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Peng B, Zhang H, Li X, Li G. A novel spatial feature extraction method based on high-density sEMG for complex hand movement recognition. Biomed Signal Process Control 2025; 103:107403. [DOI: 10.1016/j.bspc.2024.107403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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Li Z, Zhang B, Wang H, Gouda MA. Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training. SENSORS (BASEL, SWITZERLAND) 2025; 25:1355. [PMID: 40096156 PMCID: PMC11902422 DOI: 10.3390/s25051355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2025] [Revised: 02/20/2025] [Accepted: 02/21/2025] [Indexed: 03/19/2025]
Abstract
Physiological indicators are increasingly employed in sports training. However, studies on surface electromyography (sEMG) primarily focus on the analysis of isometric contraction. Research on sEMG related to isotonic contraction, which is more relevant to athletic performance, remains relatively limited. This paper examines the changes in the isotonic contraction performance of the male upper arm muscles resulting from long-term basketball training using the sEMG metrics. We recruited basketball physical education (B-PE) and non-PE majors to conduct a controlled isotonic contraction experiment to collect and analyze sEMG signals. The sample entropy event detection method was utilized to extract the epochs of active segments of data. Subsequently, statistical analysis methods were applied to extract the key sEMG time domain (TD) and frequency domain (FD) features of isotonic contraction that can differentiate between professional and amateur athletes. Machine learning methods were employed to perform ten-fold cross-validation and repeated experiments to verify the effectiveness of the features across the different groups. This paper investigates the key features and channels of interest for categorizing male participants from non-PE and B-PE backgrounds. The experimental results show that the F12B feature group consistently achieved an accuracy of between 80% and 90% with the SVM2 model, balancing both accuracy and efficiency, which can serve as evaluation indices for isotonic contraction performance of upper limb muscles during basketball training. This has practical significance for monitoring isotonic sEMG features in sports and training, as well as for providing individualized training regimens.
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Affiliation(s)
- Ziyang Li
- Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China
| | - Bowen Zhang
- Physical Education Department, Northeastern University, Wenhua Street, Shenyang 110819, China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China
| | - Mohamed Amin Gouda
- Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China
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Gopinath V, Ramakrishnan MS, Nair RR, Swaminathan R. A proposal to analyze muscle fiber type composition in the soleus muscle of untrained subjects and sprinters using surface EMG signals. Proc Inst Mech Eng H 2025; 239:144-154. [PMID: 40035228 DOI: 10.1177/09544119251321129] [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] [Indexed: 03/05/2025]
Abstract
Muscle fiber type proportion is a key determinant of fatigue, force generation, and functions of different skeletal muscles. Analysis of muscle fiber type composition aids in the assessment of athletic abilities and individualization of training methods. This study attempts to non-invasively analyze the muscle fiber type composition in the soleus (SOL) of untrained subjects (UT) and sprinters (SP) using surface electromyography-based time-frequency analysis. Signals are recorded from both groups during an isometric calf raise test with loads until fatigue. Filtered signals are segmented into epochs of 1-s duration and processed using a reassigned Morlet scalogram. Four time-frequency features namely averaged frequency, squared frequency bandwidth, averaged time, and squared time duration are extracted from the reassigned distribution and are subjected to linear regression analysis. A fiber-type-specific reassigned profile is noticed for UT and SP reflecting their distinct muscle composition during their non-fatigue and fatigue states. The regression parameters namely slope, intercept, and Adjusted R-square values are higher for the signals of SP indicating their fast-fatigue characteristics. Greater variation of features during fatigue is noticed in the signals of UT compared to SP. Among the features, the squared time duration exhibits the highest significance of p = 8.75E-07 in differentiating the signals of UT and SP during the non-fatigue state. Thus, the proposed approach is found suitable for analyzing the fiber type differences in both subject groups. This work may be further extended in sports biomechanics for studying the fiber-type transformations in muscles due to different athletic training strategies.
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Affiliation(s)
- Venugopal Gopinath
- Biomedical Instrumentation and Signal Processing Lab, Department of Instrumentation and Control Engineering, NSS College of Engineering, Palakkad, APJ Abdul Kalam Technological University, Kerala, India
| | | | - Remya R Nair
- Biomedical Instrumentation and Signal Processing Lab, Department of Instrumentation and Control Engineering, NSS College of Engineering, Palakkad, APJ Abdul Kalam Technological University, Kerala, India
| | - Ramakrishnan Swaminathan
- Non-Invasive Imaging and Diagnostics Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
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Zhang G, Yang B, Zan P, Zhang D. Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and Convolution Network (MACNet) based on Surface Electromyography. IEEE Trans Neural Syst Rehabil Eng 2024; PP:243-254. [PMID: 40030815 DOI: 10.1109/tnsre.2024.3523332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
BACKGROUND Assessment of exercise fatigue is crucial for enhancing work capacity and minimizing the risk of injury. Surface electromyography (sEMG) has been used to quantitatively assess exercise fatigue as a new technology in recent years. However, the currently available research primarily distinguishes between fatigue and non-fatigue states, offering limited and less robust findings in multilevel evaluations. METHODS This study proposes a multiple attention and convolution network (MACNet) for a three-level assessment of muscle fatigue based on sEMG. Under the designed 50% maximum voluntary contraction experimental paradigm, sEMG signals and rate of perceived exertion scale are collected from 48 subjects. MACNet is developed to assess sEMG fatigue, incorporating improved temporal attention based on sliding window, multiscale convolution, and channel-spatial attention. Finally, GradCAM visualization is used to verify the developed model's interpretation, exploring the effects of sEMG channels and time-domain characteristics on exercise fatigue. RESULTS The average classification F1-Score and accuracy of MACNet are 83.95% and 84.11% for subject-wise and 82.83% and 82.43% for cross-subject, respectively. The GradCAM visualization highlights the greater contribution of the flexor digitorum superficialis and flexor digitorum profundus in evaluating high fatigue, along with the varied impact of time-domain features on exercise fatigue assessment. CONCLUSION MACNet achieves the highest average classification accuracy and F1-Score, significantly higher than other state-of-the-art methods like SVM, RF, MFFNet, TSCNN, LMDANet, Conformer and MSFEnet, enhancing the extraction of exercise fatigue insights from sEMG channels and time-domain features.
