Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array

Boschmann, Alexander; Platzner, Marco

doi:10.1109/embc.2012.6346923

Cited by 23 publications

(20 citation statements)

References 16 publications

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“…The multi-channel sEMG recording provides useful information to improve the recognition of fine movements. On the other hand it requires a complex and cumbersome setup with the need to place many electrodes on the forearm and to manage many connectors [10] [29] . In order to simplify the electrode setup Farina et al used a textile sleeve with four grids of 5×5 electrodes to detect sEMG signals from upper arm and forearm for the recognition of wrist and forearm movements [30] .…”

Section: Discussionmentioning

confidence: 99%

“…Geng et al [41] showed that the impact of arm position on sEMG pattern-recognition performances is a little stronger in intact arm than in amputated arm. Boschmann and Platzner [29] showed that, to compensate an electrode displacement of 1 cm on the classification accuracy, the number of detection points must be increased from 8 to 32. Hargrove et al [42] studied the effect of 1 cm electrode displacement in longitudinal and/or transversal direction on pattern classification accuracy using an IED of 3 cm, and proposed a classifier training strategy to accommodate performance degradation.…”

Section: Discussionmentioning

confidence: 99%

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Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

et al. 2014

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The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

et al. 2014

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“…To ameliorate the effects of inter-session variability, data augmentation schemes [12,13] and model adaptation techniques [2,14,15,16] were developed. However, the problem of recognizing a large set of gestures in a new recording session is still far from being solved, both in terms of the recognition accuracy and the complexity of the calibration process.…”

Section: Introductionmentioning

confidence: 99%

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

Jin

Wei

et al. 2017

Sensors

275

224

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High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.

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“…In order to simulate the effect of electrode shift in the experiments performed, multiple electrodes are placed in adjacent areas of the original locations of the electrodes (Hargrove et al, 2008 ; Boschmann and Platzner, 2012 ). The most common experimental procedure consists of training the classifiers on features extracted from the original signals and testing the classifier on features from the shifted signals.…”

Section: Causes Of Emg Variability With Timementioning

confidence: 99%

“…The most common experimental procedure consists of training the classifiers on features extracted from the original signals and testing the classifier on features from the shifted signals. The maximum electrode displacement distance that is likely to occur in the normal everyday use of the prosthetic hand is noted in different studies as 1 cm (Hargrove et al, 2008 ; Boschmann and Platzner, 2012 , 2014 ; Muceli et al, 2014 ; Pan et al, 2015 ; Stango et al, 2015 ).…”

Section: Causes Of Emg Variability With Timementioning

confidence: 99%

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Kyranou¹,

Vijayakumar

Erden

2018

Front. Neurorobot.

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Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

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Reducing classification accuracy degradation of pattern recognition based myoelectric control caused by electrode shift using a high density electrode array

Cited by 23 publications

References 16 publications

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

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