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(A) Typical full-wave-rectified EMG of a single step of cat L at 1.2 m s 1 ; (B) the corresponding calculated and interpolated IEMG; and (C) the corresponding force-time history. Interpolated IEMG in B and measured force in C were normalized relative to their respective times and expressed by 100 data points each.
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The relationship between force and electromyographic (EMG) signals of the cat soleus muscle was obtained for three animals during locomotion at five different speeds (154 steps), using implanted EMG electrodes and a force transducer. Experimentally obtained force-IEMG (= integrated EMG) relationships were compared with theoretically predicted insta...
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Context 1
... the force signals The full-wave-rectified EMG signals (Fig. 4A), and the corresponding IEMG-time (Fig. 4B) and force-time (Fig. 4C) plots, are illustrations of a typical step at a speed of 1.2 m s 1 (cat L). The rectified EMG shows a first peak between approximately 20 and 50 % and a second peak between approximately 60 and 90 % of the normalized stance time (Fig. 4A). Both EMG peaks were reflected ...
Context 2
... the force signals The full-wave-rectified EMG signals (Fig. 4A), and the corresponding IEMG-time (Fig. 4B) and force-time (Fig. 4C) plots, are illustrations of a typical step at a speed of 1.2 m s 1 (cat L). The rectified EMG shows a first peak between approximately 20 and 50 % and a second peak between approximately 60 and 90 % of the normalized stance time (Fig. 4A). Both EMG peaks were reflected in the IEMG-time plot (Fig. 4B). The ...
Context 3
... the force signals The full-wave-rectified EMG signals (Fig. 4A), and the corresponding IEMG-time (Fig. 4B) and force-time (Fig. 4C) plots, are illustrations of a typical step at a speed of 1.2 m s 1 (cat L). The rectified EMG shows a first peak between approximately 20 and 50 % and a second peak between approximately 60 and 90 % of the normalized stance time (Fig. 4A). Both EMG peaks were reflected in the IEMG-time plot (Fig. 4B). The first peak was usually higher ...
Context 4
... The full-wave-rectified EMG signals (Fig. 4A), and the corresponding IEMG-time (Fig. 4B) and force-time (Fig. 4C) plots, are illustrations of a typical step at a speed of 1.2 m s 1 (cat L). The rectified EMG shows a first peak between approximately 20 and 50 % and a second peak between approximately 60 and 90 % of the normalized stance time (Fig. 4A). Both EMG peaks were reflected in the IEMG-time plot (Fig. 4B). The first peak was usually higher than the second ...
Context 5
... IEMG-time (Fig. 4B) and force-time (Fig. 4C) plots, are illustrations of a typical step at a speed of 1.2 m s 1 (cat L). The rectified EMG shows a first peak between approximately 20 and 50 % and a second peak between approximately 60 and 90 % of the normalized stance time (Fig. 4A). Both EMG peaks were reflected in the IEMG-time plot (Fig. 4B). The first peak was usually higher than the second ...
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... In particular, the biceps femoris long head was not active at BDC, as seen in the literature, and there was delayed active force timing of the biceps femoris short head 7,[11][12][13][14][15][16] . Delayed timing may be explained by electromechanical delay between EMG data and force production 67 . It is possible that excess vasti passive forces, which comprised a significant portion of their total force, could have affected timing and magnitudes of the active hamstring forces (Supplementary Fig. 9). ...
Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40–216 W) and cadences (75–99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling_sim for others to reproduce these results and build upon this research.
... The length of muscle activity was therefore in the same time domain as in EMGs previously recorded in other snakes [33][34][35][36] and vertebrates. [39][40][41][42][43] EMG activity in the rattle muscles showed distinct left-right alternating activation patterns, as described in previous reports. 44,45 In contrast to locomotor EMG bouts, rattle activity was characterized by very short activations ( Figure 1B) with half-widths of individual envelopes of 5.4 ± 0.02 ms ( Figure 1C, red, n = 1,016 events). ...
... The relationship between the EMG signal amplitude and the force magnitude of limb movements varies from a linear to non-linear model [25][26][27]. Basically, this relationship depends on various mechanical and physiological factors such as movement speed [25], muscle activation levels [28], muscle resting and contraction lengths [29] and muscle composition [27]. Importantly, based on these factors, the EMG amplitudes vary across different hand gestures and EMG electrodes and provide the theoretical foundation for discriminative power of the proposed features. ...
