Article
Engineering, Biomedical
Andrea Tigrini, Ali H. Al-Timemy, Federica Verdini, Sandro Fioretti, Micaela Morettini, Laura Burattini, Alessandro Mengarelli
Summary: This study explores the importance of using transient surface electromyographic (sEMG) signals in the design and control of assistive technologies. The results show that transient sEMG signals can effectively recognize upper-limb motion intent, which is crucial for the design and control of assistive technologies.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Chemistry, Analytical
Bingbin Wang, Levi Hargrove, Xinqi Bao, Ernest N. Kamavuako
Summary: This study compared the statistical properties of surface electromyography signals used in home and laboratory settings for prosthesis calibration, finding differences in between-calibration classification errors but not within-calibration classification errors.
Article
Engineering, Multidisciplinary
Lizhi Pan, Kai Liu, Kun Zhu, Jianmin Li
Summary: Amputees have poorer performances in EMG pattern recognition compared to able-bodied individuals, and factors such as muscle weakness and atrophy, limb length, and motor cortex decrease have been studied. However, the impact of the absence of joint movements has not been explored. This study found that hand and wrist joint movements significantly affect EMG pattern recognition, providing a new perspective for future research.
JOURNAL OF BIONIC ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Hanadi A. Jaber, Mofeed T. Rashid, Hisham Mahmood, Luigi Fortuna
Summary: This paper proposes a set of robust features to improve the performance of the myoelectric control system for upper limb prostheses. These features significantly increase the classification accuracy in online setups and are more resistant to noise compared to time-domain features. The results confirm the robustness of the features extracted from the high-density surface electromyography map.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Biomedical
Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme
Summary: This research introduces a set of metrics for analyzing transitions between voluntary changes in different muscle contraction types during continuous control. Results show that a linear discriminant classifier consistently outperforms other conventional classifiers during both transitions and steady-state conditions. This suggests that the proposed metrics could provide a more representative picture of a classifier's performance.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Neelum Yousaf Sattar, Zareena Kausar, Syed Ali Usama, Noman Naseer, Umer Farooq, Ahmed Abdullah, Syed Zahid Hussain, Umar Shahbaz Khan, Haroon Khan, Peyman Mirtaheri
Summary: This study successfully improved the control performance of transhumeral prostheses by integrating sEMG and fNIRS signals, achieving high accuracy rates for four arm motions and two hand motions for both healthy and amputated subjects.
Article
Medicine, General & Internal
Anna Mika, Piotr Mika, Lukasz Oleksy, Anita Kulik
Summary: This study evaluated the changes in bioelectrical activity of lower limb muscles in claudicating patients during a 12-week supervised treadmill training program. The results indicated that both proximal and distal muscles showed beneficial changes after the training period, suggesting that increased foot plantar flexion and stronger push-off as well as greater hip extension were the main mechanisms contributing to the observed improvement in gait pattern.
JOURNAL OF CLINICAL MEDICINE
(2022)
Article
Chemistry, Analytical
Neelum Yousaf Sattar, Zareena Kausar, Syed Ali Usama, Umer Farooq, Muhammad Faizan Shah, Shaheer Muhammad, Razaullah Khan, Mohamed Badran
Summary: This paper proposes a method based on fNIRS for recognizing human intention in upper limb motions. The results show that the accuracy of classifying six arm actions using this method can reach 78%. These achieved fNIRS results for intention detection are promising and suggest their potential application for real-time control of transhumeral prostheses.
Article
Chemistry, Analytical
Robert V. Schulte, Erik C. Prinsen, Jaap H. Buurke, Mannes Poel
Summary: This study investigated the development of error rate over one week and compared three adaptation strategies to reduce error rate increase. Among the three tested strategies, entropy based adaptation showed the smallest increase in error rate over time. This suggests that entropy based adaptation is a simple and feasible strategy for lower limb pattern recognition.
Article
Engineering, Electrical & Electronic
Cheng Shen, Zhongcai Pei, Weihai Chen, Jianhua Wang, Jianbin Zhang, Zuobing Chen
Summary: This study explores the generalization ability of gesture recognition via surface electromyography (sEMG) and proposes a new feature extraction method to reduce the influence of limb position on sEMG-based pattern recognition. The results show significant improvements in accuracy and generalization compared to traditional methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Maged S. Al-Quraishi, Irraivan Elamvazuthi, Tong Boon Tang, Muhammad Al-Qurishi, S. Parasuraman, Alberto Borboni
Summary: The study developed a fusion technique combining cortical and muscular activities to recognize bilateral lower limb movements. The multimodal approach showed significant improvement in movement recognition accuracy compared to single modality methods, particularly when using the linear discriminant analysis (LDA) classifier.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Ejay Nsugbe, Oluwarotimi William Samuel, Mojisola Grace Asogbon, Guanglin Li
Summary: Multiresolution decomposition techniques allow for separating a signal into sublevels to minimize redundancy and contain necessary information. Wavelet decomposition is favored for processing non-stationary signals, but its widespread practical application is hindered by perceived computational complexity. A time-domain-based signal decomposition method shows improved motion intent decoding compared to wavelet decomposition and raw signals in processing neuromuscular and brain wave signals.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Ziyou Li, Xingang Zhao, Guangjun Liu, Bi Zhang, Daohui Zhang, Jianda Han
Summary: A transfer learning method is proposed to reduce the impact of electrode shifts in sEMG-based recognition systems, introducing a novel activation angle and adaptive transformation to handle electrode positions being moved. Experimental results show a significant improvement in gesture recognition accuracy with this method.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Naveen Kumar Karnam, Anish Chand Turlapaty, Shiv Ram Dubey, Balakrishna Gokaraju
Summary: This article presents the analysis of electromyography signals for evaluating the activities of daily living using a novel dataset called EMAHA-DB1. The dataset consists of multichannel sEMG signals acquired from 25 non-disabled subjects performing various activities. The classification accuracy achieved using state-of-the-art machine learning classifiers was found to be 83.21% for five FAABOS categories and 75.39% for the 22 class hand activities.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Chemistry, Analytical
Sara Abbaspour, Autumn Naber, Max Ortiz-Catalan, Hamid GholamHosseini, Maria Linden
Summary: This study compared the offline and real-time performance of nine different classification algorithms, showing that linear discriminant analysis and maximum likelihood estimation performed well in offline decoding, while the multilayer perceptron also excelled in real-time investigation.
