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Wearable Sensor Gait Analysis of Fall Detection using Attention Network

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Wearable Sensor Gait Analysis of Fall Detection using Attent...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haben Yhdego Jiang Li Christopher Paolini Michel Audette Computational Modeling and Simulation Engineering Old Dominion University Norfolk USA Electrical and Computer Engineering Old Dominion University Norfolk USA Electrical and Computer Engineering San Diego State University San Diego California

ISBN: (纸本)9781665429825

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding-founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.

关键词： Sensitivity Computational modeling Sociology Transformers Natural language processing Real-time systems Fall detection

Neural Predictive Monitoring under Partial Observability

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arXiv 2021年

作者： Cairoli, Francesca Bortolussi, Luca Paoletti, Nicola Department of Mathematics and Geosciences Università di Trieste Italy Modeling and Simulation Group Saarland University Germany Department of Computer Science Royal Holloway University London United Kingdom

We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings where only partial and noisy observations of the state are available at runtime. Such settings directly affect the accuracy and reliability of the reachability predictions, jeopardizing the safety of the system. In this work, we present a learning-based method for PM that produces accurate and reliable reachability predictions despite partial observability (PO). We build on Neural Predictive Monitoring (NPM), a PM method that uses deep neural networks for approximating hybrid systems reachability, and extend it to the PO case. We propose and compare two solutions, an end-to-end approach, which directly operates on the rough observations, and a two-step approach, which introduces an intermediate state estimation step. Both solutions rely on conformal prediction to provide 1) probabilistic guarantees in the form of prediction regions and 2) sound estimates of predictive uncertainty. We use the latter to identify unreliable (and likely erroneous) predictions and to retrain and improve the monitors on these uncertain inputs (i.e., active learning). Our method results in highly accurate reachability predictions and error detection, as well as tight prediction regions with guaranteed coverage. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room

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arXiv 2025年

作者： Hooshyar, Danial Šír, Gustav Yang, Yeongwook Kikas, Eve Hämäläinen, Raija Kärkkäinen, Tommi Gašević, Dragan Azevedo, Roger School of digital Technologies Tallinn University Tallinn Estonia Department of Education University of Jyväskylä Jyväskylä Finland Department of Computer Science Czech Technical University Prague Czech Republic Department of Computer Science and Engineering Gangneung-Wonju National University Wonju Korea Republic of School of Natural Sciences and Health Tallinn University Tallinn Estonia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Faculty of Information Technology Monash University ClaytonVIC Australia School of Modeling Simulation and Training University of Central Florida Orlando United States

Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, multiple critical issues remain unresolved—acting as the elephant in the room within the AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis paper identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: 1) the lack of clarity around what AI for education truly means—often ignoring the distinct purposes, strengths, and limitations of different AI families—and the growing trend of equating it with domain-agnostic, company-driven large language models;2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature;3) the limited integration of domain knowledge and lack of stakeholder involvement in AI design and development;4) the continued use of non-sequential machine learning models on temporal educational data, which fails to capture the dynamics and dependencies critical to understanding real learning progressions;5) the misuse of non-sequential metrics to evaluate sequential or temporal models;6) using unreliable explainable AI methods to provide explanations for black-box models;7) ignoring ethical guidelines in addressing data inconsistencies during model training;8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking, which risks leading to unreliable conclusions;and 9) focusing on global prescriptions while overlooking localized, student-specific recommendations. Supported by both theoretical and empirical research, we demonstrate how hybrid AI methods—specifically neural-symbolic AI—address the elephant in the room and serve as the foundation for building responsibl

关键词： Teaching

Cyclic Quantum Teleportation of Two-Qubit Entangled States by using Six-Qubit Cluster State and Six-Qubit Entangled State

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arXiv 2024年

作者： Slaoui, A. El Kirdi, M. Ahl Laamara, R. Alabdulhafith, M. Chelloug, S.A. Abd El-Latif, A.A. LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next neighbour in a circular manner. Here, new cyclic quantum teleportation scheme is proposed for perfectly transmitting cyclically three arbitrary unknown two-qubit states (α, β and γ) among the three collaborators. In this scheme, Alice can send to Bob the quantum information contained in her two-qubit state α and receive from Charlie the quantum information contained in the two-qubit state in his possession γ and similarly, Bob can transmit to Charlie the quantum information contained in his two-qubit state β through a quantum channel of twelve-qubit state consisting of a six-qubit cluster state and a six-qubit entangled state by sequentially and cyclically performing Bell state measurements. Subsequently, each one of the three participants can afterwards retrieve his own desired two-qubit state using classical channel and by performing appropriate unitary Pauli operators and we have shown that our proposed scheme performs efficiently. © 2024, CC0.

关键词： Qubits

Comparison of Turbulence Approaches Modelling the Flow Around the NACA 0012 Profile

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Comparison of Turbulence Approaches Modelling the Flow Aroun...

Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Soufinae Elouardi Bouchaib Radi Rabii El Maani Laidi Zahiri Laboratory of Modeling and Simulation of intelligent industrial systems (MS2IS) Hassan II University of Casablanca Higher Normal School of Technical Education Mohammedia Morocco Laboratory of Engineering Industrial Management and Innovation (IMII) FST Settat Hassan 1st University Settat Morocco Laboratory of Computer and Mathematical Process Engineering ENSA Khouribga Sultan Moulay Slimane University Khouribga Morocco

Turbulence refers to the instability of air movements in which the speed has a whirling character at all points. This is a phenomenon often observed and generates sometimes violent movements that can disrupt flight in the case of aerodynamic interactions with aircraft. Bodily accidents due to turbulence are numerous, but they can be avoided with good control and prediction of the fluid behavior around this structure. In order to do this, designers are increasingly using numerical modeling to boost these aircraft's efficiency. We carried out a numerical analysis of the two-dimensional transonic flow on a NACA 0012 airfoil using the ANSYS/FLUENT software in order to compare the capacity of different turbulence models to predict results that are similar to those of experimental studies. The results were compared to the provided experimental data in the high Reynolds Number range of 9.10 6 . Then, a comparison based on the RANS, LES and Hybrid approach in order to obtain a better control of the profile performance. We demonstrated that Spalart-Allmaras model of Dettached Eddy simulation gave good results for unsteady flow over the wing.

