Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro; Orlando, M. Felix; Behera, Laxmidhar; Saxena, Anupam; Dutta, Ashish

doi:10.1109/embc.2013.6609506

Cited by 47 publications

(29 citation statements)

References 6 publications

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“…Nevertheless, over the last decade several groups have also addressed the challenge of using surface EMG signals to reconstruct kinematic variables (e.g. position or velocity) of independent finger movement, both offline [14][15][16][17][18] and in real-time [19][20][21] . As compared to non-invasive methods, intramuscular recordings offer the advantage of lower level of muscle cross-talk 22 , hence making it possible to create multiple one-to-one mappings between specific muscles and prosthesis degrees of actuation (DOAs).…”

Section: Introductionmentioning

confidence: 99%

Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

Krasoulis

Vijayakumar

Nazarpour

2019

Preprint

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Machine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift towards continuous prosthesis control, yet little is known about whether and how motor learning affects myoelectric control performance in dexterous, intuitive tasks. We investigate motor learning aspects of independent finger position control by conducting real-time experiments with 10 able-bodied and two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, learning still occurs and leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments. 2/17 3/17 12/17 14/17 15/17 17/17

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Section: Introductionmentioning

confidence: 99%

Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

Krasoulis

Vijayakumar

Nazarpour

2019

Preprint

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“…To evaluate the performance of the amplitude independent muscle activity detection method against amplitude dependent detection methods, a comparison has been conducted between the performance of the amplitude independent FLA-MSE algorithm and three amplitude dependent detection methods. The first method is the classical detection method which compares the rectified and filtered sEMG signal with a predefined threshold, where this method was used in most of the practical implemented hand robotic devices [16][17][18][19][20][21][22][23][24][25]. The second method is the Teager Kaiser Energy Operator (TKE) [7][8][9][10]26] and the third method is the Integrated Profile (IP) [13,27] of the sEMG signal.…”

Section: Performance Comparison Between the Fla-mse Algorithm And Thrmentioning

confidence: 99%

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Hameed

Hasan

Shafie

et al. 2020

IJEECS

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The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level. Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people. To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.

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“…For example, such EMG-based robot control devices as hand prostheses, upperand lower-body prostheses, hand exoskeleton, upper-and lower-body exoskeleton robots have been investigated [1], [2], [3], [4], [5], [6], [7]. For these applications, using multiple sensor channels is a promising approach to estimate the intentions of user limb movements [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Estimating joint movements from observed EMG signals with multiple electrodes under sensor failure situations toward safe assistive robot control

Furukawa¹,

Noda²,

Teramae³

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we propose an estimation method of human joint movements from measured EMG signals for assistive robot control. We focus on how to estimate joint movements using multiple EMG electrodes even under sensor failure situations. In real world applications, EMG sensor electrodes might become disconnected or detached from skin surfaces. If we consider EMG-based robot control for assistive robots, such sensor failures lead to significant errors in the estimation of user joint movements. To cope with these sensor failures, we propose a state estimation model that takes uncertain observations into account. Sensor channel anomalies are found by checking the covariance of the EMG signals measured by multiple EMG electrodes. To validate the proposed control framework, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow joint movement estimation. We show proper control of a one-DOF exoskeleton robot based on the estimated joint torque using our proposed method even when one EMG electrode has a sensor problem; a standard method with no tolerability against uncertain observations was unable to deal with these fault situations. Furthermore, the errors of the estimated joint torque with our proposed method were smaller than the standard method or a method with a conventional sensor fault detection algorithm.

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Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals

Cited by 47 publications

References 6 publications

Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Estimating joint movements from observed EMG signals with multiple electrodes under sensor failure situations toward safe assistive robot control

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