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IOT based wearable sensor system architecture for classifying human activity

Measurement: Sensors 2025年 39卷

作者： Mahalakshmi, V. Kumar, Pramod Bhende, Manisha Keshta, Ismail Rathod, Swatiben Yashvantbhai Ramesh, Janjhyam Venkata Naga Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Computer science and Technology Ganga Institute of Technology and Management Kablana Haryana Jhajjar124104 India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Instrumentation And Control Department Vishwakrma Government Engineer College Chandkheda Ahmedabad382424 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist. Andhra Pradesh 522302 India

Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment. © 2025

关键词： Discrete wavelet transforms

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A Hybrid Data-Driven Granular Model for Battery Remaining Useful Life Prediction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jing, TaiLong Du, Sheng Wang, Cong Wu, Chao Pedrycz, Witold Sharaf, Mohamed Li, ZhiWu Luoyang Institute of Science and Technology School of Electrical Engineering and Automation Luoyang471023 China China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an710072 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland King Saud University College of Engineering Department of Industrial Engineering Riyadh11421 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau China

Accurate and meaningful prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) provides a significant guide for the maintenance and management of these LIBs. A singular numerical predictive result by numerous conventional models is insufficient to furnish decision-makers with comprehensive or nuanced insights. To address this issue, predictive RUL results in the form of information granules are essential. In this study, a hybrid data-driven granular model is established for battery RUL prediction with granular (interval-based) quantification for LIBs. This granular model is designed and formulated in a way that it produces comprehensive prediction outcomes realized as information granules, which are expressed in a more descriptive and comprehensive manner. Thus, the resulting information granules can effectively reflect and communicate the confidence associated with the predictions. The granular model is realized in two steps: 1) probabilistic prediction results are produced by a hybrid numeric predictive model and 2) a comprehensive granulation of the probabilistic results is achieved under the principle of information granularity. The granular predictive results are holistically evaluated and optimized by the two associated criteria of granularity (coverage and specificity). This transformation of information granules provides an intuitive and interpretable display for battery maintenance and safe work. The proposed granular model is verified by using an open-access LIBs dataset, and the experimental results demonstrate that the proposed granular model outperforms other comparative methods. © 2025 IEEE. All rights reserved.

关键词： Prediction models

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

arXiv

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arXiv 2025年

作者： Sandrini, Samuele Faroni, Marco Pedrocchi, Nicola Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy Milan20133 Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Collaborative robotics cells leverage heterogeneous agents to provide agile production solutions. Effective coordination is essential to prevent inefficiencies and risks for human operators working alongside robots. This paper proposes a human-aware task allocation and scheduling model based on Mixed Integer Nonlinear Programming to optimize efficiency and safety starting from task planning stages. The approach exploits synergies that encode the coupling effects between pairs of tasks executed in parallel by the agents, arising from the safety constraints imposed on robot agents. These terms are learned from previous executions using a Bayesian estimation;the inference of the posterior probability distribution of the synergy coefficients is performed using the Markov Chain Monte Carlo method. The synergy enhances task planning by adapting the nominal duration of the plan according to the effect of the operator’s presence. Simulations and experimental results demonstrate that the proposed method produces improved human-aware task plans, reducing unuseful interference between agents, increasing human-robot distance, and achieving up to an 18% reduction in process execution time. © 2025, CC BY.

关键词： Markov chains

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State Feedback Tracking control for Indirect Field-oriented Induction Motor Using Fuzzy Approach

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International Journal of Automation and computing 2013年第2期10卷 99-110页

作者： M. Allouche M. Chaabane M. Souissi D. Mehdi F. Tadeo Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (Lab-STA) National School of Engineering of SfaxUniversity of Sfax Laboratory of Computer and Automatic for System (LIAS) Higher School of Engineering of Poitiers (ESIP) University of Poitiers Department of Systems Engineering University of Valladolid

This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno （T-S） fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities （LMIs）. Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.

关键词： Fuzzy tracking control H∞ performance feedback controller linear matrix inequality （LMI） fuzzy observer

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Stabilizability of Dynamic Coalitional Games with Transferable Utility

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IFAC-PapersOnLine 2016年第22期49卷 121-126页

作者： Smyrnakis, Michalis Bauso, Dario Veres, Sandor Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

Stabilizing allocations in coalitional games with transferable utility are studied within the framework of uncertain and dynamic stochastic systems. We assume that the excess of allocations relative to the evolution of coalition values can be described by a stochastic differential equation. As main contribution we provide a feedback control law of allocations to maintain the coalition within a target set. We show that the resulting dynamics is second moment stable under our feedback linear-saturated control strategy. © 2016

关键词： Game theory Agents control theory Differential equations Feedback Stochastic control systems Stochastic systems Coalition value Coalitional game Cooperative game Dynamic stochastic systems Feedback control law Robot cooperation Saturated control Stochastic differential equations

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ANALYZING NONLINEAR-systems IN THE FREQUENCY-DOMAIN .2. THE PHASE RESPONSE

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MECHANICAL systems AND SIGNAL PROCESSING 1994年第1期8卷 45-62页

作者： BILLINGS, SA ZHANG, H Department of Automatic Control and Systems Engineering University of Sheffield U.K.

