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A review of artificial intelligence methods for Alzheimer's disease diagnosis: Insights from neuroimaging to sensor data analysis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 92卷

作者： Bazarbekov, Ikram Razaque, Abdul Ipalakova, Madina Yoo, Joon Assipova, Zhanna Almisreb, Ali Int Informat Technol Univ Dept Comp Engn Alma Ata Kazakhstan Satbayev Univ Dept Cybersecur Informat Proc & Storage Alma Ata 050000 Kazakhstan Al Farabi Kazakh Natl Univ Dept Recreat Geog & Tourism Alma Ata Kazakhstan Int Univ Sarajevo Dept Comp Sci & Engn Sarajevo Bosnia & Herceg Gachon Univ Sch Comp Seongnam South Korea

Alzheimer's disease is the most common cause of dementia, gradually impairing memory, intellectual, learning, and organizational capacities. An individual's capacity to perform fundamental daily tasks is greatly impacted. This review examines the advancements in diagnosing Alzheimer's disease (AD) using artificial intelligence (AI) methods and machine learning (ML) algorithms. The review introduces the importance of diagnosing AD accurately and the potential benefits of using AI techniques and machine learning algorithms for this purpose. The review is based on various state-of-the-art data sources including MRI data, PET imaging, EEG and MEG signals, and data from various sensors. The state-of-the-art radiomics approaches are explored to extract a wide range of information from medical images using data-characterization algorithms. These features can show temporal patterns and qualities that are not visible to the human eye. A novel data source (handwriting data) is thoroughly investigated and coupled with AI algorithms for the precise and early detection of cognitive loss associated with Alzheimer's disease. The paper discusses research directions, prospects, and future advances, as well as the proposed notion of employing a Robopen with an MPU-9250 sensor connected via Arduino. Finally, the review concludes with a summary of its significant findings and their clinical implications.

关键词： Alzheimer's disease Artificial intelligence Diagnostic methods Machine learning Neuroimaging sensor data analysis

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Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for sensor data analysis

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sensorS 2023年第21期23卷 8856页

作者： Golovko, Victor V. Kamaev, Oleg Sun, Jiansheng Canadian Nucl Labs 286 Plant Rd Chalk River ON K0J 1J0 Canada

The paper explores the application of Steiner's most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method's versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 mu Gy for 1342 h of exposure, with an uncertainty range of +3.41 to -3.59 mu Gy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 mu Gy, with an uncertainty range of +3.58 to -3.48 mu Gy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research.

关键词： sensor data analysis most-frequent-value thermoluminescent dosimeters environmental gamma background dark matter bootstrapping confidence intervals

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Applying Machine Learning for sensor data analysis in Interactive Systems: Common Pitfalls of Pragmatic Use and Ways to Avoid Them

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ACM COMPUTING SURVEYS 2021年第6期54卷 134-134页

作者： Plotz, Thomas Georgia Inst Technol Coll Comp Sch Interact Comp 85 5th St NW Atlanta GA 30332 USA

With the widespread proliferation of (miniaturized) sensing facilities and the massive growth and popularity of the field of machine learning (ML) research, new frontiers in automated sensor data analysis have been explored that lead to paradigm shifts in many application domains. In fact, many practitioners now employ and rely more and more on ML methods as integral part of their sensor data analysis workflows-thereby not necessarily being ML experts or having an interest in becoming one. The availability of toolkits that can readily be used by practitioners has led to immense popularity and widespread adoption and, in essence, pragmatic use of ML methods. ML having become mainstream helps pushing the core agenda of practitioners, yet it comes with the danger of misusing methods and as such running the risk of leading to misguiding if not flawed results. Based on years of observations in the ubiquitous and interactive computing domain that extensively relies on sensors and automated sensor data analysis, and on having taught and worked with numerous students in the field, in this article I advocate a considerate use of ML methods by practitioners, i.e., non-ML experts, and elaborate on pitfalls of an overly pragmatic use of ML techniques. The article not only identifies and illustrates the most common issues, it also offers ways and practical guidelines to avoid these, which shall help practitioners to benefit from employing ML in their core research domains and applications.

关键词： sensor data analysis machine learning applications

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Optimizing tool wear prediction in intelligent manufacturing: a multi-sensor approach enhanced by RealNVP

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第12期35卷 126140-126140页

作者： Lei, Huanyi Li, Bo Liu, Hengchang Xia, Keyao Zhao, Wenhe Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu Peoples R China Univ Elect Sci & Technol China Aircraft Swarm Intelligent Sensing & Cooperat Cont Chengdu Peoples R China Centech EG Co Ltd Chengdu Peoples R China

As intelligent manufacturing advances, tool condition monitoring (TCM) becomes crucial for maintaining the accuracy and efficiency of cutting tools. Traditional methods, such as physics-based and data-driven models, often fail to keep pace with the rapidly evolving manufacturing environments, resulting in efficiency losses and potential equipment damage. In response, this study introduces a novel tool wear monitoring model called deep convolutional neural network-multichannel bidirectional long short-term memory enhanced by RealNVP (DCNN-McBiLSTM-ENVP). This integrated model combines the robust feature extraction of CNNs with the sequential analysis capabilities of BiLSTMs and includes RealNVP data augmentation to enhance real-time applicability in smart manufacturing settings. The effectiveness of the DCNN-McBiLSTM-ENVP model is proven by its excellent performance on the PHM2010 challenge dataset across multiple key indicators.

