Estimating human arm's muscle force using Artificial Neural Network

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

doi:10.1109/memea.2012.6226662

Cited by 3 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These analyses effectively demonstrate that the application of multiple electrodes provides valuable information for assessing the contractile activity of muscle. Stretchable microneedle electrode arrays could be combined with artificial neural networks [133] or physiological models of muscle [131] to produce more accurate estimates of a muscle's activity than the method presented, which would prove valuable for both studies of the neuromuscular system and clinical applications. Any estimate of contractile force based on EMG measurements, however, is confounded by the dependence of the muscle's contractile force on the length [134] and the velocity [135] of the muscle.…”

Section: Force Estimation Based On Emg Activitymentioning

confidence: 99%

A Stretchable Microneedle Electrode Array for Stimulating and Measuring Intramuscular Electromyographic Activity

Guvanasen

Guo

Aguilar

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

We have developed a stretchablemicroneedle electrode array (sMEA) to stimulate andmeasure the electrical activity of muscle across multiple sites. The technology provides the signal fidelity and spatial resolution of intramuscular electrodesacross a large area of tissue. Our sMEA is composed of a polydimethylsiloxane (PDMS) substrate, conductive-PDMS traces, and stainless-steel penetrating electrodes. The traces and microneedles maintain a resistance of less than 10 [Formula: see text] when stretched up to a ~63% tensile strain, which allows for the full range of physiological motion of felinemuscle. The device and its constituent materials are cytocompatible for at least 28 days in vivo. When implanted in vivo, the device measures electromyographic (EMG) activity with clear compound motor unit action potentials. The sMEA also maintains a stable connection with moving muscle while electrically stimulating the tissue. This technology has direct application to wearable sensors, neuroprostheses, and electrophysiological studies of animals and humans.

show abstract

Section: Force Estimation Based On Emg Activitymentioning

confidence: 99%

A Stretchable Microneedle Electrode Array for Stimulating and Measuring Intramuscular Electromyographic Activity

Guvanasen

Guo

Aguilar

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…Studies of muscle force models have been carried out by author [9]. The model was estimated based on a rectified smoothed EMG signal using the BPANN method to predict the muscle force.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the signal was smoothed and normalized passing it through a 5 th order Butterworth type low-pass filter with cut off frequency 10Hz and the smooth signal is illustrate in Fig. 5(d) [8] [9]. …”

Section: A Experimental Setupmentioning

confidence: 99%

“…The network requires data for learning and testing in order to determine the weights each node uses. The training has been done by dividing the input data of 70% for training, 15% for validation and 15% for testing [9]. The network will be trained until the following condition fulfilled before it stop [10]:i-reach maximum number of epochs ii-gradient performance became less than the minimum gradient iii-validation performance increased more than the maximum fail times since the last decreased one The performance evaluation of the network is based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs as well as the characteristics of the training, validation, and testing errors.…”

Section: Artificial Neural Network (Ann) Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Jali

Izzuddin

Bohari

et al. 2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

View full text Add to dashboard Cite

This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

show abstract

Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques

Jali

Izzuddin

Bohari

et al. 2014

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Estimating human arm's muscle force using Artificial Neural Network

Cited by 3 publications

References 10 publications

A Stretchable Microneedle Electrode Array for Stimulating and Measuring Intramuscular Electromyographic Activity

A Stretchable Microneedle Electrode Array for Stimulating and Measuring Intramuscular Electromyographic Activity

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques

Contact Info

Product

Resources

About