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Sid'El Moctar SM, Rida I, Boudaoud S. Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches. Ing Rech Biomed 2024; 45:100866. [DOI: 10.1016/j.irbm.2024.100866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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Ullah A, Zhang F, Song Z, Wang Y, Zhao S, Riaz W, Li G. Surface Electromyography-Based Recognition of Electronic Taste Sensations. BIOSENSORS 2024; 14:396. [PMID: 39194625 DOI: 10.3390/bios14080396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/29/2024]
Abstract
Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.
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Affiliation(s)
- Asif Ullah
- Institute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China
| | - Fengqi Zhang
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310021, China
| | - Zhendong Song
- Institute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China
| | - You Wang
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310021, China
| | - Shuo Zhao
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310021, China
| | - Waqar Riaz
- Institute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China
| | - Guang Li
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310021, China
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Gallón VM, Vélez SM, Ramírez J, Bolaños F. Comparison of machine learning algorithms and feature extraction techniques for the automatic detection of surface EMG activation timing. Biomed Signal Process Control 2024; 94:106266. [DOI: 10.1016/j.bspc.2024.106266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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Clancy CE, Santana LF. Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications. J Physiol 2024; 602:3871-3892. [PMID: 39032073 PMCID: PMC11326976 DOI: 10.1113/jp282562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 07/02/2024] [Indexed: 07/22/2024] Open
Abstract
A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.
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Affiliation(s)
- Colleen E Clancy
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
| | - L Fernando Santana
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
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Vinothini S, Punitha N, Karthick PA, Ramakrishnan S. Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth. Biomed Eng Lett 2024; 14:727-736. [PMID: 38946820 PMCID: PMC11208349 DOI: 10.1007/s13534-024-00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 07/02/2024] Open
Abstract
Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.
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Affiliation(s)
- S. Vinothini
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - N. Punitha
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India
| | - P. A. Karthick
- Department of Instrumentation and Control, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
| | - S. Ramakrishnan
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India
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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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Peng B, Zhang H, Li X, Zheng Y, Li G. A Spatial Feature Extraction Method for Enhancing Upper Limb Motion Intent Prediction in EMG-PR System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024; 2024:1-4. [PMID: 40040108 DOI: 10.1109/embc53108.2024.10782222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
High-Density Surface Electromyography (HD-sEMG) enriches motion intention pattern recognition systems by providing more spatial information. Multichannel linear descriptors (MLD) could provide a comprehensive description of the overall state characteristics within the muscle regions. In this study, an MLD-based spatial feature extraction method was proposed to capture differences and correlations in various muscle regions during movement, ultimately enhancing the system's classification accuracy. The performance of the feature extraction method was compared with traditional time domain feature extraction method under various classifiers and different movement types. The results show that employing the proposed method with the spatial features improves the classification error rates of combined movements from 11.14% to 7.28%, and better adaptability for all classifiers utilized in this study, which shows the effect of utilization of spatial information in different muscle regions.
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Zhang W, Bai Z, Yan P, Liu H, Shao L. Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System. SENSORS (BASEL, SWITZERLAND) 2024; 24:2377. [PMID: 38610589 PMCID: PMC11014134 DOI: 10.3390/s24072377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user's intention to move or muscle fatigue. These issues impede the user's ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.
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Affiliation(s)
| | - Ziqian Bai
- School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China (P.Y.); (L.S.)
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Ma S, Zhang J, Shi C, Di P, Robertson ID, Zhang ZQ. Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1246-1256. [PMID: 38466606 DOI: 10.1109/tnsre.2024.3375320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.
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Hu B, Wang Y, Mu J. A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:144-169. [PMID: 38303417 DOI: 10.3934/mbe.2024007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of -1.658 × 10-3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: -0.4156 × 10-3) and DispEn (slope: -0.1675 × 10-3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.
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Affiliation(s)
- Baohua Hu
- School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
| | - Yong Wang
- School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
| | - Jingsong Mu
- School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Hospital, Hefei 230036, China
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Zhao H, Zhang X, Chen M, Zhou P. Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off. IEEE Trans Biomed Eng 2024; 71:160-170. [PMID: 37432836 DOI: 10.1109/tbme.2023.3294016] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.