关键词： sensor data analysis deep learning Tool condition monitoring

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Blockchain sensor Network Based CPS analysis in Healthcare System for Real Time Applications Using 6G IoT Model and Deep Learning Techniques

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-16页

作者： Liao, Shubin Shenzhen Matern & Child Healthcare Hosp Shenzhen 518000 Peoples R China

A cyber-physical system (CPS) combines communication and computer systems with physical processes. Recent developments in wireless sensor networks (WSN), cloud computing, healthcare sensors, other fields are advancing CPS as a useful model for healthcare applications, particularly home patient care. Electronic health records, or EHRs, employ an open channel, or Internet, to allow patients to manage, share, control their health records with friends, family, healthcare providers. Deep learning (DL) techniques show potential in various health applications when large amounts of data are accessible. These concepts would be very helpful for a distributed blockchain-based Internet of Things architecture. This study offers a novel method for analysing cyber-physical systems in smart healthcare that combines deep learning for sensor data analysis with blockchain technology and 6G IoT models. Here, a sensor-based health monitoring system with CPS modelling is constructed using 6G IoT (internet of things) and blockchain technology. This monitored data was then evaluated using a graph adversarial transfer regressive component neural network for the purpose of detecting abnormalities. A deep neural network-based blockchain cyber-physical system enhances privacy preservation of medical data with a high accuracy rate as a consequence of proposed study. Based on blockchain CPS analysis modelling, the experimental analysis is performed to classify outcomes in terms of accuracy, precision, recall, F-1 score, RMSE, data transmission rate, time consumption, and data privacy rate.

关键词： Smart healthcare Cyber physical system Blockchain 6G IoT model Deep learning technique sensor data analysis

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Graph Neural Networks With Trainable Adjacency Matrices for Fault Diagnosis on Multivariate sensor data

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IEEE ACCESS 2024年 12卷 152860-152872页

作者： Kovalenko, Aleksandr Pozdnyakov, Vitaliy Makarov, Ilya AIRI Moscow 121170 Russia ISP RAS Res Ctr Trusted Artificial Intelligence Moscow 109004 Russia HSE Univ Fac Comp Sci Moscow 109028 Russia

Timely detection and accurate diagnosis of faults in technological processes can significantly reduce production costs in manufacturing. Modern industrial equipment, equipped with numerous sensors, generates vast amounts of data, providing opportunities for advanced fault detection and diagnosis. While convolutional and recurrent neural networks have achieved state-of-the-art performance, they often overlook the correlations and hidden relationships among sensor signals. To address this, we propose a graph neural network (GNN) architecture that constructs graphs of sensor relationships from data. We evaluated five methods for training different types of adjacency matrices allowing to set certain restrictions on the structure of the graph. The resulting graph structures were analyzed and potential for their use in transfer learning was evaluated. Additionally, we developed an architecture that uses multiple adjacency matrices, which reduces the number of trainable parameters while maintaining high prediction quality. Our models demonstrated state-of-the-art performance on the Tennessee Eastman Process dataset, showcasing their potential for fault diagnosis on multivariate sensor data.

关键词： Graph neural networks Fault diagnosis Nearest neighbor methods Fault detection Convolutional neural networks Training Time series analysis Computer architecture Batch normalization Solid modeling sensor systems Adjacency matrix fault diagnosis graph neural networks sensor data analysis Tennessee Eastman process

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APPLICATION OF MACHINE LEARNING MODELS FOR PREDICTIVE MAINTENANCE OF BIOTECHNICAL SYSTEMS

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INMATEH-AGRICULTURAL ENGINEERING 2025年第1期75卷 931-950页

作者： Iosif, Adrian Maican, Edmond Biris, Sorin Stefan Vladut, Nicolae-Valentin Univ POLITEHN Fac Biotech Syst Engn Bucharest Romania INMA Bucharest Romania

Ensuring the reliability and efficiency of agricultural machinery is critical for modern farming operations. Traditional maintenance strategies, including corrective and preventive approaches, often lead to unexpected downtime or excessive servicing costs. This study explores the application of machine learning-based predictive maintenance for agricultural equipment, focusing on the hydraulic system of a Massey Ferguson 7700 S tractor. Real-time sensor data was collected, with hydraulic pressure selected as the primary diagnostic metric for detecting early signs of mechanical degradation. A predictive maintenance framework was developed using seven machine learning models: Isolation Forest, One-Class SVM, KMeans, DBSCAN, Autoencoders, Convolutional Neural Networks (CNNs), and XGBoost. These models were individually applied to identify pressure anomalies indicative of potential failures. To enhance detection accuracy, a "Council of the Wise" ensemble approach was introduced, where an anomaly was validated only if at least four of the seven models agreed on its presence. This consensus-based method reduced false positives and improved fault identification reliability. Results demonstrated that integrating multiple models effectively distinguished significant anomalies from noise, capturing both transient mechanical instabilities and gradual wear-related failures. The findings highlight the potential of machine learning-driven predictive maintenance in optimizing maintenance schedules, reducing unplanned downtime, and extending equipment lifespan. This study establishes a scalable, data-driven maintenance approach that enhances the operational resilience of agricultural machinery, ensuring greater efficiency and sustainability in farming operations.

