EMG-driven control in lower limb prostheses: a topic-based systematic review

Cimolato, Andrea; Driessen, Josephus J. M.; Mattos, Leonardo S.; Momi, Elena De; Laffranchi, Matteo; Michieli, Lorenzo De

doi:10.1186/s12984-022-01019-1

Cited by 28 publications

(12 citation statements)

References 134 publications

(147 reference statements)

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“…While for the dry skin no gel is required to interface it with the electrode, up to now, many researchers have investigated EMG sensors and applied it to control robotic interface, these investigations can be divided into three categories: controlling prosthetic arms, remotely operated robots mainly used in medical surgeries, and the application of orthoses. (Bitzer & Van Der Smagt, 2006) controlled a four-fingered robot hand using EMG sensor inputs from 10 forearm muscles, according to (Cimolato et al, 2022) the results indicates that there is a lack of quantitative and standardized measurements among the researchers that work on EMG based bionic limbs which hinders the possibility to analyze and compare the performances of different EMG-driven controllers.…”

Section: Emg Sensor and Related Workmentioning

confidence: 99%

Employing EMG sensors in Bionic limbs based on a New Binary Trick Method

Mohammed

Salih

Haji

2023

SJUOZ

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Human muscles can be read by using electromyography (EMG) sensors, which are electrical signals generated by the muscles of human and animal bodies. This means it is possible to use electricity generated by muscles to control actuators/servo motors for any specific tasks. This could support a wide range of applications, especially for people with disabilities. One such application would be making bionic limbs based on servo motors. According to a study held by the K4D helpdesk report based on estimations that 15.3% of the world’s population has a moderate or severe disability, this proportion is likely to increase to 18-20% in conflict- affected areas (Thompson, 2017). The goal of this study is to make bionic limbs affordable by minimizing the cost while maintaining accuracy at an acceptable rate. To achieve this goal, the study proposes a new idea for using electromyography (EMG) sensors in bionic limbs, which suggests a decrease in the number of EMG sensors to decrease the cost and power consumption. Decreasing the number of EMG sensors will result in a loss of accuracy in controlling actuators (servo motors) because usually, each sensor is responsible for activating one servo motor. In normal projects, one will need at least six EMG sensors to control six servo motors. The study will use only three EMG sensors to control/activate six servo motors depending on the binary trick idea suggested by this study, which is manipulating all three input signals from EMG sensors at once and then deciding which servo motor to activate by using a supervised machine learning technique such as K-nearest neighbors (kNN).

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Section: Emg Sensor and Related Workmentioning

confidence: 99%

Employing EMG sensors in Bionic limbs based on a New Binary Trick Method

Mohammed

Salih

Haji

2023

SJUOZ

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“…With these methods, the recorded EMG signals are processed to provide a unique neural control to a single Hill-type actuator that phenomenologically describes whole muscle dynamics. This approach is wellestablished and implemented in automated tools (Pizzolato et al, 2015) and has had important applications, for example in human-machine interfacing (Sartori et al, 2012;Ao et al, 2017;Caggiano et al, 2022;Cimolato et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy

Caillet

Phillips

Farina

et al. 2023

Preprint

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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 described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The control of motoneurons provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decode the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.

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“…This technique is classified as either non-invasive, including surface electromyography (sEMG), electroencephalography (EEG), forcemyography (FMG), mechanomyography (MMG), magnetoencephalography (MEG), force sensitive resistance (FSR), and magnetomicrometry (MM), with the last one being presently developed in MIT [ 15 ], or invasive, including implanted electromyography (iEMG), myoelectric implantable recording arrays (MIRAs), electroneurography (ENG), electrocorticography (ECoG), brain–chip interfaces (BCHIs), and magnetomicrometry (MM) [ 16 ]. Among all of these techniques, sEMG is the most commonly used method for prosthesis control, which has been studied very extensively [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

Nazari

Zheng

2023

Sensors

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This paper presents a critical review and comparison of the results of recently published studies in the fields of human–machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords “Human Machine Interface”, “Sonomyography”, “Ultrasound”, “Upper Limb Prosthesis”, “Artificial Intelligence”, and “Non-Invasive Sensors” was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human–machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

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EMG-driven control in lower limb prostheses: a topic-based systematic review

Cited by 28 publications

References 134 publications

Employing EMG sensors in Bionic limbs based on a New Binary Trick Method

Employing EMG sensors in Bionic limbs based on a New Binary Trick Method

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

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