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Su JM, Chang JH, Indrayani NLD, Wang CJ. Machine learning approach to determine the decision rules in ergonomic assessment of working posture in sewing machine operators. JOURNAL OF SAFETY RESEARCH 2023; 87:15-26. [PMID: 38081690 DOI: 10.1016/j.jsr.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 04/10/2023] [Accepted: 08/10/2023] [Indexed: 12/18/2023]
Abstract
INTRODUCTION There are some inherent problems with the use of observation methods in the ergonomic assessment of working posture, namely the stability and precision of the measurements. This study aims to use a machine learning (ML) approach to avoid the subjectivity bias of observational methods in ergonomic assessments and further identify risk patterns for work-related musculoskeletal disorders (WMSDs) among sewing machine operators. METHODS We proposed a decision tree analysis scheme for ergonomic assessment in working postures (DTAS-EAWP). First, DTAS-EAWP used computer vision-based technology to detect the body movement angles from the on-site working videos to generate a dataset of risk scores through the criteria of Rapid Entire Body Assessment (REBA) for sewing machine operators. Second, data mining techniques (WEKA) using the C4.5 algorithm were used to construct a representative decision tree (RDT) with paths of various risk levels, and attribute importance analysis was performed to determine the critical body segments for WMSDs. RESULTS DTAS-EAWP was able to recognize 11,211 samples of continuous working postures in sewing machine operation and calculate the corresponding final REBA scores. A total of 13 decision rules were constructed in the RDT, with over 95% prediction accuracy and 83% path coverage, to depict the possible risk tendency in the working postures. Through RDT and attribute importance analysis, it was identified that the lower arm and the upper arms exhibited as critical segments that significantly increased the risk levels for WMSDs. CONCLUSIONS This study demonstrates that ML approach with computer vision-based estimation and DT analysis are feasible for comprehensively exploring the decision rules in ergonomic assessment of working postures for risk prediction of WMSDs in sewing machine operators. PRACTICAL APPLICATIONS This DTAS-EAWP can be applied in manufacturing industries to automatically analyze working postures and identify risk patterns of WMSDs, leading to the development of effectively preventive interventions.
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Affiliation(s)
- Jun-Ming Su
- Department of Information and Learning Technology, National University of Tainan, 33, Sec. 2, Shu-Lin St., 700301 Tainan, Taiwan
| | - Jer-Hao Chang
- Department of Occupational Therapy and Institute of Allied Health Science, College of Medicine, National Cheng Kung University, No.1, University Road, 701401 Tainan, Taiwan
| | - Ni Luh Dwi Indrayani
- Department of Nursing, College of Medicine, National Cheng Kung University, No.1, University Road, 701401 Tainan, Taiwan
| | - Chi-Jane Wang
- Department of Nursing, College of Medicine, National Cheng Kung University, No.1, University Road, 701401 Tainan, Taiwan.
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Otálora S, Segatto MEV, Monteiro ME, Múnera M, Díaz CAR, Cifuentes CA. Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:9291. [PMID: 38005677 PMCID: PMC10674769 DOI: 10.3390/s23229291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/08/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023]
Abstract
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.
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Affiliation(s)
- Sophia Otálora
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | - Marcelo E. V. Segatto
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | | | - Marcela Múnera
- Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK;
| | - Camilo A. R. Díaz
- Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil; (S.O.); (M.E.V.S.); (C.A.R.D.)
| | - Carlos A. Cifuentes
- Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK;
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Karthick PA, Selvaraju V, Swaminathan R. Empirical Mode Decomposition Based Measures for Investigating the Progression of Pregnancy from Uterine EMG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083708 DOI: 10.1109/embc40787.2023.10340089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The objective of this study is to analyze the uterine electromyography (uEMG) signals to study the progression of pregnancy under term condition (gestational age > 36 weeks) using EMD-based time-frequency features. uEMG signals are obtained from the multiple public datasets during two conditions, namely T1 (acquired < 26 gestational weeks) and T2 (acquired ≥ 26 gestational weeks). The considered signals are preprocessed. Empirical mode decomposition is applied to decompose the signals and time-frequency features, such as median frequency (MDF), mean frequency (MNF), peak frequency and peak magnitude, are extracted from each intrinsic mode functions and statistically analyzed. The results depict that the obtained time-frequency features are able to distinguish between T1 and T2 conditions. The extracted features, namely MNF and MDF, are observed to decrease from T1 to T2 conditions. These features are found to have higher effect size, confirming the better differentiation between T1 and T2 conditions. It appears that EMD-based time-frequency features can aid in studying the evolving changes in uterine contractions towards labor.
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Biondi FN, Graf F, Cort J. On the potential of pupil size as a metric of physical fatigue during a repeated handle push/pull task. APPLIED ERGONOMICS 2023; 110:104025. [PMID: 37071948 DOI: 10.1016/j.apergo.2023.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/24/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023]
Abstract
Force output and muscle activity represent the gold standards for measuring physical fatigue. This study explores using ocular metrics for tracking changes in physical fatigue during the completion of a repeated handle push/pull task. Participants completed this task over three trials, and pupil size was recorded by means of a head-mounted eye-tracker. Blink frequency was also measured. Force impulse and maximum peak force were used as ground-truth measures of physical fatigue. As expected, a reduction in peak force and impulse was observed over time as participants became more fatigued. More interestingly, pupil size was also found to decrease from trial 1 through trial 3. No changes in blink rate were found with increasing physical fatigue. While exploratory in nature, these findings add to the sparse literature exploring the use of ocular metrics in Ergonomics. They also advance the use of pupil size as a possible future alternative for physical fatigue detection.
