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Venugopal G, Sasidharan D, Swaminathan R. Analysis of induced dynamic biceps EMG signal complexity using Markov transition networks. Biomed Eng Lett 2024; 14:765-774. [PMID: 38946822 PMCID: PMC11208393 DOI: 10.1007/s13534-024-00372-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/15/2024] [Accepted: 03/04/2024] [Indexed: 07/02/2024] Open
Abstract
Purpose Surface electromyography (sEMG) is a non-invasive technique to characterize muscle electrical activity. The analysis of sEMG signals under muscle fatigue play a crucial part in the branch of neurorehabilitation, sports medicine, biomechanics, and monitoring neuromuscular pathologies. In this work, a method to transform sEMG signals to complex networks under muscle fatigue conditions using Markov transition field (MTF) is proposed. The importance of normalization to a constant Maximum voluntary contraction (MVC) is also considered. Methods For this, dynamic signals are recorded using two different experimental protocols one under constant load and another referenced to 50% MVC from Biceps brachii of 50 and 45 healthy subjects respectively. MTF is generated and network graph is constructed from preprocesses signals. Features such as average self-transition probability, average clustering coefficient and modularity are extracted. Results All the extracted features showed statistical significance for the recorded signals. It is found that during the transition from non-fatigue to fatigue, average clustering coefficient decreases while average self-transition probability and modularity increases. Conclusion The results indicate higher degree of signal complexity during non-fatigue condition. Thus, the MTF approach may be used to indicate the complexity of sEMG signals. Although both datasets showed same trend in results, sEMG signals under 50% MVC exhibited higher separability for the features. The inter individual variations of the MTF features is found to be more for the signals recorded using constant load. The proposed study can be adopted to study the complex nature of muscles under various neuromuscular conditions.
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Affiliation(s)
- G. Venugopal
- Department of Instrumentation and Control Engineering, N.S.S. College of Engineering Palakkad, Affiliated to A P J Abdul Kalam Technological University, Kerala, 678008 India
| | - Divya Sasidharan
- Department of Instrumentation and Control Engineering, N.S.S. College of Engineering Palakkad, Affiliated to A P J Abdul Kalam Technological University, Kerala, 678008 India
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics and CoE in Medical Device Regulations and Standards, IIT Madras, Chennai, 600036 India
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Girdhar N, Sharma D, Kumar R, Sahu M, Lin CC. Emerging trends in biomedical trait-based human identification: A bibliometric analysis. SLAS Technol 2024; 29:100136. [PMID: 38677477 DOI: 10.1016/j.slast.2024.100136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
Personal human identification is a crucial aspect of modern society with applications spanning from law enforcement to healthcare and digital security. This bibliometric paper presents a comprehensive analysis of recent advances in personal human identification methodologies focusing on biomedical traits. The paper examines a diverse range of research articles, reviews, and patents published over the last decade to provide insights into the evolving landscape of biometric identification techniques. The study categorizes the identified literature into distinct biomedical trait categories, including but not limited to, fingerprint and palmprint recognition, iris and retinal scanning, facial recognition, voice and speech analysis, gait recognition, and DNA-based identification. Through systematic analysis, the paper highlights key trends, emerging technologies, and interdisciplinary collaborations in each category, revealing the interdisciplinary nature of research in this field. Furthermore, the bibliometric analysis examines the geographical distribution of research efforts, identifying prominent countries and institutions contributing to advancements in personal human identification. Collaboration networks among researchers and institutions are visualized to depict the knowledge flow and collaborative dynamics within the field. Overall, this study serves as a valuable reference for researchers, practitioners, and policymakers, shedding light on the current status and potential future directions of personal human identification leveraging biomedical traits.
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Affiliation(s)
- Nancy Girdhar
- L3i, University of La Rochelle, La Rochelle, 17000, France.
| | - Deepak Sharma
- Department of Computer Science, Christian-Albrechts-University zu Kiel, Kiel, 24118, Germany.
| | - Rajeev Kumar
- Blockchain Technology Research Lab, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042, India.
| | - Monalisa Sahu
- Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.
| | - Chia-Chen Lin
- Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan.
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Vijayvargiya A, Sinha A, Gehlot N, Jena A, Kumar R, Moran K. S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality. PLoS One 2024; 19:e0301263. [PMID: 38820390 PMCID: PMC11142505 DOI: 10.1371/journal.pone.0301263] [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: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
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Affiliation(s)
- Ankit Vijayvargiya
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
| | - Aparna Sinha
- Department of Information Technology, Bansthali Vidyapeeth, Radha Kishnpura, Rajasthan, India
| | - Naveen Gehlot
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Ashutosh Jena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rajesh Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Kieran Moran
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
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Wu Z, Gu M. A novel attention-guided ECA-CNN architecture for sEMG-based gait classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7140-7153. [PMID: 37161144 DOI: 10.3934/mbe.2023308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Gait recognition and classification technology is one of the essential technologies for detecting neurodegenerative dysfunction. This paper presents a gait classification model based on a convolutional neural network (CNN) with an efficient channel attention (ECA) module for gait detection applications using surface electromyographic (sEMG) signals. First, the sEMG sensor was used to collect the experimental sample data, and various gaits of different persons were collected to construct the sEMG signal data sets of different gaits. The CNN is used to extract the features of the one-dimensional input sEMG signal to obtain the feature vector, which is input into the ECA module to realize cross-channel interaction. Then, the next part of the convolutional layer is input to learn the signal features further. Finally, the model is output and tested to obtain the results. Comparative experiments show that the accuracy of the ECA-CNN network model can reach 97.75%.
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Affiliation(s)
- Zhangjie Wu
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Minming Gu
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
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Fu J, Wang H, Na R, Jisaihan A, Wang Z, Ohno Y. Recent advancements in digital health management using multi-modal signal monitoring. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:5194-5222. [PMID: 36896542 DOI: 10.3934/mbe.2023241] [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] [Indexed: 06/18/2023]
Abstract
Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.
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Affiliation(s)
- Jiayu Fu
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Haiyan Wang
- Ma'anshan University, maanshan 243000, China
| | - Risu Na
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Shanghai Jian Qiao University, Shanghai 201315, China
| | - A Jisaihan
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Zhixiong Wang
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Ma'anshan University, maanshan 243000, China
| | - Yuko Ohno
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
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