关键词： Predictive maintenance Machine learning Anomaly detection Agricultural machinery Hydraulic system monitoring Multi-model voting framework sensor data analysis

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Achieving realistic cyclist behavior in SUMO using the SimRa dataset

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COMPUTER COMMUNICATIONS 2023年第1期205卷 97-107页

作者： Karakaya, Ahmet-Serdar Stef, Ioan-Alexandru Koehler, Konstantin Heinovski, Julian Dressler, Falko Bermbach, David TU Berlin Dept Telecommun Syst Mobile Cloud Comp Berlin Germany ECDF Dept Telecommun Syst Mobile Cloud Comp Berlin Germany TU Berlin Telecommun Networks Dept Telecommun Syst Berlin Germany

Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarly, studies of vulnerable road users, here cyclists, also use such models to assess the performance of communication-based road traffic safety systems. The cyclist model in SUMO, however, is very imprecise as SUMO cyclists behave either like slow cars or fast pedestrians, thus, casting doubt on simulation results for bicycle traffic. In this paper, we analyze acceleration, deceleration, velocity, and intersection left-turn behavior of cyclists in a large dataset of real world cycle tracks. We use the results to improve the existing cyclist model in SUMO and add three more detailed cyclist models and implement them in SUMO.

关键词： Urban planning Motion sensor data sensor data analysis Traffic simulation

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Versatile Multimodal Strain sensors for Responsive Soft Robot Applications 10

Versatile Multimodal Strain Sensors for Responsive Soft Robo...

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10th International Conference on Electrical Engineering, Control and Robotics (EECR)

作者： Nnadi, Sylvester Ndidiamaka Aliyu, Aliyu Elgeneidy, Khaled Rahmani, Amir Sohani, Behnaz Univ Lincoln Sch Engn Lincoln England Coventry Univ Egypt Sch Engn Cairo Governorate Egypt

ISBN: (纸本)9798350370010;9798350370003

Medical and agricultural robotics critically depend on tactile and flexible sensors to safely interact with living tissue and fragile objects. Traditional rigid robots, composed of materials like metal or plastic, lack adaptability, imposing constraints in environments requiring precise and safe interactions, such as limb rehabilitation or delicate object handling. Soft robot components, specifically engineered to engage with living tissue or fragile objects, present a promising solution due to their hyper-elasticity and conductivity. This paper introduces a novel method for fabricating flexible strain sensors using a 3D-printed soft-sensing body, ensuring flexibility and cost-effectiveness. These sensors, characterised by high resistivity and easy 3D printing capability, play a critical role in advancing soft robotics. The research focuses on designing and implementing flexible strain sensors integrated with Arduino circuitry for efficient data acquisition and analysis of electrical components. Experimental investigations demonstrate that sensor resistance is significantly affected by deflection, conductor thickness, and curvature, rendering them suitable for real-time sensing applications such as hand-wearable gloves for monitoring rehabilitation progress.

关键词： Soft Robotics Tactile Sensing sensors Additive Manufacturing Flexible Strain sensors Wearable sensors Flexible sensors Rehabilitation Technology sensor data analysis

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Detecting Railway Track Irregularities Using Conformal Prediction 33rd

Detecting Railway Track Irregularities Using Conformal Predi...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Plesner, Andreas Engsig-Karup, Allan P. True, Hans Swiss Fed Inst Technol Zurich Switzerland Tech Univ Denmark DTU Lyngby Denmark

ISBN: (纸本)9783031723551;9783031723568

This study addresses the challenge of assessing railway track irregularities using convolutional neural networks (CNNs) and conformal prediction techniques. Using high-fidelity sensor data from high-speed trains, the study proposes a CNN model that outperforms state-of-the-art results, achieving a mean unsigned error of 0.31mm on the test set. Incorporating conformal prediction with the CV-minmax method, the model delivers prediction intervals with 97.18% coverage, averaging 2.33mm in width, ensuring reliable uncertainty estimation. The model also exhibits impressive computational efficiency, processing data at a rate suitable for real-time applications, with the capacity to evaluate over 2,000km of track data per hour. These advances demonstrate the potential of the model for practical implementation in continuous monitoring systems, providing a contribution to the field of predictive maintenance within the railway industry.

关键词： Railway track integrity convolutional neural networks conformal prediction predictive maintenance sensor data analysis machine learning

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