Phase shift is very important in the frequency response analysis of non-linear systems and in this paper the phase response function is analysed and interpreted to provide a clear understanding of phase. Non-linear systems which include delay elements are also discussed and simulations of both continuous and discrete time non-linear systems are included to demonstrate the concepts involved.

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Artificial Intelligence Based Segmentation for Skin-Layer Visualization from Optical Coherence Tomography Images

Artificial Intelligence Based Segmentation for Skin-Layer Vi...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Gilang Titah Ramadhan Yori Pusparani Ardha Ardea Prisilla Wei-Cheng Shen Hsu-Tang Cheng Ben-Yi Liau Yih-Kuen Jan Wen-Thong Chang Chih-Yang Lin Chi-Wen Lung Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana USA Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92 % accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications.

关键词： Deep learning Visualization Solid modeling Image segmentation Accuracy Three-dimensional displays Optical coherence tomography Medical services Predictive models Epidermis

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Fe2O3-Functionalized MoS2 Nanostructure Sensor for High-Sensitivity and Low-Level SO2 Detection

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ACS Sensors 2025年

作者： Gond, Rahul Barala, Suraj Shukla, Prajjwal Bassi, Gaurav Kumar, Suresh Kumar, Mukesh Kumar, Mahesh Rawat, Brajesh Department of Electrical Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India IDRP-Space Science and Technology Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Physics Indian Institute of Technology Ropar Punjab Rupnagar140001 India Department of Physics Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Real-time sulfur dioxide (SO2) monitoring is essential to mitigate its severe health and environmental impacts while ensuring compliance with industrial safety and emission regulations. Two-dimensional MoS2 stands out as a promising material for developing low-temperature-operated gas sensors due to its exceptionally high surface-to-volume ratio and ease of surface functionalization. However, monitoring the SO2 level faces challenges, including limited selectivity, sensitivity, and detection range, with high operating temperatures (200-600 °C) or external light source requirements. To address these issues, we present a highly sensitive SO2 sensor based on Fe2O3 nanoparticle-functionalized vertically aligned MoS2 nanostructure material, which is fabricated using a scalable sputtering process. The Fe2O3-MoS2 sensor exhibits a broad detection range from 100 ppb to 100 ppm, with a theoretical detection limit of around 22.8 ppb. When exposed to 5 ppm of SO2, the sensor achieves a response of around 32.2%, with response and recovery times of approximately 104 and 141 s, respectively. The fabricated sensor demonstrated impressive sensitivity (4.9%/ppm) for SO2 concentration in the range of 0.1 to 5 ppm, coupled with excellent reproducibility and stability at 150 °C. This enhanced performance is attributed to the catalytic effect of Fe2O3 and modulation of the heterojunction barrier at the interface. This study introduces a highly scalable, reliable, and stable Fe2O3-MoS2 sensor, which paves the way for developing energy-efficient and miniaturized SO2 sensors. © 2025 American Chemical Society.

关键词： Layered semiconductors

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A hierarchical multivariable fuzzy controller for learning with genetic algorithms

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INTERNATIONAL JOURNAL OF control 1996年第5期63卷 865-883页

作者： Linkens, DA Nyongesa, HO [a] Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield U.K.

The most common approach to multivariate fuzzy control is to extend the single-variable case by combining more state-variable pairs. This approach, however, results in high-dimensional rule-bases that may not be implementable in practical systems. Typically, for each additional state variable, the number of rules increases exponentially. Clearly, the number of rules, even for systems of moderate complexity, becomes impractical in real-time, due to the required processing time. This paper proposes and implements a simplified structure for a multi-variable fuzzy controller, which also reduces the total number of rules. This approach is suitable for machine-learning applications since different rules can be derived to infer the correct control actions, dependent on the controller structure and the way in which the input variables have been paired. The number of rules using this approach is minimal and is a linear function of the number of the state inputs. Furthermore, no fuzzification/defuzzification scaling factors are required for the controllers in intermediate levels of composition. The methodology is demonstrated on an anaesthesia simulation case study.

关键词： Multivariable control systems

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