analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-17513-3.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…Data driven models are observed to be utilized frequently for speculating the behavior of complex systems in various domains such as biological [1], mechanical [2], [3], robotics [4]- [6], and energy forecasting [7] systems. In certain cases, wherein a "full physics" (high fidelity) model is unavailable or is computationally prohibitive to evaluate, a "partial physics" (lower fidelity) model can be used for prediction.…”

Section: A Physics-infused Hybrid Modelingmentioning

confidence: 99%

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Iqbal¹,

Behjat²,

Adlakha³

et al. 2022

Preprint

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Physics-Infused Machine Learning (PIML) architectures aim at integrating machine learning with computationallyefficient, low-fidelity (partial) physics models, leading to improved generalizability, extrapolability, and robustness to noise, compared to pure data-driven approximation models. End-uses of PIML include, but are not limited to, model-based optimization and model-predictive control. Recently a new PIML architecture was reported by the same authors, known as Opportunistic Physics-mining Transfer Mapping Architecture or OPTMA, which transfers the original inputs into latent features using a transfer neural network; the partial physics model then uses the latent features to generate the final output that is as close as possible to the high-fidelity output. While gradient-free solvers and back-propagation with supervised learning (where optimum labels are pre-generated) have been used to train OPTMA, that approach remains computationally inefficient, and challenging to generalize across different problems or exploit state-of-theart machine learning architectures. This paper aims to alleviate these issues by infusing the partial physics model inside the neural network, as described via tensors in the popular machine learning framework, PyTorch. Such a description also naturally allows auto-differentiation of the partial physics model, thereby enabling the use of efficient back-propagation methods to train the transfer network. The benefits of the upgraded OPTMA architecture with Automatic Differentiation (OPTMA-Net) is demonstrated by applying it to the problem of modeling the sound pressure field created by a hovering unmanned aerial vehicle (UAV). Ground truth data for this problem has been obtained from an indoor UAV noise measurement setup. Here, the partial physics model is based on the interference of acoustic pressure waves generated by an arbitrary number of acoustic monopole sources. Case studies show that OPTMA-Net provides generalization performance close to, and extrapolation performance that is 4 times better than, those given by a pure data-driven model.

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“…For comparison purpose, we need a feed-forward model with complex parameterization that is online adaptable. Hence, we use Artificial Neural Networks (ANNs) to approximate the motion transition model f in (1), which is fast to train and widely used in modeling human motion [15].…”

Section: B Neural Network Model Without Adaptationmentioning

confidence: 99%

Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration

Zhao

Sun

Liu

et al. 2020

Preprint

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Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human robot collaboration, but also enhance human safety in close proximity to robots. Among existing prediction models, the parameterization and identification methods of those models vary. It remains unclear what is the necessary parameterization of a prediction model, whether online adaptation of the model is necessary, and whether prediction can help improve safety and efficiency during human robot collaboration. These problems result from the difficulty to quantitatively evaluate various prediction models in a closedloop fashion in real human-robot interaction settings. This paper develops a method to evaluate the closed-loop performance of different prediction models. In particular, we compare models with different parameterizations and models with or without online parameter adaptation. Extensive experiments were conducted on a human robot collaboration platform. The experimental results demonstrated that human motion prediction significantly enhanced the collaboration efficiency and human safety. Adaptable prediction models that were parameterized by neural networks achieved the best performance.

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Neural network for dynamic human motion prediction

Cited by 37 publications

References 25 publications

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration

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