The performance of a robotic exoskeleton depends upon the accuracy of control commands from the controller fed with Surface ElectroMyoGraphy (sEMG) input signals. The classification of hand gestures based on sEMG signals extracted from a human hand depends upon the type of EMG features extracted. In this paper, an ensemble of energy features is proposed for the sEMG classification. The idea is motivated by the energy features’ relation to the movement force, dependence on related mechanical factors, robustness with respect to the repetition of trials and the presence of noise. The suitability of the proposed energy features is tested by using the standard machine learning classifiers, including the K-Nearest Neighbour (KNN), Probabilistic Neural Networks, Ensemble KNN, Quadratic Discriminant Analysis and the Cubic Support Vector Machines. In order to show the superiority of the proposed energy features, the experiments are conducted over benchmark NinaPro DB1 sEMG hand gesture dataset. The fine KNN classifier has achieved the highest validation accuracy of 88.8%, an improvement of 13% over the state of the art accuracy. The performance of the classifiers is analyzed with various evaluation metrics using the proposed feature ensemble. The contribution of individual features for the performance is also analyzed and observed that spectral band energy features have provided an highest accuracy of 85.2%. Additionally, the proposed method is found to be computationally least expensive.
... Örneğin, Guimaraes ve ark. EMG sinyalleri ve geometrik bir model kullanarak bir kedinin kas gücünü tahmin etmiştir [6]. EMG sinyallerinden yararlanan bir diğer çalışmada da Vilimek [7], EMG sinyalleri ve matematiksel eşitlikleri kullanarak kas ve tendon kuvvetlerinin tahmini için farklı modeller önermişlerdir. ...
Kas kuvvetlerinin belirlenmesi sportif faaliyetler, egzersiz etkinliği ve rehabilitasyon süreçleri bakımından önemlidir. Kas kuvvetleri genellikle biyomekanik modeller aracılığıyla hesaplanmakta veya elektromiyografi (EMG) ölçümleri ile tahmin edilmektedir. Bu çalışmada kas kuvvetlerinin tahmini için farklı yapay zeka yöntemleri incelenmiştir. Yapay zeka algoritmalarının eğitimi için eklem açısı, eklem açısal hızı ve kas kuvveti kullanılmıştır. Algoritmaların eğitim ve test işlemi aynı veri üzerinde yapıldığında hem karar ağaçları hem de yapay sinir ağları yüksek başarı göstermiştir. Ancak algoritmalar farklı bir hareket verisindeki kas kuvvetlerini tahmin etmek için sınandıklarında en yüksek başarının sadece yapay sinir ağları yönteminde elde edilmiştir. Bu çalışmanın sonuçları, yapay zeka yöntemlerinin kas kuvvetlerini EMG verileri kullanılmadan da tahmin edebileceğini göstermiştir.
... A number of studies have previously reported the relationship between the surface EMG and muscle force (25,26). Some authors have concluded that the magnitude of the EMG signal is directly proportional to muscle strength for isometric and/or isotonic contractions with constant speed for various muscles. ...
Objectives: To describe and critique a systematic multidisciplinary approach to user engagement, and selection and evaluation of sensor technologies for development of a sensor-based Digital Toolkit for assessment of movement in children with cerebral palsy (CP).
Methods: A sequential process was employed comprising three steps: Step 1: define user requirements, by identifying domains of interest; Step 2: map domains of interest to potential sensor technologies; and Step 3: evaluate and select appropriate sensors to be incorporated into the Digital Toolkit. The process employed a combination of principles from frameworks based in either healthcare or technology design.
Results: A broad range of domains were ranked as important by clinicians, patients and families, and industry users. These directly informed the device selection and evaluation process that resulted in three sensor-based technologies being agreed for inclusion in the Digital Toolkit, for use in a future research study.
Conclusion: This report demonstrates a systematic approach to user engagement and device selection and evaluation during the development of a sensor-based solution to a healthcare problem. It also provides a narrative on the benefits of employing a multidisciplinary approach throughout the process. This work uses previous frameworks for evaluating sensor technologies and expands on the methods used for user engagement.
... Additionally, Ruijven and Weijus 1990, Guimaraes et al. 1995 andHerzog et al. 1998 showed a poor prediction alone from the rectified EMG signal and had a shorter duration than the resulting force. However, their study has suggested the addition of muscle twitch to the activation function can give a better prediction of the muscle forces [13,14,41]. ...
... Additionally, Ruijven and Weijus 1990, Guimaraes et al. 1995 andHerzog et al. 1998 showed a poor prediction alone from the rectified EMG signal and had a shorter duration than the resulting force. However, their study has suggested the addition of muscle twitch to the activation function can give a better prediction of the muscle forces [13,14,41]. Miler brown et al. 1973 and Rabiner and Gold 1975 have suggested that a critically damped linear second-order discrete linear model can be used to represent muscle twitches and which can be expressed in the discrete form by using backward difference [24,30]. ...
Surface Electromyography (S-EMG) has shown the advantages of robotic rehabilitation. Robotic rehabilitation can be significantly improved if the intended body movement of the patients can be well identified. In this chapter, we first use the SVM classifier to identify the intended motion patterns, which are plantarflexion and dorsiflexion, by using three wireless EMG sensors placed at the tibialis anterior, gastrocnemius lateralis and gastrocnemius medialis muscles. To estimate the ankle joint torque as well as the joint angle for both plantarflexion and dorsiflexion, this chapter also develops nonlinear mathematical models for joint torque estimation and utilises Swarm Techniques to identify model parameters for each movement pattern of the ankle. During rehabilitation, once the intended motion is recognised, the activation functions extracted from an individual associated EMG channel can be used to estimate both the torque and angle by using the established nonlinear models. Experimental results demonstrated the effectiveness of the proposed approach.
