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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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Local Sequential MCMC for Data Assimilation with Applications in Geoscience

arXiv

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arXiv 2024年

作者： Ruzayqat, Hamza Knio, Omar Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [36] which is provably convergent and has been recently used for filtering, particularly for high-dimensional non-linear, and potentially, non-Gaussian state-space models. Unlike particle filters, which can be considered exact methods and can be used for filtering non-linear, non-Gaussian models, SMCMC does not assign weights to the samples/particles, and therefore, the method does not suffer from the issue of weight-degeneracy when a relatively small number of samples is used. We design a localization approach within the SMCMC framework that focuses on regions where observations are located and restricts the transition densities included in the filtering distribution of the state to these regions. This results in immensely reducing the effective degrees of freedom and thus improving the efficiency. We test the new technique on high-dimensional (d ∼ 104 − 105) linear Gaussian model and non-linear shallow water models with Gaussian noise with real and synthetic observations. For two of the numerical examples, the observations mimic the data generated by the Surface Water and Ocean Topography (SWOT) mission led by NASA, which is a swath of ocean height observations that changes location at every assimilation time step. We also use a set of ocean drifters' real observations in which the drifters are moving according the ocean kinematics and assumed to have uncertain locations at the time of assimilation. We show that when higher accuracy is required, the proposed algorithm is superior in terms of efficiency and accuracy over competing ensemble methods and the original SMCMC *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2024, CC BY.

关键词： Markov chains

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Process Flow and Design Optimization of a Microbolometer 6

Process Flow and Design Optimization of a Microbolometer

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6th International Conference on Electrical Information and Communication technology, EICT 2023

作者： Rana, Mukti Almaktoom, Abdulaziz T. Delaware State University Division of Physics Engineering Mathematics and Computer Sciences and Optical Science Center for Applied Research Dover United States Department of Operations and Supply Chain Management Jeddah Saudi Arabia

ISBN: (纸本)9798350370836

Microbolometers using a-Si0.15Ge0.85O0.0236 thin films were fabricated using radio frequency magnetron sputtering and lift of technique. When a 200 nm-thick and 40 × 40 μm2 Si0.15Ge0.85O0.0236 pixels on top of the electrode arm was used, it was observed that the electrode arms and the pixels were wrapped after they were subjected to the surface micromachining process. In order to fix this problem, the design and process flow of the microbolometer array were optimized to obtain mechanically stable microbolometers. We used GexSi1-xOy, silicon nitride, NiCr and silicon nitride to provide mechanical strength and stability to the microbolometer pixels. With the modified structure and process flow optimization it was found that the detectors of bridge structure were suspended on top of the substrate. We performed forming gas passivation at 250 oC for seven hours to minimize the 1/f-noise. A temperature coefficient of resistance (TCR) of 4.8%/K was obtained at 295 K. We obtained the highest detectivity and responsivity values 1 × 104 V/W and 8.3 × 106 cm-Hz1/2/W. © 2023 IEEE.

关键词： Pixels

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Adaptive Trust-Based Optimal Route Selection Algorithm in MANET Using Sooty Tern Optimization Algorithm 7

Adaptive Trust-Based Optimal Route Selection Algorithm in MA...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Goswami, Subhrananda Johardar, Subhankar Mondal, Sukumar Das, Chandan Bikash Raja Narendra Lal Khan Women's College Research Centre of Natural and Applied Sciences Department of Computer Science India Haldia Institute of Technology Department of Computer Science India Raja Narendra Lal Khan Women's College Department of Mathematics India Tamralipta Mahavidyalaya Department of Mathematics India

ISBN: (纸本)9798350340600

An infrastructure-free, a self-configuring network of mobile nodes, known as a Mobile Ad hoc Network (MANET), has recently gained popularity due to its simple setup. MANET has become widely applicable in various fields. In recent years, diverse research works have been explored to provide strategies for attaining the optimal path for energy routing in MANET. Thus, it faces several challenges which could not be more effective in the existing approaches. Here, the route maintenance, route setup time, and routing overhead problems become serious consideration issues in the MANET. An energy-efficient routing protocol might perform substantially worse than a standard routing protocol if it is not designed properly. MANET faces several issues that are related to security design, such as restricted resources, shared wireless media, dynamic network topology and open end-to-end networks. Due to the dynamic nature of nodes, it could be difficult to establish a Quality of Service (QoS) while the routing occurs in the MANET. To solve these issues, a trust-based optimal path selection in MANET is designed using the Sooty Tern Optimization Algorithm (STOA). Here, the different multi-objective functions like throughput, path loss, the energy that is still available in the battery, link cost, packet energy cost, end-to-delay, routing overhead ratio, energy consumption, packet delivery ratio and security which help to provide an effective data communication in MANET. In the developed model, security is the main constraint to be considered for the optimal routing scheme in the MANET. The implementation results are validated with the traditional optimal routing schemes in MANET to ensure the effectiveness of the suggested routing approach. © 2023 IEEE.

