Article
Neurosciences
Eduardo Martinez-Valdes, Roger M. Enoka, Ales Holobar, Kevin McGill, Dario Farina, Manuela Besomi, Francois Hug, Deborah Falla, Richard G. Carson, Edward A. Clancy, Catherine Disselhorst-Klug, Jaap H. van Dieen, Kylie Tucker, Simon Gandevia, Madeleine Lowery, Karen Sogaard, Thor Besier, Roberto Merletti, Matthew C. Kiernan, John C. Rothwell, Eric Perreault, Paul W. Hodges
Summary: The analysis of single motor unit (SMU) activity is crucial for understanding the neural strategies controlling muscle force. Traditionally, this analysis has been done invasively through intramuscular electromyography (EMG), but recent advances in signal processing techniques have enabled the identification of SMU activity in high-density surface electromyography (HDsEMG) recordings.
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
(2023)
Article
Engineering, Biomedical
Javier Navallas, Adrian Eciolaza, Cristina Mariscal, Armando Malanda, Javier Rodriguez-Falces
Summary: An analytical derivation of the EMG signal's amplitude probability density function is presented, showing the changes in the distribution as the muscle contraction increases. The EMG filling factor is introduced to quantify the degree to which the EMG signal has been built-up. The usefulness of the EMG filling factor and curve is demonstrated with simulated and real signals.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Chengjun Huang, Maoqi Chen, Zhiyuan Lu, Cliff S. Klein, Ping Zhou
Summary: This study examined electromyography (EMG)-force relations using both simulated and experimental approaches. Simulations showed significant variations in the EMG-force relations, depending on the size and location of motor units in the muscle. Experimental analysis of the biceps brachii muscles of healthy individuals confirmed spatial dependence of the log-transformed EMG-force relations. The findings suggest that the slope (b) of the log-transformed EMG-force relation could serve as a valuable measure for investigating muscle or motor unit changes associated with disease, injury, or aging.
BIOENGINEERING-BASEL
(2023)
Article
Neurosciences
Lucien Robinault, Ales Holobar, Sylvain Cremoux, Usman Rashid, Imran Khan Niazi, Kelly Holt, Jimmy Lauber, Heidi Haavik
Summary: Recent research has shown that spinal manipulation can improve sensorimotor integration and processing, impacting neuromuscular activity. Specifically, a study observed a significant decrease in conduction velocity following spinal manipulation during ankle dorsiflexion exercises.
Article
Computer Science, Information Systems
Yang Xu, Yang Yu, Miaojuan Xia, Xinjun Sheng, Xiangyang Zhu
Summary: A novel approach using local spatial information is proposed for the accurate and efficient decomposition of surface electromyography (sEMG) signals from forearm muscles. By leveraging the spatial distribution characteristics of motor unit action potentials, low-energy motor units can be easily identified, resulting in a faster and more efficient decomposition process.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Engineering, Biomedical
Milos Kalc, Jakob Skarabot, Matjaz Divjak, Filip Urh, Matej Kramberger, Matjaz Vogrin, Ales Holobar
Summary: We developed and tested a methodology for identifying individual motor unit (MU) firings from surface high-density EMG recorded Hoffman reflex (H-reflex). Synthetic HD-EMG signals were constructed to simulate different levels of muscle contractions and H-reflexes, while experimental H-reflexes were recorded from 12 men using HD-EMG arrays. The Convolution Kernel Compensation method was used to estimate MU filters, which accurately identified MU firings and latencies in both synthetic and experimental signals.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Jeroen Aeles, Luke A. Kelly, Andrew G. Cresswell
Summary: This study compared motor unit behavior during slow and fast contractions, and found that increasing the force of muscle contraction gradually at slower speed can increase the initial discharge rate of motor units, leading to a faster rate of force development, without changing their recruitment thresholds and mean discharge rates.
Article
Computer Science, Software Engineering
Yibo Liu, Chengcheng Li, Du Jiang, Baojia Chen, Nannan Sun, Yongcheng Cao, Bo Tao, Gongfa Li
Summary: This study focuses on continuous quantitative prediction of wrist angle under different loads using neural networks, overcoming the limitations of traditional static pattern recognition. The research is significant in accurately predicting the range of motion of the wrist angle.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Medicine, Research & Experimental
Gladys Ornelas, Hassler Bueno Garcia, David J. Bracken, Kristen Linnemeyer-Risser, Todd P. Coleman, Philip A. Weissbrod
Summary: This study utilized high-density surface electromyography (HDsEMG) to analyze the electromyographic signal during swallowing in healthy adults, and found a positive correlation between signal amplitude and bolus texture. The study also observed differences in electromyographic signal between the cranial and caudal regions of the neck, but symmetric power levels in the lateral comparison. ROC curve analysis demonstrated the ability of HDsEMG to accurately classify different textures of food to a certain extent. This research provides valuable insights for noninvasive differentiation of swallowing with varying textures, which can be applied in future diagnostic and therapeutic applications.
