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Thumilvannan S, Balamanigandan R. A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT. PeerJ Comput Sci 2024; 10:e2196. [PMID: 39314712 PMCID: PMC11419643 DOI: 10.7717/peerj-cs.2196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/23/2024] [Indexed: 09/25/2024]
Abstract
Stroke prediction has become one of the significant research areas due to the increasing fatality rate. Hence, this article proposes a novel Adaptive Weight Bi-Directional Long Short-Term Memory (AWBi-LSTM) classifier model for stroke risk level prediction for IoT data. To efficiently train the classifier, Hybrid Genetic removes the missing data with Kmeans Algorithm (HKGA), and the data are aggregated. Then, the features are reduced with independent component analysis (ICA) to reduce the dataset size. After the correlated features are identified using the T-test-based uniform distribution-gradient search rule-based elephant herding optimization for cluster analysis (GSRBEHO) (T-test-UD-GSRBEHO). Next, the fuzzy rule-based decisions are created with the T-test-UDEHOA correlated features to classify the risk levels accurately. The feature values obtained from the fuzzy logic are given to the AWBi-LSTM classifier, which predicts and classifies the risk level of heart disease and diabetes. After the risk level is predicted, the data is securely stored in the database. Here, the MD5-Elliptic Curve Cryptography (MD5-ECC) technique is utilized for secure storage. Testing the suggested risk prediction model on the Stroke prediction dataset reveals potential efficacy. By obtaining an accuracy of 99.6%, the research outcomes demonstrated that the proposed model outperforms the existing techniques.
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Affiliation(s)
- S Thumilvannan
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamilnadu, India
| | - R Balamanigandan
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Saveetha University, Chennai, Tamilnadu, India
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2
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Perea-Trigo M, Botella-López C, Martínez-Del-Amor MÁ, Álvarez-García JA, Soria-Morillo LM, Vegas-Olmos JJ. Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language. SENSORS (BASEL, SWITZERLAND) 2024; 24:1472. [PMID: 38475008 DOI: 10.3390/s24051472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/12/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE.
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Affiliation(s)
- Marina Perea-Trigo
- Department of Languages and Computer Systems, Universidad de Sevilla, 41012 Sevilla, Spain
| | - Celia Botella-López
- Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain
| | - Miguel Ángel Martínez-Del-Amor
- Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain
- SCORE Lab, I3US, Universidad de Sevilla, 41012 Sevilla, Spain
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3
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Lazaro G. When Positive is Negative: Health Literacy Barriers to Patient Access to Clinical Laboratory Test Results. J Appl Lab Med 2023; 8:1133-1147. [PMID: 37681277 PMCID: PMC10756206 DOI: 10.1093/jalm/jfad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/09/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Health literacy is a multidimensional set of skills (e.g., narrative, numeracy, digital, medication) that patients need to access and understand health information timely and accurately to make evidence-based informed decisions. CONTENT Multiple barriers prevent patients from effectively interacting with health information. The most salient barriers are poor overall health literacy skills and linguistic proficiency in English. As patients prefer direct access to laboratory test results, especially those of routine tests, contextualization and provider-directed interpretation of results are required to foster shared decision-making to address their healthcare issues and improve health outcomes. SUMMARY The use of systematic approaches that account for poor health literacy skills and include culturally and linguistically appropriate planning and availability of resources is warranted at individual and population health levels (e.g., human-centered design of patient portals).
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Affiliation(s)
- Gerardo Lazaro
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, GA, United States
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Ahmed MF, Masood K, Fremont V, Fantoni I. Active SLAM: A Review on Last Decade. SENSORS (BASEL, SWITZERLAND) 2023; 23:8097. [PMID: 37836928 PMCID: PMC10575033 DOI: 10.3390/s23198097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation.
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Affiliation(s)
- Muhammad Farhan Ahmed
- Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS, Ecole Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France; (M.F.A.); (I.F.)
| | - Khayyam Masood
- Capgemini Engineering, 4 Avenue Didier Daurat, 31700 Blagnac, France;
| | - Vincent Fremont
- Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS, Ecole Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France; (M.F.A.); (I.F.)
| | - Isabelle Fantoni
- Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS, Ecole Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France; (M.F.A.); (I.F.)