关键词： Mobile ad hoc networks

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INNS: A Superficial Learning Scheme to Predict Complex Human Activity Behaviour using Improved Neural Network Schema 9

INNS: A Superficial Learning Scheme to Predict Complex Human...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Shifani, S. Agnes Paramaiyappan, M. Thamil, Selvi M.S Franklin Suresh, M. Deepa Giri, Jayant Ai-Safarini, Maram Y. Saveetha University Department of Ece Chennai India J.P. College of Engineering Department of Ece Tenkasi India Sathyabama Institute of Science and Technology Department of Mathematics Chennai India School of Sciences Srm Institute of Science and Technology Division of Mathematics Trichy India Yeshwantrao Chavan College of Engineering Division of Mechanical Engineering Nagpur India Lovely professional University Division of Research and Development Phagwara India Zarqa University Computer Science Department Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350377972

Human Activity Recognition (HAR) is a crucial task in various domains such as healthcare, smart homes, and fitness tracking, where real-time, accurate detection of human activities is required. In this work, we propose a hybrid model combining a Cascaded AutoEncoder (CAE) for feature extraction with a Random Forest (RF) classifier for human activity recognition. The model was evaluated against 9 existing models, including CNN, LSTM, GRU, ResNet, and MobileNet, using various metrics such as accuracy, precision, recall, F1-score, and inference time. The proposed model achieved a superior accuracy of 93.87%, outperforming models like ResNet (90.11%) and MobileNet (91.23%). Additionally, the model showed competitive precision (92.45%), recall (92.89%), and Fl-score (92.50%), along with an efficient inference time of 21.3 ms, making it suitable for real-time applications. The low memory usage (185 MB) further supports its deployability in resource-constrained environments. These results demonstrate that the hybrid CAE + RF model offers an optimal balance between high classification performance and computational efficiency, making it a powerful solution for HAR tasks. © 2024 IEEE.

关键词： mHealth

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Analysis of Import Substitution Processes Taking into Account Industry Specifics in the Regional Economy 14th

Analysis of Import Substitution Processes Taking into Acco...

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14th International Conference on Optimization and Applications, OPTIMA 2023

作者： Rassokha, Anastasiia Obrosova, Natalia Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of RAS Moscow Russia Center of Fundamental and Applied Mathematics Lomonosov MSU Moscow Russia

ISBN: (纸本)9783031478581

Analysis of import substitution processes is one of the urgent problems for many countries in the context of deglobalization of the world economy. Mathematical methods of convex analysis make it possible to construct a model of input-output balance with non-linear production functions. It can be identified from the official statistics of input-output tables. This model allows us to describe the substitution of production factors. The elasticity of substitution can be estimated using the solution of the Yang dual problem. With the help of the developed model for industries with a significant share of imports, the possibility of replacing imported production factors with domestic ones is studied on the example of the Russian economy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Regional planning

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Dissecting the contributions of organic nitrogen aerosols to global atmospheric nitrogen deposition and implications for ecosystems

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National science Review 2023年第12期10卷 124-136页

作者： Yumin Li Tzung-May Fu Jian Zhen Yu Xu Yu Qi Chen Ruqian Miao Yang Zhou Aoxing Zhang Jianhuai Ye Xin Yang Shu Tao Hongbin Liu Weiqi Yao Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks School of Environmental Science and Engineering Southern University of Science and Technology Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area Southern University of Science and Technology Division of Environment and Sustainability Hong Kong University of Science and Technology National Center for Applied Mathematics Shenzhen Department of Chemistry Hong Kong University of Science and Technology State Key Joint Laboratory of Environmental Simulation and Pollution Control International Joint Laboratory for Regional Pollution Control College of Environmental Sciences and Engineering Peking University Frontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory Ocean University of China College of Oceanic and Atmospheric Sciences Ocean University of China Department of Ocean Science Hong Kong University of Science and Technology Department of Ocean Science and Engineering Southern University of Science and Technology

Atmospheric deposition of particulate organic nitrogen（ONp） is a significant process in the global nitrogen cycle and may be pivotally important for N-limited ***,past models largely overlo oked the spatial and chemical inhomogeneity of atmospheric ONpand were thus deficient in assessing global *** constructed a comprehensive global model of atmospheric gaseous and particulate organic nitrogen（ON）,including the latest knowledge on emissions and secondary *** this model,we simulated global atmospheric ONpabundances consistent with *** estimated global atmospheric ON deposition was 26 Tg N yr-1,predominantly in the form of ONp(23 Tg N yr-1)and mostly from wildfires（37%）,oceans（22%） and aqueous productions（17%）.Globally,ONpcontributed as much as 40% to 80% of the total N deposition downwind of biomass-burning *** ONpdeposition thus constituted the dominant external N supply to the N-limited boreal forests,tundras and the Arctic Ocean,and its importance maybe amplified in a future warming climate.

关键词： organic nitrogen aerosol secondary organic nitrogen biomass burning

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