Article
Neurosciences
Alessio Gallina, Catherine Disselhorst-Klug, Dario Farina, Roberto Merletti, Manuela Besomi, Ales Holobar, Roger M. Enoka, Francois Hug, Deborah Falla, Karen Sogaard, Kevin McGill, Edward A. Clancy, Richard G. Carson, Jaap H. van Dieen, Simon Gandevia, Madeleine Lowery, Thor Besier, Matthew C. Kiernan, John C. Rothwell, Kylie Tucker, Paul W. Hodges
Summary: This article summarizes recommendations and steps for using high-density surface electromyography (HDsEMG) to help researchers collect, report, and interpret HDsEMG data, aiming to improve its effectiveness and accuracy in research and clinical applications.
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
(2022)
Article
Computer Science, Information Systems
Alberto Botter, Taian Vieira, Marco Carbonaro, Giacinto L. Cerone, Emma F. Hodson-Tole
Summary: This study compared the detection sensitivity of muscle fasciculations by ultrasound imaging and multichannel surface EMG techniques, and investigated their performance in different muscle regions and with varying EMG electrode configurations. The results showed that both techniques had similar sensitivities to muscle fasciculations, but had relatively low agreement between them. The study suggests that a combination of ultrasound imaging and EMG may maximize the sensitivity to muscle fasciculations.
Article
Physiology
Kylie K. Harmon, Adam S. Hamilton, Brent D. Johnson, Frank J. Bartek, Ryan M. Girts, Rob J. MacLennan, Debbie L. Hahs-Vaughn, Matt S. Stock
Summary: The study compared the motor unit action potential (MUAP) amplitude at different torque levels and in fatigue conditions. Results showed that MUAP amplitude during a 30% MVC fatiguing protocol was comparable to that during a non-fatigued 80% MVC condition.
EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY
(2021)
Article
Mathematical & Computational Biology
Carlos Magno Medeiros Queiroz, Gustavo Moreira da Silva, Steffen Walter, Luciano Brinck Peres, Luiza Maire David Luiz, Samila Carolina Costa, Kelly Christina de Faria, Adriano Alves Pereira, Marcus Fraga Vieira, Ariana Moura Cabral, Adriano de Oliveira Andrade
Summary: This study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG, evaluated the performance of multiple decomposition and adaptive filtering techniques, and estimated a set of features from experimental signals to evaluate the method's performance. The results showed variations in contamination of EEG by different facial muscles.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2022)
Article
Sport Sciences
Jakob Skarabot, Jonathan P. Folland, Jules Forsyth, Apostolos Vazoukis, Ales Holobar, Alessandro Del Vecchio
Summary: This study examined the motor unit (MU) discharge properties and MU conduction velocity in long-term resistance-trained (RT) and untrained (UT) men. The results showed that both groups had similar MU discharge characteristics during maximal and submaximal contractions, while the RT group had higher MU conduction velocity, which may explain the strength difference between the two groups.
MEDICINE & SCIENCE IN SPORTS & EXERCISE
(2023)
Article
Engineering, Multidisciplinary
Damian Gogolewski
Summary: The article explores the potential of using fractional spline wavelets for diagnosing surface textures of machine parts. The research compared the results obtained from fractional spline wavelets analysis with those from one-dimensional wavelet transform, demonstrating the advantages and disadvantages of the approach. The study shows that using fractional spline wavelets can lead to improved surface diagnostics for manufactured machine parts.
Article
Chemistry, Analytical
Xinqi Bao, Aime Kingwengwe Abdala, Ernest Nlandu Kamavuako
Summary: This study investigated the accuracy of ECG-derived respiratory rate, finding that electrode placement does not impact estimation, baseline wander and amplitude modulation algorithms perform best, and frequency domain features are helpful for accurate estimation of respiratory rate.
Article
Neurosciences
Jian Dong, Winnie Jensen, Bo Geng, Ernest Nlandu Kamavuako, Strahinja Dosen
Summary: Subdermal stimulation is a viable method to provide tactile feedback, but the quality of online control is somewhat worse compared to surface stimulation. Nevertheless, the subdermal interface could be an attractive solution for the functional application in sensate prostheses due to its minimal invasiveness, compactness, and power efficiency.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Engineering, Biomedical
Lea Tottrup, S. Farokh Atashzar, Dario Farina, Ernest Nlandu Kamavuako, Winnie Jensen
Summary: This study aimed to investigate the bidirectional information flow from the primary somatosensory cortex (SI) to the anterior cingulate cortex (ACC) in an animal model of neuropathic pain. The results showed that peripheral nerve injury led to an immediate decrease in information flow between SI and ACC, possibly due to decreased sensory input from the injured nerve. However, hours after injury, the connectivity between SI and ACC increased, indicating hypersensitivity of this pathway.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Ines Chihi, Lilia Sidhom, Ernest Nlandu Kamavuako
Summary: This paper presents a novel approach for characterising muscle force using electromyography (EMG) signals. The proposed method, based on a nonlinear Hammerstein-Wiener model, effectively estimates muscle force with reasonable accuracy. Compared to traditional artificial neural network methods, this approach demonstrates higher accuracy and reliability. The findings suggest that the use of a multimodel approach is important for improving proportional control accuracy in prostheses.