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Affiliation(s)
- Francesco N Biondi
- Human Systems Lab, University of Windsor, Windsor, Ontario, Canada; Department of Psychology, University of Utah, Salt Lake City, UT, USA.
| | | | - Joel Cort
- Occupational Simulation and Ergonomics Lab, University of Windsor, Windsor, Ontario, Canada
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20
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Bouteraa Y, Abdallah IB, Boukthir K. A New Wrist-Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation. Bioengineering (Basel) 2023; 10:bioengineering10020219. [PMID: 36829713 PMCID: PMC9952609 DOI: 10.3390/bioengineering10020219] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023] Open
Abstract
In this research, a new remote rehabilitation system was developed that integrates an IoT-based connected robot intended for wrist and forearm rehabilitation. In fact, the mathematical model of the wrist and forearm joints was developed and integrated into the main controller. The proposed new rehabilitation protocol consists of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of wrist-forearm biomechanics while the active components are extracted in the second session. The third session consists of performing continuous exercises using the determined dynamic model of the forearm-wrist joints, taking into account the torque generated by muscle fatigue. The main objective of this protocol is to determine the state level of the affected wrist and above all to provide a dynamic model in which the torque generated by the robot and the torque supplied by the patient are combined, taking into account the constraints of fatigue. A Support Vector Machine (SVM) classifier is designed for the estimation of muscle fatigue based on the features extracted from the electromyography (EMG) signal acquired from the patient. The results show that the developed rehabilitation system allows a good progression of the joint's range of motion as well as the resistive-active torques.
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Affiliation(s)
- Yassine Bouteraa
- Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
- Correspondence:
| | - Ismail Ben Abdallah
- Control and Energy Management Laboratory, National School of Engineering of Sfax, Higher Institute of Biotechnology of Sfax, University of Sfax, Sfax 3038, Tunisia
| | - Khalil Boukthir
- Research Laboratory in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia
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21
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Hüpen P, Kumar H, Shymanskaya A, Swaminathan R, Habel U. Impulsivity Classification Using EEG Power and Explainable Machine Learning. Int J Neural Syst 2023; 33:2350006. [PMID: 36632032 DOI: 10.1142/s0129065723500065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.
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Affiliation(s)
- Philippa Hüpen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - Translational Brain Medicine, Aachen, Germany
| | - Himanshu Kumar
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Aliaksandra Shymanskaya
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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22
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Wang Y, Lu C, Zhang M, Wu J, Tang Z. Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training. Healthcare (Basel) 2022; 10:2292. [PMID: 36421616 PMCID: PMC9691149 DOI: 10.3390/healthcare10112292] [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: 10/09/2022] [Revised: 11/10/2022] [Accepted: 11/12/2022] [Indexed: 09/08/2024] Open
Abstract
Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract surface electromyogram (sEMG) features and establish recognition models to classify muscle states of the target muscles in the isokinetic strength training of the knee joint. First, an experiment was carried out to collect the sEMG signals of the target muscles. Second, two nonlinear dynamic indexes, wavelet packet entropy (WPE) and power spectrum entropy (PSE), were extracted from the obtained sEMG signals to verify the feasibility of characterizing muscle fatigue. Third, a convolutional neural network (CNN) recognition model was constructed and trained with the obtained sEMG experimental data to enable the extraction and recognition of EMG deep features. Finally, the CNN recognition model was compared with multiple support vector machines (Multi-SVM) and multiple linear discriminant analysis (Multi-LDA). The results showed that the CNN model had a better classification accuracy. The overall recognition accuracy of the CNN model applied to the test data (91.38%) was higher than that of the other two models, which verified that the CNN dynamic fatigue recognition model based on subjective and objective information feedback had better recognition performance. Furthermore, training on a larger dataset could further improve the recognition accuracy of the CNN recognition model.
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Affiliation(s)
| | - Chunfu Lu
- Industrial Design Department, Zhejiang University of Technology, Hangzhou 310023, China
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Xue J, Sun Z, Duan F, Caiafa CF, Solé-Casals J. Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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24
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Wang L, Song X, Yang H, Wang C, Shao Q, Tao H, Qiao M, Niu W, Liu X. Are the antagonist muscle fatigued during a prolonged isometric fatiguing elbow flexion at very low forces for young adults? Front Physiol 2022; 13:956639. [PMID: 36277214 PMCID: PMC9585301 DOI: 10.3389/fphys.2022.956639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
The aim of this study was to examine whether antagonist muscles may be fatigued during a prolonged isometric fatiguing elbow flexion at very low forces. Twelve healthy male subjects sustained an isometric elbow flexion at 10% maximal voluntary contraction torque until exhaustion while multichannel electromyographic signals were collected from the biceps brachii (BB) and triceps brachii (TB). Muscle fiber conduction velocity (CV) and fractal dimension (FD) of both muscles were calculated to reflect peripheral and central fatigue. CV and FD of TB as well as FD of BB decreased progressively during the sustained fatiguing contraction, while the CV of BB declined at the beginning of the contraction and then increased progressively until the end of the contraction. The result may indicate that during the sustained low-force isometric fatiguing contraction, antagonist muscle may be peripherally fatigued, and changes in coactivation activities were modulated not only by central neuronal mechanisms of common drive but also by peripheral metabolic factors.
