Cybersickness Detection Through Head Movement Patterns: A Promising Approach

Salehi, Masoud; Javadpour, Nikoo; Beisner, Brietta; Sanaei, Mohammadamin; Gilbert, Stephen B.

doi:10.1007/978-3-031-60611-3_18

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-3-031-60611-3_18

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Cybersickness Detection Through Head Movement Patterns: A Promising Approach

Masoud Salehi,

Nikoo Javadpour,

Brietta Beisner

et al.

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2024

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Cited by 2 publications

References 54 publications

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Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Dong,

Hu,

et al. 2024

J. Am. Chem. Soc.

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Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism−nano systems are crucial to facilitate the biological applications of NPs. Here, we propose an interpretable causal system optimization (ICSO) framework and construct the upstream and downstream tasks of accurate prediction and intelligent NP optimization. ICSO framework screens the key drivers (recovery duration, specific surface area, and nanomaterial size) and potential causal information for immune responses and organ burden, revealing the hidden priming/constraint effects in bionano interactions. ICSO can be used to quantify the thresholds of biological responses to multiple properties (e.g., the specific surface area, diameter, and zeta potential). ICSO provides quantitative information and constraint conditions for the design of highly biocompatible and targeted organ delivery nanomaterials. For example, negative inflammation is reduced by 36.19%, and positive lung accumulation is promoted by 40.14% by optimizing the specific surface areas and shape and increasing the diameter-to-length ratio. ICSO overcomes the limitations of experience-dependent approaches and provides powerful and automated solutions for decision-makers during nanomaterial design.

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Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Dong,

Hu,

et al. 2024

J. Am. Chem. Soc.

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Flexural Eigenfrequency Analysis of Healthy and Pathological Tissues Using Machine Learning and Nonlocal Viscoelasticity

Farajpour,

Ingman

2024

Computers

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Biomechanical characteristics can be used to assist the early detection of many diseases, including breast cancer, thyroid nodules, prostate cancer, liver fibrosis, ovarian diseases, and tendon disorders. In this paper, a scale-dependent viscoelastic model is developed to assess the biomechanical behaviour of biological tissues subject to flexural waves. The nonlocal strain gradient theory, in conjunction with machine learning techniques such as extreme gradient boosting, k-nearest neighbours, support vector machines, and random forest, is utilised to develop a computational platform for biomechanical analysis. The coupled governing differential equations are derived using Hamilton’s law. Transverse wave analysis is conducted to investigate different normal and pathological human conditions including ovarian cancer, breast cancer, and ovarian fibrosis. Viscoelastic, strain gradient, and nonlocal effects are used to describe the impact of fluid content, stiffness hardening caused by the gradients of strain components, and stiffness softening associated with the nonlocality of stress components within the biological tissues and cells. The integration of the scale-dependent biomechanical continuum model with machine learning facilitates the adoption of the developed model in practical applications by allowing for learning from clinical data, alongside the intrinsic mechanical laws that govern biomechanical responses.

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Cybersickness Detection Through Head Movement Patterns: A Promising Approach

Cited by 2 publications

References 54 publications

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Flexural Eigenfrequency Analysis of Healthy and Pathological Tissues Using Machine Learning and Nonlocal Viscoelasticity

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