EMG-muscle force estimation model based on back-propagation neural network

Naeem, Usama J.; Chen, Xiong; Abdullah, Asaad A.

doi:10.1109/vecims.2012.6273225

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Muscles are natural amplifiers of the neural drive ( Ruff et al, 2010 ). Thus, with advanced signal analysis methods, e.g., motor unit decomposition and machine learning, muscle signals can be used as a control source in various MMIs, e.g., prostheses ( Naeem et al, 2012 ; Bergmeister et al, 2017 ), wheelchairs ( Jang et al, 2016 ), exoskeleton ( Singh et al, 2012 ; Kawase et al, 2017 ; Lyu et al, 2019 ), and human–robot collaboration ( Melcer et al, 2018 ), Compared with the brain–machine interfaces ( Grush, 2016 ), an MMI can obtain cleaner motor and intention-related signals in terms of signal-to-noise ratio (SNR) ( Grush, 2016 ).…”

Section: Muscle–machine Interfacesmentioning

confidence: 99%

Wearable super-resolution muscle–machine interfacing

et al. 2022

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Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

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Section: Muscle–machine Interfacesmentioning

confidence: 99%

Wearable super-resolution muscle–machine interfacing

et al. 2022

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“…• Support mechanisms based on the estimation of muscular strength, known as exoskeletons [25], [26].…”

Section: A Study Selectionmentioning

confidence: 99%

“…Number of channels used for each application. 1 channel[5],[24],[21],[4]; 3 channels[48]; 4 channels Exoskeletons[25], Electric powered wheelchair control[27],[28], Myoelectric bracelets[37], Handwriting recognition[44], Silent speech recognition[49]; 6 channels[42] and[43]; 8 channels Exoskeletons[24] and[60], Myoelectric bracelets[63], Handwriting recognition…”

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confidence: 99%

Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

et al. 2020

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The myoelectric interfaces are being used in rehabilitation technology, assistance and as an input device. This review focuses on an insightful analysis of the data acquisition system of EMG signals from these interfaces. According to applications reported in research articles of the last five years, the properties of the sensors, the number of channels, the pre-processing of the EMG signal, as well as the software and hardware used were identified. This analysis was performed for the following applications: monitoring of muscular activation for rehabilitation, muscle activation plans, and identification of possible pathologies, exoskeletons, electric of wheelchairs, prosthetics control, myoelectric bracelets, handwriting recognition and silent speech recognition. The results presented in this review become a guide of recommendations for the myoelectric signal processing according to the application of the interface. The main developments, degrees of research and open challenges are also presented in this direction.

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“…However, the weakness in the study is that the [53]. In addition to using the Hill muscle model [54]- [56] to predict arm joint angle or force, several other researchers used machine learning [57]- [60] for the development of upper limb exoskeleton devices. Tang developed an upper limb exoskeleton device with angle prediction using artificial neural network (ANN) and EMG features [27].…”

Section: Introductionmentioning

confidence: 99%

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Triwiyanto¹,

Caesarendra

Abdullayev

et al. 2023

Journal of Robotics and Control

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Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.

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EMG-muscle force estimation model based on back-propagation neural network

Cited by 10 publications

References 2 publications

Wearable super-resolution muscle–machine interfacing

Wearable super-resolution muscle–machine interfacing

Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

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