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5
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Bovenizer W, Chetthamrongchai P. A comprehensive systematic and bibliometric review of the IoT-based healthcare systems. CLUSTER COMPUTING 2023; 26:1-27. [PMID: 37359057 PMCID: PMC10251338 DOI: 10.1007/s10586-023-04047-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/28/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023]
Abstract
In the healthcare sector, the growth in technology has had a huge effect. Besides, when introduced to the world of healthcare, the Internet of Things (IoT) will simplify the transition by helping physicians closely track their patients, allowing rapid recovery. Aged patients/people should be intensively checked, and their loved ones must be aware of their wellbeing periodically. Therefore, using IoT in healthcare will simplify the lives of physicians and patients alike. Hence, this study explored a comprehensive review of intelligent IoT-based embedded healthcare systems. The papers around intelligent IoT-based healthcare systems printed until Dec-2022 are studied, and some research lines are suggested for the upcoming researchers. Thus, this study's innovation will apply healthcare systems based on IoT to include certain strategies for the future deployment of new generations of IoT-based health technology. The findings revealed that IoT is beneficial for governments to strengthen society's health and economic relations. Besides, owing to novel functional principles, IoT needs modern safety infrastructure. This study is helpful for prevalent and useful electronic healthcare services, health experts, and clinicians.
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Affiliation(s)
- Wimalyn Bovenizer
- College of Digital Innovation Technology, Rangsit University, Pathum Thani, Thailand
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6
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Talaat FM, El-Balka RM. Stress monitoring using wearable sensors: IoT techniques in medical field. Neural Comput Appl 2023; 35:1-14. [PMID: 37362562 PMCID: PMC10237081 DOI: 10.1007/s00521-023-08681-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The concept "Internet of Things" (IoT), which facilitates communication between linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports healthcare and is essential to numerous applications for tracking medical services. By examining the pattern of observed parameters, the type of the disease can be anticipated. For people with a range of diseases, health professionals and technicians have developed an excellent system that employs commonly utilized techniques like wearable technology, wireless channels, and other remote equipment to give low-cost healthcare monitoring. Whether put in living areas or worn on the body, network-related sensors gather detailed data to evaluate the patient's physical and mental health. The main objective of this study is to examine the current e-health monitoring system using integrated systems. Automatically providing patients with a prescription based on their status is the main goal of the e-health monitoring system. The doctor can keep an eye on the patient's health without having to communicate with them. The purpose of the study is to examine how IoT technologies are applied in the medical industry and how they help to raise the bar of healthcare delivered by healthcare institutions. The study will also include the uses of IoT in the medical area, the degree to which it is used to enhance conventional practices in various health fields, and the degree to which IoT may raise the standard of healthcare services. The main contributions in this paper are as follows: (1) importing signals from wearable devices, extracting signals from non-signals, performing peak enhancement; (2) processing and analyzing the incoming signals; (3) proposing a new stress monitoring algorithm (SMA) using wearable sensors; (4) comparing between various ML algorithms; (5) the proposed stress monitoring algorithm (SMA) is composed of four main phases: (a) data acquisition phase, (b) data and signal processing phase, (c) prediction phase, and (d) model performance evaluation phase; and (6) grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). From the findings, it is shown that random forest is best suited for this classification, with decision tree and XGBoost following closely behind.
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Affiliation(s)
- Fatma M. Talaat
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Rana Mohamed El-Balka
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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7
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Scandurra G, Arena A, Ciofi C. A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115264. [PMID: 37299990 DOI: 10.3390/s23115264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
The Internet of Things (IoT) is gaining more and more popularity and it is establishing itself in all areas, from industry to everyday life. Given its pervasiveness and considering the problems that afflict today's world, that must be carefully monitored and addressed to guarantee a future for the new generations, the sustainability of technological solutions must be a focal point in the activities of researchers in the field. Many of these solutions are based on flexible, printed or wearable electronics. The choice of materials therefore becomes fundamental, just as it is crucial to provide the necessary power supply in a green way. In this paper we want to analyze the state of the art of flexible electronics for the IoT, paying particular attention to the issue of sustainability. Furthermore, considerations will be made on how the skills required for the designers of such flexible circuits, the features required to the new design tools and the characterization of electronic circuits are changing.
