Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

Varrecchia, Tiwana; Ranavolo, Alberto; Conforto, Silvia; Nunzio, Alessandro Marco De; Arvanitidis, Michail; Draicchio, Francesco; Falla, Deborah

doi:10.1016/j.apergo.2021.103456

Cited by 16 publications

(24 citation statements)

References 64 publications

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“…These areas are affected by the barriers and difficulties that have been discussed elsewhere [78]. The early NIOSH publication by Soderberg [79], the book by Kumar and Mital [80], the review by Marras [81], and the recent works of Gazzoni et al [82] and Varecchia et al [83], among many other contributions, underline the importance of teaching the proper use of sEMG in ergonomics and occupational medicine. Similarly, the early work of Clarys [84], Chapter 9 of Electrodiagnosis in New Frontiers of Clinical Research [85], and the recent work of Felici [77], among many other works, outline 30 years of efforts in the field of sEMG use in sports and exercise.…”

Section: Issues In Ergonomics Occupational Medicine and Sport Science...mentioning

confidence: 99%

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Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Campanini

Merlo

Disselhorst‐Klug

et al. 2022

Sensors

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Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

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Section: Issues In Ergonomics Occupational Medicine and Sport Science...mentioning

confidence: 99%

“…Further considerations for sEMG applications in sports and ergonomics are relevant and discussed in the extensive literature [83,86] but exceed the purpose of this work. In all studies, the proper acquisition of sEMG is fundamental to reaching consistent conclusions.…”

Section: Issues In Ergonomics Occupational Medicine and Sport Science...mentioning

confidence: 99%

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Campanini

Merlo

Disselhorst‐Klug

et al. 2022

Sensors

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“…The MN-driven muscle model (Figure 3) is driven by an input vector of experimental MN spike trains, so extending the classic single input EMG-driven approach, in which the individual MN contributions are lumped into a unique neural control to the muscle, taken as the rectified-normalized-filtered envelope of recorded bipolar EMGs (bEMGs) (Hof & Van den Berg, 1981; Zajac Felix, 1989; Lloyd & Besier, 2003). bEMGs are also challenging to interpret (De Luca, Carlo J., 1997) and come with intrinsic experimental limitations that are circumvented by HDEMG and/or intramuscular approaches (Staudenmann et al, 2006; Mesin et al, 2009; Varrecchia et al, 2021), for which current decomposition techniques are a reliable process across operators (Hug, Avrillon et al, 2021). The MN-driven muscle model (Figure 3) also represents an improvement with respect to recent studies which merged the individual MN spike trains obtained from decomposed HDEMG or intramuscular recordings into a unique neural control, the CST N r , given as input to Hill-type (Sartori et al, 2017; Kapelner et al, 2020) and twitch-type (Thompson et al, 2018) muscle models.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

Caillet

Phillips

Farina

et al. 2022

Preprint

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Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from EMG decomposition. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of leaky fire- and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. Representative applications of the method are also presented for the prediction of activity-dependant changes in MN intrinsic properties and in MN-driven neuromuscular modelling. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neurophysiology of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.Author SummaryOur experimental understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to the observation of small samples of active MNs obtained from EMG decomposition. EMG decomposition therefore provides an important but incomplete description of the role of individual MNs in the firing activity of the complete MN pool, which limits our understanding of the neural strategies of the whole MN pool and of how the firing activity of each MN contributes to the neural drive to muscle. Here, we combine decomposed high-density EMG (HDEMG) data and a physiologically realistic model of MN population to predict the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. In brief, an experimental estimation of the synaptic current is input to a cohort of MN models, which are calibrated using the available decomposed HDEMG data, and predict the MN spike trains fired by the entire MN population. This novel approach is experimentally validated and applied to muscle force prediction from neuromuscular modelling, and to investigate neurophysiological properties of the human MN population during voluntary contractions.

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“…To define a fatiguing lifting task [ 2 ] with different level of risk we have designed the experimental setup as follows [ 28 ]: the F values were set at 4 lift/min (FM = 0.83), 11 lift/min (FM = 0.41) and 15 lift/min (FM = 0.28). The other parameters were set constant across all the risk conditions: L = 10 kg, H = 44 cm (HM = 0.57), V = 75 cm (VM = 0.99), D = 40 cm (DM = 0.93), A = 0° (AM = 1).…”

Section: Methodsmentioning

confidence: 99%

“…Each whole-lifting cycle was subdivided into lifting and lowering phases. The onset and termination of the lifting phase were defined as the times the IMU velocity exceeded a threshold of 0.025 m/s along the vertical axis and the maximum point of the vertical displacement of the IMU, respectively [ 15 , 28 ]. Termination of the lowering phase corresponded to the IMU velocity falling below the 0.025 m/s threshold (see Fig 1 ) [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Centre of pressure parameters for the assessment of biomechanical risk in fatiguing frequency-dependent lifting activities

et al. 2022

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Lifting tasks, among manual material handling activities, are those mainly associated with low back pain. In recent years, several instrumental-based tools were developed to quantitatively assess the biomechanical risk during lifting activities. In this study, parameters related to balance and extracted from the Centre of Pressure (CoP) data series are studied in fatiguing frequency-dependent lifting activities to: i) explore the possibility of classifying people with LBP and asymptomatic people during the execution of task; ii) examine the assessment of the risk levels associated with repetitive lifting activities, iii) enhance current understanding of postural control strategies during lifting tasks. Data were recorded from 14 asymptomatic participants and 7 participants with low back pain. The participants performed lifting tasks in three different lifting conditions (with increasing lifting frequency and risk levels) and kinetic and surface electromyography (sEMG) data were acquired. Kinetic data were used to calculated the CoP and parameters extracted from the latter show a discriminant capacity for the groups and the risk levels. Furthermore, sEMG parameters show a trend compatible with myoelectric manifestations of muscular fatigue. Correlation results between sEMG and CoP velocity parameters revealed a positive correlation between amplitude sEMG parameters and CoP velocity in both groups and a negative correlation between frequency sEMG parameters and CoP velocity. The current findings suggest that it is possible to quantitatively assess the risk level when monitoring fatiguing lifting tasks by using CoP parameters as well as identify different motor strategies between people with and without LBP.

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Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

Cited by 16 publications

References 64 publications

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

Centre of pressure parameters for the assessment of biomechanical risk in fatiguing frequency-dependent lifting activities

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