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Affiliation(s)
- Lejun Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
- *Correspondence: Lejun Wang, ; Xiaodong Liu,
| | - Xiaoqian Song
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Hua Yang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Ce Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Qineng Shao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Haifeng Tao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Minjie Qiao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Wenxin Niu
- School of Medicine, Tongji University, Shanghai, China
| | - Xiaodong Liu
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
- *Correspondence: Lejun Wang, ; Xiaodong Liu,
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25
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Sun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of Surface Electromyography in Exercise Fatigue: A Review. Front Syst Neurosci 2022; 16:893275. [PMID: 36032326 PMCID: PMC9406287 DOI: 10.3389/fnsys.2022.893275] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
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26
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Ramakrishnan MS, Ganapathy N. Phenotypes based Classification of Blood-Brain-Barrier Drugs using Feature Selection Methods and Extreme Gradient Boosting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1346-1349. [PMID: 36085687 DOI: 10.1109/embc48229.2022.9871431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this work, an attempt has been made to discriminate drug with blood brain barrier (BBB) permeability using clinical phenotypes and extreme gradient boosting (XGBoost) methods. For this, the drug name and their clinical phenotypes namely side effects and indications are obtained from public available database. Prominent clinical phenotypes are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Four machine algorithms namely k-Nearest Neighbours, support vector machines, rotation forest and XGBoost are used for classification of BBB drugs. The result show that the proposed clinical phenotypes based features are able to distinguish drugs with BBB permeability. The maximum number of clinical phenotypes (69%) is reduced by BPSO and GA for classification. The XGBoost method is found to be most accurate [Formula: see text] is discriminating drugs with BBB permeability. The proposed approach are found to be capable of handling multi-parametric characteristics of the drugs. Particularly, the combination of XGBoost with combination of side effects and indications could be used for precision medicine applications. Clinical relevance- This establishes XGBoost approach for improved BBB permeability based drug classification with F1 =98.7% using exclusively clinical phenotypes.
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27
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Beltran Martinez K, Nazarahari M, Rouhani H. K-score: A novel scoring system to quantify fatigue-related ergonomic risk based on joint angle measurements via wearable inertial measurement units. APPLIED ERGONOMICS 2022; 102:103757. [PMID: 35378482 DOI: 10.1016/j.apergo.2022.103757] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/21/2022] [Accepted: 03/27/2022] [Indexed: 06/14/2023]
Abstract
Work-related musculoskeletal disorders have been recognized as a global problem that affects millions of people annually. Fatigue is one of the main contributors to musculoskeletal disorders. Thus, this study investigated fatigue detection based on the measured body motion by wearable inertial measurement units. We quantified the body motion during manual handling tasks using a novel kinematic score (i.e., K-score), and the Rapid Entire Body Assessment (REBA). K-score and REBA were calculated using joint angles. Nevertheless, unlike REBA, K-score showed a significant correlation (Spearman's correlation coefficient of ρ(302) = 0.21, p < 0.05) with electromyography (EMG) signal amplitude, which was affected by muscle fatigue. Therefore, in-field measurement of K-score using inertial measurement units could detect the fatigue-induced change of body motion in long-duration manual handling tasks. Our proposed K-score can be used to assess fatigue-related ergonomic risk in long-term and real-world working conditions without the need for tedious EMG recording at workplaces.
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Affiliation(s)
- Karla Beltran Martinez
- Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada.
| | - Milad Nazarahari
- Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada; Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada.
| | - Hossein Rouhani
- Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada.
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Mohanavelu K, Poonguzhali S, Janani A, Vinutha S. Machine learning-based approach for identifying mental workload of pilots. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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29
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Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:5143757. [PMID: 35291422 PMCID: PMC8901308 DOI: 10.1155/2022/5143757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/28/2022] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect (P < 0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) (P < 0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.
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30
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Li D, Chen C. Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion. BMC Med Inform Decis Mak 2022; 22:67. [PMID: 35303877 PMCID: PMC8932330 DOI: 10.1186/s12911-022-01808-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/11/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man–machine devices and improving the safety of Pilates rehabilitation.
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Affiliation(s)
- Dujuan Li
- North Sichuan Medical College, Nanchong, 631000, China
| | - Caixia Chen
- China West Normal University, Nanchong, 631000, China.
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31
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Qassim HM, Hasan WZW, Ramli HR, Harith HH, Mat LNI, Ismail LI. Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier. SENSORS 2022; 22:s22051900. [PMID: 35271046 PMCID: PMC8914984 DOI: 10.3390/s22051900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022]
Abstract
The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.
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Affiliation(s)
- Hassan M. Qassim
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (H.M.Q.); (H.R.R.); (L.I.I.)
- Department of Medical Instrumentation Engineering, Technical Engineering College of Mosul, Northern Technical University, Mosul 41001, Iraq
| | - Wan Zuha Wan Hasan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (H.M.Q.); (H.R.R.); (L.I.I.)
- Correspondence:
| | - Hafiz R. Ramli
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (H.M.Q.); (H.R.R.); (L.I.I.)
| | - Hazreen Haizi Harith
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Liyana Najwa Inche Mat
- Department of Neurology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Luthffi Idzhar Ismail
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (H.M.Q.); (H.R.R.); (L.I.I.)
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A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method. ELECTRONICS 2022. [DOI: 10.3390/electronics11050691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training.