Article
Computer Science, Artificial Intelligence
Muhammad Akmal, Syed Zubair, Mads Jochumsen, Muhammad Zia Ur Rehman, Ernest Nlandu Kamavuako, Muhammad Irfan Abid, Imran Khan Niazi
Summary: This paper focuses on the key issue in designing a prosthetic hand that can classify movements based on electromyography (EMG) signals. It explores the use of multiday intramuscular EMG signals and applies matrix and tensor factorization methods to recover missing data. The results show that CP-WOPT outperforms other methods in recovering large percentage of missing data and exhibits robustness.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Chemistry, Analytical
Xinqi Bao, Yujia Xu, Ernest Nlandu Kamavuako
Summary: This study analyzed the effect of signal duration on the performance of deep learning methods for heart sound classification. The results showed that short signal duration weakened the performance of recurrent neural networks (RNNs), while it had little impact on convolutional neural networks (CNNs). RNNs outperformed CNNs when using Mel-frequency cepstrum coefficients (MFCCs) as features. The addition of dynamic information had minimal improvement on the performance of both RNNs and CNNs.
Article
Chemistry, Analytical
Carlotta Malvuccio, Ernest N. Kamavuako
Summary: As the elderly population increases, there is a need for healthcare technologies to monitor fluid intake. This study investigated the potential of combining optimal features from sEMG sensors to improve classification and estimation accuracy. Results showed promising accuracy in differentiating between liquid swallows and non-liquid swallowing events, as well as estimating the volume of fluid intake.
Editorial Material
Chemistry, Analytical
Ernest N. Kamavuako
Article
Engineering, Electrical & Electronic
Muhammad Farrukh Qureshi, Zohaib Mushtaq, Muhammad Zia Ur Rehman, Ernest Nlandu Kamavuako
Summary: This research compares multiday surface EMG recordings and measures the performance of a convolutional neural network (CNN) in enhancing myoelectric control. The proposed CNN achieves high accuracy in classifying EMG data from able-bodied individuals and amputees. It outperforms other classifiers in terms of accuracy and computational cost.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Analytical
Bingbin Wang, Levi Hargrove, Xinqi Bao, Ernest N. Kamavuako
Summary: This study compared the statistical properties of surface electromyography signals used in home and laboratory settings for prosthesis calibration, finding differences in between-calibration classification errors but not within-calibration classification errors.
Article
Engineering, Electrical & Electronic
Muhammad Farrukh Qureshi, Zohaib Mushtaq, Muhammad Zia Ur Rehman, Ernest Nlandu Kamavuako
Summary: In this study, an efficient concatenated convolutional neural network (E2CNN) is proposed for classification of surface electromyography (sEMG) extracted from the upper limb. The E2CNN model shows good response time and high accuracy when applied to LMS-based images. It has the potential to be a candidate model for real-time classification of sEMG based on LM spectrogram images.
IEEE SENSORS JOURNAL
(2023)
Article
Chemistry, Analytical
Iman Ismail, Imran Khan Niazi, Heidi Haavik, Ernest N. Kamavuako
Summary: This paper investigates the effect of sEMG features on the detection of drinking events and estimation of the amount of water swallowed per sip. The results show that sEMG can accurately distinguish drinking events from non-drinking events and provide a good estimation of fluid intake. These findings validate the potential and importance of sEMG in monitoring and estimating fluid intake.
Review
Chemistry, Analytical
Xin Chen, Ernest N. Kamavuako
Summary: Food and fluid intake monitoring is crucial for preventing dehydration, malnutrition, and obesity. While dietary monitoring has received more attention, fluid intake monitoring has been neglected. Vision-based methods offer non-intrusive solutions for monitoring, but face challenges in areas such as occlusion, privacy, computational efficiency, and practicality. This paper reviews existing research on vision-based intake monitoring methods, assesses the available literature, and identifies current challenges and research gaps.
Article
Biotechnology & Applied Microbiology
Lilia Sidhom, Ines Chihi, Mahfoudh Barhoumi, Nesrine Ben Afia, Ernest Nlandu Kamavuako, Mohamed Trabelsi
Summary: This paper aims to design a smart biosensor to predict ECG signals in a specific auscultation site, using a hybrid architecture of Artificial Neural Networks (ANNs) and Taguchi optimizer. By optimizing the input factors and improving the prediction accuracy, the developed biosensor outperforms the ANN-based biosensor in predicting ECG signals from different measurement sites.
BIOENGINEERING-BASEL
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Xinqi Bao, Fenghe Hu, Yujia Xu, Mohamed Trabelsi, Ernest Kamavuako
Summary: Paroxysmal atrial fibrillation is a type of atrial fibrillation that occurs rapidly and stops spontaneously. This study proposes a two-stage algorithm that can accurately classify and identify the onset of paroxysmal atrial fibrillation in real time.
BIOSIGNALS: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS
(2022)