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A Biased-Randomized Simheuristic for a Hybrid Flow Shop with Stochastic Processing Times in the Semiconductor Industry

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A Biased-Randomized Simheuristic for a Hybrid Flow Shop with...

simulation Winter Conference

作者： Majsa Ammouriova Javier Panadero Madlene Leißau Christoph Laroque Christin Schumacher Angel A. Juan Computer Science Department Universitat Oberta de Catalunya Euncet Business School Barcelona SPAIN Institute for Management and Information University of Applied Sciences Zwickau Zwickau GERMANY Department of Computer Science Informatik 4 - Modeling and Simulation TU Dortmund University Dortmund GERMANY Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN

ISBN: (纸本)9781665476621

Compared to other industries, production systems in semiconductor manufacturing have an above-average level of complexity. Developments in recent decades document increasing product diversity, smaller batch sizes, and a rapidly changing product range. At the same time, the interconnections between equipment groups increase due to rising automation, thus making production planning and control more difficult. This paper discusses a hybrid flow shop problem with realistic constraints, such as stochastic processing times and priority constraints. The primary goal of this paper is to find a solution set (permutation of jobs) that minimizes the production makespan. The proposed algorithm extends our previous work by combining biased-randomization techniques with a discrete-event simulation heuristic. This simulation-optimization approach allows us to efficiently model dependencies caused by batching and by the existence of different flow paths. As shown in a series of numerical experiments, our methodology can achieve promising results even when stochastic processing times are considered.

关键词： Semiconductor device modeling Production systems Uncertainty Computational modeling Electronics industry Stochastic processes Semiconductor device manufacture

Triadic Temporal Exponential Random Graph Models (TTERGM)

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arXiv 2022年

作者： Huang, Yifan Barham, Clayton Page, Eric Douglas, P.K. Modeling and Simulation Department University of Central Florida OrlandoFL United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States IACS Santa MonicaCA United States Department of Psychiatry and Biobehavioral Medicine UCLA Los AngelesCA United States

Temporal exponential random graph models (TERGM) are powerful statistical models that can be used to infer the temporal pattern of edge formation and elimination in complex networks (e.g., social networks). TERGMs can also be used in a generative capacity to predict longitudinal time series data in these evolving graphs. However, parameter estimation within this framework fails to capture many real-world properties of social networks, including: triadic relationships, small world characteristics, and social learning theories which could be used to constrain the probabilistic estimation of dyadic covariates. Here, we propose triadic temporal exponential random graph models (TTERGM) to fill this void, which includes these hierarchical network relationships within the graph model. We represent social network learning theory as an additional probability distribution that optimizes Markov chains in the graph vector space. The new parameters are then approximated via Monte Carlo maximum likelihood estimation. We show that our TTERGM model achieves improved fidelity and more accurate predictions compared to several benchmark methods on GitHub network data. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov chains

'Surgical Gps' Proof of Concept for Scoliosis Surgery

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'Surgical Gps' Proof of Concept for Scoliosis Surgery

2020 Spring simulation Conference, SpringSim 2020

作者： Tapp, Austin Audette, Michel Old Dominion University Electrical Computer Biomedical Engineering 5115 Hampton Blvd NorfolkVA United States Old Dominion University Computational Modeling Simulation Engineering 5115 Hampton Blvd NorfolkVA United States

ISBN: (纸本)9781565553705

Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time. © 2020 SCS.

关键词： Ontology

Reconsidering spatial priors in EEG source estimation: Does white matter contribute to EEG rhythms?

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arXiv 2021年

作者： Douglas, Pamela K. Douglas, David B. Computer Science Modeling & Simulation IST UCF Department of Psychiatry & Biobehavioral Medicine David Gefen School of Medicine UCLA Nuclear Medicine & Molecular Imaging Stanford Hospital Palo AltoCA United States

Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use in the BCI setting. Nonetheless, a complete understanding of how EEG measurements (voltage fluctuations) relate to information processing in the brain remains somewhat elusive. A deeper understanding of the neuroanatomical underpinnings of the EEG signal may help explain inter-individual variability in evoked and induced potentials, which may improve BCI therapies targeted to the individual. According to classic biophysical models, EEG fluctuations are primarily a reflection of locally synchronized neuronal oscillations within the gray matter oriented approximately orthogonal to the scalp. In contrast, global models ignore local signals due to dendritic processing, and suggest that propagation delays due to white matter architecture are responsible for the EEG signal, and are capable of explaining the coherence between numerous rhythms (e.g., alpha) at spatially distinct areas of the scalp. Recently, combined local-global models suggest that the EEG signal may reflect a superposition of local processing along with global contributors including transduction along white matter tracts in the brain. Incorporating both local and global (e.g., white matter) priors into EEG source models may therefore improve source estimates. These models may also help disentangle which aspects of the EEG signal are predicted to colocalize spatially with measurements from functional MRI (fMRI). Here, we explore the possibility that white matter conductivity contributes to EEG measurements via a generative model based on classic axonal transduction models, and discuss its potential implications for source estimation. Copyright © 2021, The Authors. All rights reserved.

关键词： Electroencephalography