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Davahli A, Shamsi M, Abaei G, Khosravi A. Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Azam Davahli
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Mahboubeh Shamsi
- Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran
| | - Golnoush Abaei
- Department of Software Engineering, School of Information Technology, Monash University Malaysia, Malaysia
| | - Arash Khosravi
- Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
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Mokri C, Bamdad M, Abolghasemi V. Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques. Med Biol Eng Comput 2022; 60:683-699. [PMID: 35029815 PMCID: PMC8854337 DOI: 10.1007/s11517-021-02466-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/05/2021] [Indexed: 11/15/2022]
Abstract
The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length. Graphical Abstract ![]()
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Affiliation(s)
- Chiako Mokri
- Corrective Exercise and Rehabilitation Laboratory, Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Mahdi Bamdad
- Corrective Exercise and Rehabilitation Laboratory, Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Vahid Abolghasemi
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK.
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Chan VCH, Ross GB, Clouthier AL, Fischer SL, Graham RB. The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review. APPLIED ERGONOMICS 2022; 98:103574. [PMID: 34547578 DOI: 10.1016/j.apergo.2021.103574] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.
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Affiliation(s)
- Victor C H Chan
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 200 Lees Avenue, Ottawa, Ontario, K1N 6N5, Canada
| | - Gwyneth B Ross
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 200 Lees Avenue, Ottawa, Ontario, K1N 6N5, Canada
| | - Allison L Clouthier
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 200 Lees Avenue, Ottawa, Ontario, K1N 6N5, Canada
| | - Steven L Fischer
- Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
| | - Ryan B Graham
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 200 Lees Avenue, Ottawa, Ontario, K1N 6N5, Canada; Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada.
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36
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Bizzego A, Gabrieli G, Neoh MJY, Esposito G. Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets. Bioengineering (Basel) 2021; 8:bioengineering8120193. [PMID: 34940346 PMCID: PMC8698903 DOI: 10.3390/bioengineering8120193] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/05/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
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Affiliation(s)
- Andrea Bizzego
- Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy;
| | - Giulio Gabrieli
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
| | - Michelle Jin Yee Neoh
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
| | - Gianluca Esposito
- Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy;
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
- Correspondence: or
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Garro F, Chiappalone M, Buccelli S, De Michieli L, Semprini M. Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front Neurorobot 2021; 15:742163. [PMID: 34776920 PMCID: PMC8579108 DOI: 10.3389/fnbot.2021.742163] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.
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Affiliation(s)
- Florencia Garro
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
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Schulte RV, Prinsen EC, Hermens HJ, Buurke JH. Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition. Front Robot AI 2021; 8:710806. [PMID: 34760930 PMCID: PMC8573095 DOI: 10.3389/frobt.2021.710806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022] Open
Abstract
Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.
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Affiliation(s)
- Robert V Schulte
- Roessingh Research and Development, Enschede, Netherlands.,Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
| | - Erik C Prinsen
- Roessingh Research and Development, Enschede, Netherlands.,Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
| | - Hermie J Hermens
- Roessingh Research and Development, Enschede, Netherlands.,Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
| | - Jaap H Buurke
- Roessingh Research and Development, Enschede, Netherlands.,Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
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Chen S, Xu K, Yao X, Ge J, Li L, Zhu S, Li Z. Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106451. [PMID: 34644668 DOI: 10.1016/j.cmpb.2021.106451] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Human factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and electromyography (EMG) signals in miners working under the extreme conditions of a plateau environment. To provide insights into models for fatigue classification and recognition based on machine learning, multi-modal feature information fusion and miner fatigue identification based on ECG and EMG signals as physiological indicators were studied. METHODS Fifty-five miners were randomly selected as field test subjects, and characteristic signals were extracted from 110 groups of ECG and EMG signals as the basic signals for fatigue analysis. We conducted principal component analysis (PCA) and grey relational analysis (GRA) on the measurement indicators. Support vector machine (SVM), random forest (RF) and extreme gradient boosting (XG-Boost) machine learning models were used for fatigue classification based on multi-modal information fusion. The area under the receiver operating characteristic (ROC) curve and the confusion matrix were used to evaluate the performance of the recognition models. RESULTS The ECG and EMG signals showed obvious changes with fatigue. The results of fatigue model identification showed that PCA feature fusion was superior to GRA feature fusion for all three machine learning approaches, and XG-Boost achieved the best performance, with a recognition accuracy of 89.47%, a sensitivity and specificity of 100%, and an AUC of 0.90. The SVM model also showed good recognition performance (89.47% accuracy, AUC=0.89). The worst performance was that of the RF model, with a recognition accuracy of only 78.95%. CONCLUSIONS This study shows that the physiological indexes of ECG and EMG exhibit obvious, regular changes with fatigue and that it is feasible to use SVM, RF and XG-Boost models for miner fatigue identification. The PCA fusion technique can improve the identification accuracy more than the GRA method. XG-Boost classification yields the best accuracy and robustness. This study can serve as a reference for clinical research on the identification of human fatigue at high altitudes and for the clinical study of acute mountain sickness and human acclimatization to high altitudes.
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Affiliation(s)
- Shoukun Chen
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
| | - Xiwen Yao
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
| | - Ji Ge
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; School of Resources and Environmental Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China.
| | - Li Li
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Siyi Zhu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
| | - Zhengrong Li
- Yunnan Diqing Non-ferrous Metals Co., Ltd, Yunnan 674400, China
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40
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Pinto-Bernal MJ, Cifuentes CA, Perdomo O, Rincón-Roncancio M, Múnera M. A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States. SENSORS (BASEL, SWITZERLAND) 2021; 21:6401. [PMID: 34640722 PMCID: PMC8513020 DOI: 10.3390/s21196401] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/03/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023]
Abstract
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients' intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual's characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.
