Relevant bibliographies by topics / EMG-driven modeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'EMG-driven modeling'

Author: Grafiati
Published: 4 June 2025
Last updated: 14 July 2025

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Journal articles on the topic "EMG-driven modeling"

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Zhao, Jiamin, Yang Yu, Xu Wang, Shihan Ma, Xinjun Sheng, and Xiangyang Zhu. "A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements." Journal of Neural Engineering 19, no. 1 (2022): 016027. http://dx.doi.org/10.1088/1741-2552/ac4851.

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Abstract Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle
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Sartori, Massimo, Marco Maculan, Claudio Pizzolato, Monica Reggiani, and Dario Farina. "Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion." Journal of Neurophysiology 114, no. 4 (2015): 2509–27. http://dx.doi.org/10.1152/jn.00989.2014.

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This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during overground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as
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Meyer, Andrew J., Carolynn Patten, and Benjamin J. Fregly. "Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry." PLOS ONE 12, no. 7 (2017): e0179698. http://dx.doi.org/10.1371/journal.pone.0179698.

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Mantoan, Alice, Fabiola Spolaor, Elena Ceseracciu, Zimi Sawacha, and Monica Reggiani. "Estimation of muscle forces based on a multi-DOF EMG-driven neuromusculoskeletal modeling approach: Impact of different EMG normalization strategies." Gait & Posture 42 (September 2015): S2. http://dx.doi.org/10.1016/j.gaitpost.2015.07.016.

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Caillet, Arnault H., Andrew T. M. Phillips, Dario Farina, and Luca Modenese. "Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy." PLOS Computational Biology 19, no. 12 (2023): e1011606. http://dx.doi.org/10.1371/journal.pcbi.1011606.

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The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was de
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Kumar, Deepak, Katherine S. Rudolph, and Kurt T. Manal. "EMG-driven modeling approach to muscle force and joint load estimations: Case study in knee osteoarthritis." Journal of Orthopaedic Research 30, no. 3 (2011): 377–83. http://dx.doi.org/10.1002/jor.21544.

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Durandau, Guillaume, Dario Farina, Guillermo Asín-Prieto, et al. "Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling." Journal of NeuroEngineering and Rehabilitation 16, no. 1 (2019): 91. https://doi.org/10.1186/s12984-019-0559-z.

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<strong>Background: </strong>Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.<strong>Methods: </strong
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Mathieu, Emilie, Sylvain Crémoux, David Gasq, Philippe Pudlo, and David Amarantini. "A feasibility study of leveraging intermuscular coherence in EMG-driven neuromusculoskeletal modeling to improve muscle moment estimation." Biomedical Signal Processing and Control 109 (November 2025): 108004. https://doi.org/10.1016/j.bspc.2025.108004.

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Menegaldo, Luciano L., and Liliam F. Oliveira. "The influence of modeling hypothesis and experimental methodologies in the accuracy of muscle force estimation using EMG-driven models." Multibody System Dynamics 28, no. 1-2 (2011): 21–36. http://dx.doi.org/10.1007/s11044-011-9273-8.

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Caillet, Arnault H., Andrew T. M. Phillips, Dario Farina, and Luca Modenese. "Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling." PLOS Computational Biology 18, no. 9 (2022): e1010556. http://dx.doi.org/10.1371/journal.pcbi.1010556.

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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 decomposition of electromyographical (EMG) signals. 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 t
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Dissertations / Theses on the topic "EMG-driven modeling"

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Mantoan, Alice. "Towards the application of multi-DOF EMG-driven neuromusculoskeletal modeling in clinical practice: methodological aspects." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424140.

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New methods able to assess the individual ability of patients to generate motion and adaptation strategies are increasingly required for clinical applications aiming at recovering motor functions. Indeed, more effective rehabilitation treatments are designed to be personalized on the subject capabilities. In this context, neuromusculoskeletal (NMS) models represent a valuable tool, as they can provide important information about the unique anatomical, neurological, and functional characteristics of different subjects, through the computation of human internal variables, such as muscle activati
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Miller, Stuart Charles. "Mechanical factors affecting the estimation of tibialis anterior force using an EMG-driven modelling approach." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8763.

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The tibialis anterior (TA) muscle plays a vital role in human movement such as walking and running. Overuse of TA during these movements leads to an increased susceptibility of injuries e.g. chronic exertional compartment syndrome. TA activation has been shown to be affected by increases in exercise, age, and the external environment (i.e. incline and footwear). Because activation parameters of TA change with condition, it leads to the interpretation that force changes occur too. However,activation is only an approximate indicator of force output of a muscle. Therefore, the overall aim of this
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Morris, Alan R. "Musculoskeletal modelling and EMG driven simulation of the human lower body /." 2006. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=442410&T=F.

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Kian, Azadeh. "An EMG-driven neuromusculoskeletal modelling framework for evaluation of shoulder muscle and joint function." Thesis, 2020. https://vuir.vu.edu.au/42684/.

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Books on the topic "EMG-driven modeling"

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Morris, Alan R. Musculoskeletal modelling and EMG driven simulation of the human lower body. 2006.