Article
Computer Science, Software Engineering
Dominique Beaini, Sofiane Achiche, Alexandre Duperre, Maxime Raison
Summary: This paper introduces a method to improve saliency convolutional neural networks (CNN) by using Green's function convolution (GFC) to extrapolate edge features into salient regions. By combining edge features with saliency features using the gradient integration and sum (GIS) layer, the network's sensitivity to parameter initialization, overfitting, and repeatability of training are improved. Adding a GIS layer near the network's output in models like HED and DSS shows performance enhancements in noisy or low-brightness images, surpassing denseCRF post-processing methods and drastically increasing speed.
Article
Computer Science, Interdisciplinary Applications
B. Samadi, M. Raison, S. Achiche, C. Fortin
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Olivier Barron, Maxime Raison, Guillaume Gaudet, Sofiane Achiche
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Yann-Seing Law-Kam Cio, Yuanchao Ma, Aurelian Vadean, Giovanni Beltrame, Sofiane Achiche
Summary: The development of autonomous greenhouses has attracted the interest of researchers and industry for their potential in providing an optimal environment for high-quality crop growth with minimal resources. Considering subsystem and component interactions early in the design phase can streamline the product development process and improve overall system performance, leading to better crop quality and resource management in the case of a greenhouse.
AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING
(2021)
Article
Mechanics
Laurent Blanchet, Sofiane Achiche, Quentin Docquier, Paul Fisette, Maxime Raison
Summary: The need for upper limb assistive and wearable exoskeletons is increasing, but there are still challenges in optimizing their geometric and dynamic characteristics. This study developed procedures to optimize the dimensions and dynamic parameters of assistive upper limb exoskeletons, resulting in reduced joint torques and successful geometric and dynamic synthesis procedures. Future perspectives include building an optimization framework to further improve the design of exoskeletons.
MULTIBODY SYSTEM DYNAMICS
(2021)
Article
Engineering, Aerospace
Christophe Marcel Trouillefou, Yann-Seing Law-Kam Cio, Mario Jolicoeur, Bilel Said, Anne Galarneau, Sofiane Achiche, Giovanni Beltrame
Summary: A compact 2U incubator was developed for autonomous growth of Medicago truncatula and designed as a payload for a Cubesat nanosatellite. The experiment involved monitoring the environment using sensors, conducting growth experiments under different conditions, establishing a prediction model, and successfully measuring and predicting functional trait values.
Article
Automation & Control Systems
Radu Ionut Popescu, Maxime Raison, George Marian Popescu, David Saussie, Sofiane Achiche
Summary: The task of playing table tennis has long fascinated roboticists due to its difficulty which requires fast movements, accurate control, and adaptation to task parameters. This paper proposes a prototype of a drone playing table tennis, with real experiments showing a success rate of 40%.
Article
Business
Francesca Pipitone, Sofiane Achiche, Francesco Paolo Appio, Antonella Martini
Summary: This study proposes a decision support system based on ANFIS to guide European policy makers in allocating and prioritizing public resources while promoting regional economic growth. The results show that increasing R&D investments can enhance regional employment rates and patent numbers, leading to an increase in regional GDP through specialization and diversification.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2021)
Review
Chemistry, Analytical
Guillaume Gaudet, Maxime Raison, Sofiane Achiche
Summary: This review examines the current status and future trends of upper limb exoskeletons for children, pointing out the limitations and challenges in the development of such devices in pediatric field.
Meeting Abstract
Infectious Diseases
Y. Ma, K. Engler, S. Vicente, S. Achiche, B. Lemire, A. Rodriguez Cruz, L. Theriault, S. Soussou, B. Regazzoni, G. Tu, M. Nait Ei Haj, A. de Pokomandy, J. Cox, N. Zahedi Niaki, B. Lebouche
Article
Engineering, Manufacturing
Gabriel Bernard, Sofiane Achiche, Sebastien Girard, Rene Mayer
Summary: This research develops a method for monitoring manufacturing processes based on kernel density estimation functions of machine tools spindle load historical time signals, suitable for low sampling rates. The technique was tested on a titanium part manufacturing line by an industrial partner.
JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING
(2021)
Article
Engineering, Manufacturing
P. Assi, S. Achiche, L. Laberge Lebel
Summary: The geometry of a braid directly affects its mechanical properties, while the carriers' path determines the braid's geometry. A novel braiding machine design has been proposed, allowing independent carrier movement to achieve variable geometry parameters along the axis. This concept opens up the possibility of 3D-printing textile composites with tailorable mechanical properties.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Correction
Physiology
Luis Antonio Pereira de Lima, Ricardo Dantas de Lucas, Maxime Raison, Sofiane Achiche
PFLUGERS ARCHIV-EUROPEAN JOURNAL OF PHYSIOLOGY
(2021)
Review
Chemistry, Multidisciplinary
Clautilde Nguiadem, Maxime Raison, Sofiane Achiche
APPLIED SCIENCES-BASEL
(2020)
Article
Automation & Control Systems
Kanglin Xing, J. R. R. Mayer, Sofiane Achiche
INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY
(2020)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