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Affiliation(s)
- Maria J. Pinto-Bernal
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
| | - Carlos A. Cifuentes
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
| | - Oscar Perdomo
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111711, Colombia;
| | | | - Marcela Múnera
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
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Duan L. EMPIRICAL ANALYSIS ON THE REDUCTION OF SPORTS INJURY BY FUNCTIONAL MOVEMENT SCREENING METHOD UNDER BIOLOGICAL IMAGE DATA. REV BRAS MED ESPORTE 2021. [DOI: 10.1590/1517-8692202127042021_0110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
ABSTRACT Introduction: Sports recognition technology gradually mature. Among them, wearable sensors have attracted wide attention because of their accurate recognition. Objective: The following squats are used as an example to determine whether CNN and EMG signals determine whether functional motion is standard. Methods: Based on the FMS of EMG, 80 students of the same grade are randomly selected from the Physical Education School of XX University for the experiment and the results are verified. Results: The results show that the GBC can classify the EMG signal of the three functional movements more accurately, and the classification accuracy rate of squat, stride, and straight lunge squat is 91%, 89%, and 90%, respectively. The decision tree has a good ability to judge whether the functional movement is standard or not, and the accuracy of judgment can reach more than 98%. In conclusion, EMG-based FMS can effectively detect early sports injuries and plays a good role in reducing sports injuries. Conclusions: The classification effect of the squat is the most obvious, reaching 91%, and its recognition ability is the strongest. Level of evidence II; Therapeutic studies - investigation of treatment results.
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Aguirre A, Pinto MJ, Cifuentes CA, Perdomo O, Díaz CAR, Múnera M. Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise. SENSORS (BASEL, SWITZERLAND) 2021; 21:5006. [PMID: 34372241 PMCID: PMC8348066 DOI: 10.3390/s21155006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/02/2021] [Accepted: 07/13/2021] [Indexed: 12/11/2022]
Abstract
Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.
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Affiliation(s)
- Andrés Aguirre
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (A.A.); (M.J.P.); (M.M.)
| | - Maria J. Pinto
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (A.A.); (M.J.P.); (M.M.)
| | - Carlos A. Cifuentes
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (A.A.); (M.J.P.); (M.M.)
| | - Oscar Perdomo
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111711, Colombia;
| | - Camilo A. R. Díaz
- Electrical Engineering Department, Federal University of Espirito Santo, Vitoria 29075-910, Brazil;
| | - Marcela Múnera
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (A.A.); (M.J.P.); (M.M.)
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Wang Y, Wang H, Li H, Ullah A, Zhang M, Gao H, Hu R, Li G. Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography. SENSORS (BASEL, SWITZERLAND) 2021; 21:4994. [PMID: 34372231 PMCID: PMC8348720 DOI: 10.3390/s21154994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/09/2021] [Accepted: 07/11/2021] [Indexed: 11/18/2022]
Abstract
Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.
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Affiliation(s)
| | | | | | | | | | | | | | - Guang Li
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.W.); (H.W.); (H.L.); (A.U.); (M.Z.); (H.G.); (R.H.)
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K DB, P A K, S R. Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals. Comput Methods Biomech Biomed Engin 2021; 25:320-332. [PMID: 34289775 DOI: 10.1080/10255842.2021.1955104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In this study, an attempt has been made to develop an automated muscle fatigue detection system using cyclostationary based geometric features of surface electromyography (sEMG) signals. For this purpose, signals are acquired from fifty-eight healthy volunteers under dynamic muscle fatiguing contractions. The sEMG signals are preprocessed and the epochs of signals under nonfatigue and fatigue conditions are considered for the analysis. A computationally effective Fast Fourier transform based accumulation algorithm is adapted to compute the spectral correlation density coefficients. The boundary of spectral density coefficients in the complex plane is obtained using alpha shape method. The geometric features, namely, perimeter, area, circularity, bending energy, eccentricity and inertia are extracted from the shape and the machine learning models based on multilayer perceptron (MLP) and extreme learning machine (ELM) are developed using these biomarkers. The results show that the cyclostationarity increases in fatigue condition. All the extracted features are found to have significant difference in the two conditions. It is found that the ELM model based on prominent features classifies the sEMG signals with a maximum accuracy of 94.09% and F-score of 93.75%. Therefore, the proposed approach appears to be useful for analysing the fatiguing contractions in neuromuscular conditions.
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Affiliation(s)
- Divya Bharathi K
- Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Karthick P A
- Physiological Measurements and Instrumentation Laboratory, Department of Instrumentation and Control Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
| | - Ramakrishnan S
- Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
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45
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S. EJ, K. DB, P.A. K, S. R. Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning. SENSORS 2021; 21:s21103542. [PMID: 34069717 PMCID: PMC8161329 DOI: 10.3390/s21103542] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/04/2021] [Accepted: 05/07/2021] [Indexed: 11/20/2022]
Abstract
Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with W=3 and V=3 yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.