... When maximum effort is required, the central nervous system is probably constrained in terms of variability in muscle coordination, which leads to similar force production when muscle lengths are changed. In addition, muscle forces are a product of force-length-velocity relationship and activation state, which suggests that activation can be tuned by the central nervous system to sustain a given force when the muscle is beyond its optimum length (Guimaraes, Herzog, Allinger, & Zhang, 1995;Guimaraes, Herzog, Hulliger, Zhang, & Day, 1994). ...
Sprint cycling performance depends upon the balance between muscle and drag forces. This study assessed the influence of upper body position on muscle forces and aerodynamics during seated sprint cycling. Thirteen competitive cyclists attended two sessions. The first session was used to determine handlebar positions to achieve predetermined hip flexion angles (70-110° in 10° increments) using dynamic bicycle fitting. In the second session, full body kinematics and pedal forces were recorded throughout 2x6-s seated sprints at the predetermined handlebar positions, and frontal plane images were used to determine the projected frontal area. Leg work, joint work, muscle forces and frontal area were compared at three upper body positions, being optimum (maximum leg work), optimal+10° and optimal-10° of hip flexion. Larger hip (p = 0.01-0.02) and reduced knee (p = 0.02-0.03) contribution to leg work were observed at the optimal+10° position without changes at the ankle joint (p = 0.39). No differences were observed in peak muscle forces across the three body positions (p = 0.06-0.48). Frontal area was reduced at optimum+10° of hip flexion when compared to optimum (p = 0.02) and optimum-10° (p < 0.01). These findings suggest that large changes in upper body position can influence aerodynamics and alter contributions from the knee and hip joints, without influencing peak muscle forces.
... Finally, it must be noted that, with few exceptions (e.g. [20,27]), the "muscle force" reported in the literature is measured at the end-effector, rather than at the muscle directly, as is calculated in this simulation. Muscle and endeffector force need not be linearly related, as the co-activation of synergist [26] or antagonist muscles [3] and the lever action of the tendon attachment to the bone [13,51] will alter the end-effector force, thus affecting the measured EMG-force relation. ...
... Finally, it must be noted that, with few exceptions (e.g. [20,27]), the "muscle force" reported in the literature is measured at the end-effector, rather than at the muscle directly, as is calculated in this simulation. Muscle and endeffector force need not be linearly related, as the co-activation of synergist [26] or antagonist muscles [3] and the lever action of the tendon attachment to the bone [13,51] will alter the end-effector force, thus affecting the measured EMG-force relation. ...
The Fuglevand model is often used to address challenging questions in neurophysiology; however, there are elements of the neuromuscular system unaccounted for in the model. For instance, in some muscles, slow and fast motor units (MUs) tend to reside deep and superficially in the muscle, respectively, necessarily altering the development of surface electromyogram (EMG) power during activation. Thus, the objective of this study was to replace the randomized MU territory (MUT) placement algorithm in the Fuglevand model with an optimized method capable of reflecting these observations. To accomplish this, a weighting term was added to a previously developed optimization algorithm to encourage regionalized MUT placement. The weighting term consequently produced significantly different muscle fibre type content in the deep and superficial portions of the muscle. The relation between simulated EMG and muscle force was found to be significantly affected by regionalization. These changes were specifically a function of EMG power, as force was unaffected by regionalization. These findings suggest that parameterizing MUT regionalization will allow the model to produce a larger variety of EMG-force relations, as is observed physiologically, and could potentially simulate the loss of specific MU types as observed in ageing and clinical populations.
... EMG recorded during voluntary muscle contracting exercises has been extensively studied in order to determine its relationship with muscle force [12][13][14][15] and muscle fatigue [16][17][18][19]. The notion that the magnitude and intensity of the EMG signal is at least qualitatively related to the force produced by a muscle under given condition is well accepted in the scientific community [15]. ...
In this study, a new way to predict the muscle fatigue and force from Electromyography (EMG) signal for repeated isokinetic exercise is demonstrated. The relationship between cumulative biceps fatigue and EMG signal during repetitive dumbbell curl tasks with constant velocity was investigated with respect to Maximum voluntary contraction (MVC) levels (20 %, 35 %, 50 % and 75 % MVC). The mean integrated EMG and mean frequency per cycle were obtained from the time domain and frequency domain, respectively. The mean IEMG value and mean frequency values were co-plotted in the global EMG index map. Finally, we developed a new algorithm to predict muscle fatigue and force based on a global EMG index map employing mean IEMG and MNF values. The proposed algorithm based on a global EMG index map can be used to simultaneously predict muscle fatigue and force from real-time EMG signals with arbitrary MVC levels.