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Book chapters on the topic "EMG-driven modeling"

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Bueno, Diana R., and L. Montano. "Modeling Fatigue Effect in an EMG-Driven Hill Type Muscle Model during Dynamic Contractions." In Converging Clinical and Engineering Research on Neurorehabilitation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34546-3_54.

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Thangasamy, Veeraiyah, M. Kavitha, and V. Ramkumar. "Overview of AI applications in healthcare, focusing on prosthetics and implantable devices." In The Role of Artificial Intelligence in Advanced Prosthetics and Implantable Devices. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552975-01.

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The integration of artificial intelligence (AI) in prosthetic devices has revolutionized motor control, sensory feedback, and user adaptability, enhancing the overall functionality and user experience. Multi-modal sensory feedback, facilitated by AI-driven algorithms, enables real-time responsiveness, improving proprioception, touch sensation, and movement precision. By leveraging machine learning, sensor fusion, and adaptive control strategies, AI enhances the interaction between prosthetic users and their environment, optimizing feedback mechanisms for seamless integration with human neuromuscular systems. Advanced haptic feedback, electromyography (EMG) sensors, and neural interfaces contribute to more intuitive prosthetic control, reducing cognitive load while ensuring personalized adaptation. AI-based predictive modeling and reinforcement learning further refine sensory adaptation, addressing variations in user behavior, muscle activity, and movement patterns over time. Challenges such as real-time synchronization, sensor calibration, latency reduction, and ethical considerations in AI decision-making remain critical areas for research and development. Future advancements in AI-powered neuroprosthetics, neuromorphic computing, and biofeedback systems hold the potential to further enhance prosthetic adaptation, paving the way for next-generation smart prosthetic solutions. This chapter provides an in-depth analysis of AI-driven multi-modal sensory feedback mechanisms and their role in optimizing prosthetic adaptation, addressing current challenges, emerging solutions, and future research directions in AI-assisted prosthetic technology.
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Conference papers on the topic "EMG-driven modeling"

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Tahmid, Shadman, Josep Maria Font-Llagunes, and James Yang. "Upper Extremity Joint Torque Estimation Through an EMG-Driven Model." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89952.

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Abstract Cerebrovascular accidents like a stroke can affect lower limb as well as upper extremity joints (i.e., shoulder, elbow or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate force reduces, thus affecting the joint’s torque production. Understanding how muscles generate force is a key element to injury detection. Researchers developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain
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Sartori, Massimo, David G. Lloyd, Monica Reggiani, and Enrico Pagello. "Fast operation of anatomical and stiff tendon neuromuscular models in EMG-driven modeling." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509932.

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Manal, Kurt, Bernardo Innocenti, Luc Labey, and Thomas S. Buchanan. "Condylar Contact During Normal Walking and Lateral Trunk Sway Gait: an EMG-Driven Modeling Approach to Estimate Articular Loading." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19261.

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The knee adduction moment has a characteristic double hump pattern with the first peak generally larger than the second. Recently, Mundermann showed that walking with a lateral trunk sway can reduce the 1st peak moment [1]. One might expect from this finding that there would be a decrease in medial compartment loading. This however may be too simplistic a view. Fregly et al. showed that a decrease in knee adduction moment does not guarantee a decrease in medial contact [2]. Moreover, the relationship between net joint moments and loading is not straightforward, especially when agonist/antagoni
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Shieh, Win-Bin, and Jian Sheng Lin. "Kinematic Modeling and Kinesiology Study of a Human Index Finger Based on a Tendon-Driven Articulated Manipulator With Disc-Cam Pulleys." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64204.

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A tendon-driven articulated manipulator with disc-cam pulleys is presented for the kinematic modeling and the motion analysis of a human index finger. Using the proposed model as a foundation, the driving forces of the tendons of human finger could be further evaluated and compared with the estimation from the EMG signals. The motivation of using such a tendon-driven articulated manipulator model to emulate the structure and functionality of a human finger is initiated by the similarities between these two systems, where the joint motions of the systems are both activated by the force transmis
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Guess, Trent M., Antonis Stylianou, and Mohammad Kia. "Validation of Knee Load Predictions During a Dual Limb Squat and Calfrise." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80644.

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Knowledge of knee loading would benefit prosthetic design, development of tissue engineered materials, orthopedic repair, and management of degenerative joint diseases such as osteoarthritis. Musculoskeletal modeling provides a method for estimating in vivo joint loading, but validation of model predictions is challenging. Data provided by the “Grand Challenge Competition to Predict In-Vivo Knee Loads” for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference [1] provides data from an instrumented prosthetic knee that can be used to validate load predictions. The G
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Bassett, Daniel N., Thomas S. Buchanan, and Giuliano Cerulli. "A Clinical Approach to Multi-Joint EMG-Driven Modelling." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192964.

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Most muscles span more than one joint. This can lead to problematic questions when making biomechanical models. How many joints need to be included in an accurate model? Do all joints that each muscle crosses need to be taken into account? If so, how many joints away from the joint of interest must be included, since they are all interconnected?
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