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Chen S, Xu K, Yao X, Zhu S, Zhang B, Zhou H, Guo X, Zhao B. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Comput Biol Med 2021; 133:104413. [PMID: 33915363 DOI: 10.1016/j.compbiomed.2021.104413] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 12/11/2022]
Abstract
Fatigue-induced human error is a leading cause of accidents. The purpose of this exploratory study in China was to perform field tests to measure fatigue psychophysiological parameters, such as electrocardiography (ECG), electromyography (EMG), pulse, blood pressure, reaction time and vital capacity (VC), in miners in high-altitude and cold areas and to perform multi-feature information fusion and fatigue identification. Forty-five miners were randomly selected as subjects for a field test, and feature signals were extracted from 90 psychophysiological features as basic signals for fatigue analysis. Fatigue sensitivity indices were obtained by Pearson correlation analysis, t-test and receiver operating characteristic (ROC) curve performance evaluation. The ECG time-domain, ECG frequency-domain, EMG, VC, systolic blood pressure (SBP), and pulse were significantly different after miner fatigue. The support vector machine (SVM) and random forest (RF) techniques were used to classify and identify fatigue by information fusion and factor combination. The optimal fatigue classification factors were ECG-FD (CV Accuracy = 85.0%) and EMG (CV Accuracy = 90.0%). The optimal combination of factors was ECG-TD + ECG-FD + EMG (CV accuracy = 80.0%). Furthermore, SVM machine learning had a good recognition effect. This study shows that SVM and RF can effectively identify miner fatigue based on fatigue-related factor combinations. ECG-FD and EMG are the best indicators of fatigue, and the best performance and robustness are obtained with three-factor combination classification. This study on miner fatigue identification provides a reference for research on clinical medicine and the identification of human fatigue under high-altitude, cold and low-oxygen conditions.
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Affiliation(s)
- Shoukun Chen
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Xiwen Yao
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Siyi Zhu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Bohan Zhang
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Haodong Zhou
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Xin Guo
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Bingfeng Zhao
- Yunnan Diqing Non-ferrous Metals Co., Ltd, Yunnan, 674400, China.
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Goubault E, Verdugo F, Pelletier J, Traube C, Begon M, Dal Maso F. Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters. Sci Rep 2021; 11:8117. [PMID: 33854088 PMCID: PMC8047012 DOI: 10.1038/s41598-021-87403-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 03/23/2021] [Indexed: 02/02/2023] Open
Abstract
Muscle fatigue is considered as a risk factor for developing playing-related muscular disorders among professional pianists and could affect musical performance. This study investigated in 50 pianists the effect of fatiguing repetitive piano sequences on the development of forearm muscle fatigue and on piano performance parameters. Results showed signs of myoelectric manifestation of fatigue in the 42-electromyographic bipolar electrodes positioned on the forearm to record finger and wrist flexor and extensor muscles, through a significant non-constant decrease of instantaneous median frequency during two repetitive Digital (right-hand 16-tones sequence) and Chord (right-hand chords sequence) excerpts, with extensor muscles showing greater signs of fatigue than flexor muscles. In addition, muscle fatigue negatively affected key velocity, a central feature of piano sound intensity, in both Digital and Chord excerpts, and note-events, a fundamental aspect of musicians' performance parameter, in the Chord excerpt only. This result highlights that muscle fatigue may alter differently pianists' musical performance according to the characteristics of the piece played.
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Affiliation(s)
- Etienne Goubault
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada
| | - Felipe Verdugo
- grid.14709.3b0000 0004 1936 8649Input Devices and Music Interaction Laboratory, Centre for Interdisciplinary Research in Music Media and Technology, Schulich School of Music, McGill University, Montreal, QC Canada ,grid.267180.a0000 0001 2168 0285EXPRESSION Team, Université Bretagne-Sud, Vannes, France
| | - Justine Pelletier
- grid.38678.320000 0001 2181 0211Laboratoire Arts vivants et interdisciplinarité, Département de danse, Université du Québec à Montréal, Montreal, QC Canada
| | - Caroline Traube
- grid.14848.310000 0001 2292 3357Laboratoire de recherche sur le geste musicien, Faculté de musique, Université de Montréal, Montreal, QC Canada
| | - Mickaël Begon
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada ,grid.411418.90000 0001 2173 6322Sainte-Justine Hospital Research Center, Montreal, QC Canada
| | - Fabien Dal Maso
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada ,Centre interdisciplinaire de recherche sur le cerveau et l’apprentissage, Montréal, QC Canada
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Bizzego A, Gabrieli G, Esposito G. Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset. Bioengineering (Basel) 2021; 8:35. [PMID: 33800842 PMCID: PMC8058952 DOI: 10.3390/bioengineering8030035] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 02/25/2021] [Accepted: 03/03/2021] [Indexed: 11/16/2022] Open
Abstract
While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.
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Affiliation(s)
- Andrea Bizzego
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto (Trento), Italy;
| | - Giulio Gabrieli
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
| | - Gianluca Esposito
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto (Trento), Italy;
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
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50
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Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict COVID-19 infection. CHAOS, SOLITONS, AND FRACTALS 2020; 140:110120. [PMID: 33519109 PMCID: PMC7833512 DOI: 10.1016/j.chaos.2020.110120] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/10/2020] [Indexed: 05/05/2023]
Abstract
The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies.
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Affiliation(s)
- Talha Burak Alakus
- Kirklareli University, Engineering Faculty, Department of Software Engineering, Kirklareli, 39000, Turkey
| | - Ibrahim Turkoglu
- Firat University, Technology Faculty, Department of Software Engineering, Elazig, 23119, Turkey
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