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Affiliation(s)
| | - Antonella Arena
- Department of Engineering, University of Messina, 98166 Messina, Italy
| | - Carmine Ciofi
- Department of Engineering, University of Messina, 98166 Messina, Italy
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8
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Zou Y, Chu Z, Guo J, Liu S, Ma X, Guo J. Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective. Biosens Bioelectron 2023; 225:115103. [PMID: 36724658 DOI: 10.1016/j.bios.2023.115103] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/25/2022] [Accepted: 01/23/2023] [Indexed: 01/26/2023]
Abstract
Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.
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Affiliation(s)
- Yuanyuan Zou
- University of Electronic Science and Technology of China, 611731, Chengdu, China
| | - Zhengkang Chu
- School of Sensing Science and Engineering, Shanghai Jiaotong University, Shanghai, China
| | - Jiuchuan Guo
- University of Electronic Science and Technology of China, 611731, Chengdu, China; Chongqing Medical University, 400016, Chongqing, China
| | - Shan Liu
- Department of Laboratory Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology, Chengdu, 610072, China.
| | - Xing Ma
- School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
| | - Jinhong Guo
- Chongqing Medical University, 400016, Chongqing, China; School of Sensing Science and Engineering, Shanghai Jiaotong University, Shanghai, China.
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9
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Tang J, Bader DL, Moser D, Parker DJ, Forghany S, Nester CJ, Jiang L. A Wearable Insole System to Measure Plantar Pressure and Shear for People with Diabetes. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063126. [PMID: 36991838 PMCID: PMC10056665 DOI: 10.3390/s23063126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 06/12/2023]
Abstract
Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring plantar pressure and shear hinders the development of an effective foot ulcer prevention solution that could be potentially used in a daily living environment. This study reports the development of a first-of-its-kind sensorised insole system and its evaluation in laboratory settings and on human participants, indicating its potential as a wearable technology to be used in real-world applications. Laboratory evaluation revealed that the linearity error and accuracy error of the sensorised insole system were up to 3% and 5%, respectively. When evaluated on a healthy participant, change in footwear resulted in approximately 20%, 75% and 82% change in pressure, medial-lateral and anterior-posterior shear stress, respectively. When evaluated on diabetic participants, no notable difference in peak plantar pressure, as a result of wearing the sensorised insole, was measured. The preliminary results showed that the performance of the sensorised insole system is comparable to previously reported research devices. The system has adequate sensitivity to assist footwear assessment relevant to foot ulcer prevention and is safe to use for people with diabetes. The reported insole system presents the potential to help assess diabetic foot ulceration risk in a daily living environment underpinned by wearable pressure and shear sensing technologies.
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Affiliation(s)
- Jinghua Tang
- School of Engineering, University of Southampton, Southampton SO17 1BJ, UK
| | - Dan L. Bader
- School of Health Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - David Moser
- School of Engineering, University of Southampton, Southampton SO17 1BJ, UK
| | - Daniel J. Parker
- School of Health and Society, University of Salford, Salford M6 6PU, UK
| | - Saeed Forghany
- School of Allied Health Professions, Keele University, Keele, Newcastle ST5 5BG, UK
| | | | - Liudi Jiang
- School of Engineering, University of Southampton, Southampton SO17 1BJ, UK
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10
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Sarra RR, Dinar AM, Mohammed MA, Ghani MKA, Albahar MA. A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models. Diagnostics (Basel) 2022; 12:diagnostics12122899. [PMID: 36552906 PMCID: PMC9777498 DOI: 10.3390/diagnostics12122899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.
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Affiliation(s)
- Raniya R. Sarra
- Computer Engineering Department, University of Technology, Baghdad 00964, Iraq
| | - Ahmed M. Dinar
- Computer Engineering Department, University of Technology, Baghdad 00964, Iraq
- Correspondence: ; Tel.: +964-770-307-2072
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
| | - Mohd Khanapi Abd Ghani
- Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
| | - Marwan Ali Albahar
- Department of Computer Science, Umm Al Qura University, Mecca 24211, Saudi Arabia
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11
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A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition. ELECTRONICS 2022. [DOI: 10.3390/electronics11152427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.
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12
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Adaptive Synergetic Motion Control for Wearable Knee-Assistive System: A Rehabilitation of Disabled Patients. ACTUATORS 2022. [DOI: 10.3390/act11070176] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, synergetic-based adaptive control design is developed for trajectory tracking control of joint position in knee-rehabilitation system. This system is often utilized for rehabilitation of patients with lower-limb disabilities. However, this knee-assistive system is subject to uncertainties when applied to different persons undertaking exercises. This is due to the different masses and inertias of different persons. In order to cope with these uncertainties, an adaptive scheme has been proposed. In this study, an adaptive synergetic control scheme is established, and control laws are developed to ensure stable knee exoskeleton system subjected to uncertainties in parameters. Based on Lyapunov stability analysis, the developed adaptive synergetic laws are used to estimate the potential uncertainties in the coefficients of the knee-assistive system. These developed control laws guarantee the stability of the knee rehabilitation system controlled by the adaptive synergetic controller. In this study, particle swarm optimization (PSO) algorithm is introduced to tune the design parameters of adaptive and non-adaptive synergetic controllers, in order to optimize their tracking performances by minimizing an error-cost function. Numerical simulations are conducted to show the effectiveness of the proposed synergetic controllers for tracking control of the exoskeleton knee system. The results show that compared to classical synergetic controllers, the adaptive synergetic controller can guarantee the boundedness of the estimated parameters and hence avoid drifting, which in turn ensures the stability of the controlled system in the presence of parameter uncertainties.
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13
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Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Front Bioeng Biotechnol 2022; 10:876672. [PMID: 35646863 PMCID: PMC9135106 DOI: 10.3389/fbioe.2022.876672] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | - Hoda Gad
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar
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D’Antoni F, Petrosino L, Sgarro F, Pagano A, Vollero L, Piemonte V, Merone M. Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application. Bioengineering (Basel) 2022; 9:bioengineering9050183. [PMID: 35621461 PMCID: PMC9137786 DOI: 10.3390/bioengineering9050183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population. Methods: A Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) Recurrent Neural Network were optimized on glucose, insulin, and meal data of 10 virtual pediatric patients. The trained models were then implemented on two edge-computing boards to evaluate the feasibility of an edge system for glucose forecasting in terms of prediction accuracy and inference time. Results: The LSTM model achieved the best numeric and clinical accuracy when tested in the .tflite format, whereas the CNN achieved the best clinical accuracy in uint8. The inference time for each prediction was far under the limit represented by the sampling period. Conclusion: Both models effectively predict glucose in pediatric patients in terms of numerical and clinical accuracy. The edge implementation did not show a significant performance decrease, and the inference time was largely adequate for a real-time application.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
- Correspondence: (F.D.); (M.M.)
| | - Lorenzo Petrosino
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Fabiola Sgarro
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Antonio Pagano
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Vincenzo Piemonte
- Unit of Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
- Correspondence: (F.D.); (M.M.)
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15
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Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.
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16
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Xue Y, Thalmayer AS, Zeising S, Fischer G, Lübke M. Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:425. [PMID: 35062385 PMCID: PMC8780031 DOI: 10.3390/s22020425] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 12/25/2022]
Abstract
Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.
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Affiliation(s)
| | | | | | - Georg Fischer
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 9, 91058 Erlangen, Germany; (Y.X.); (A.S.T.); (S.Z.)
| | - Maximilian Lübke
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 9, 91058 Erlangen, Germany; (Y.X.); (A.S.T.); (S.Z.)
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17
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Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. ELECTRONICS 2021. [DOI: 10.3390/electronics10232888] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.
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