On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement

Radmand, Ashkan; Scheme, Erik; Englehart, Kevin

doi:10.1097/jpo.0000000000000041

Cited by 36 publications

(43 citation statements)

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“…In this context, accelerometers have been the main supplementary modality and are the most prevalent in shared surface EMG datasets, such as Khushaba et al 5, Ninapro 2, 3, 5, 7, and mmGest datasets (see Tables 1-3). Accelerometery has been shown to provide additional information to EMG, especially to reduce the effects of limb position [47,48].…”

Section: Multiple Modalitiesmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

Self Cite

188

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: Multiple Modalitiesmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

Self Cite

188

105

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“…4–7,20 These efforts rely primarily on the incorporation of data from accelerometers to augment EMG signal to identify training positions that correspond to the current limb position, and utilize grasp classifiers constructed based on position-specific training data. 4,5,8 However, this methodology has resulted in a decline in performance when using accelerometer and EMG signals compared to when using EMG alone.…”

Section: Introductionmentioning

confidence: 99%

“…4,5,8 However, this methodology has resulted in a decline in performance when using accelerometer and EMG signals compared to when using EMG alone. 20 Position-matching strategies alone do not seem adequate to improve robustness. Therefore, this study was designed to examine specific covariates, including features such as hand height, elbow angle and shoulder angle, as well as a novel 3D training paradigm to generate a more robust classifier to function in multiple positions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control

et al. 2017

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Introduction Electromyogram (EMG)-based pattern recognition control of prosthetic limbs is the current state of the art. However, these systems commonly fail when the user attempts to use the limb in a different position from which it was trained, resulting in significantly reduced functionality. Robust models for decoding EMG signals, accounting for specific changes that occur with positional variation, are needed to reduce this negative effect. Methods Ten able-bodied participants and two participants with transradial amputation were included in the study. Participants were fitted with surface EMG electrodes as well as a network of inertial measurement units (IMUs) to monitor limb position during tasks. Positional covariates including elbow angle, hand height, and forearm angle were analyzed for impact on EMG signal features to drive the generation of unique LDA classifier algorithms. Offline analysis of classification error for each control scheme was then completed. Results Elbow angle demonstrated the strongest impact on the EMG signal. Hand height also demonstrated a consistent increase in EMG signal with increasing height. Incorporating these specific covariates into classifier algorithms improved performance compared with classifiers trained in the conventional fashion (single-position EMG). However, able-bodied participants demonstrated lowest classification error when data from random-training positions were incorporated (10.3% vs. 17.2% single position, P < 0.001). These results were even more dramatic in participants with amputation (with five training repetitions: 7.14% vs. 32.08%, P < 0.001). Performance differences between single-position and random-position training for individuals with amputations were significantly larger when the user was wearing his/her prosthesis than otherwise. Conclusions Incorporating position-specific covariates into myoelectric classification algorithms can dramatically improve robustness and classification accuracy when using the prosthesis in the user's entire workspace. In single-position training paradigms, classification error rates were 39.22% and 32.18%, respectively, for two participants with amputation and resulted in unusable classifiers. Conversely, classification errors were at 10% for able-bodied and near 7% for participants with amputation when at least five training repetitions were used to train either a random position or position-specific classifier. As position-tracking hardware becomes smaller and can be implemented into socket designs, incorporating this information into classifier algorithms can dramatically reduce the limb-position effect. Current users can experience reduction of the limb-position effect through training in multiple random positions.

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“…27 Applications in prosthetic control were not explicitly explored. Practical discussions and considerations for fusing accelerometer and EMG data were presented by Radmand et al 30 The clinically impractical needs of training classifiers in a number of possible arm positions is discussed, referred to as ''Dynamic training'', and the otherwise very lengthy time requirement for training was able to be minimized somewhat.…”

Section: Introductionmentioning

confidence: 99%

“…26,27 Others have proposed force sensors placed in contact the skin used to detect the changes in force and pressure within the socket due to volumetric changes of the forearm. This technique also known as force myography (FMG), 28 residual kinetic imaging (RKI), 29 and muscle pressure mapping (MPM) 30 shows promise in providing an alternate and additional input for signal classification. Craelius et al referred to this method as residual kinetic imaging in 1999.…”

Section: Introductionmentioning

confidence: 99%

Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

Sanford

Patterson

Popa

2017

Journal of Rehabilitation and Assistive Technologies Engineerin

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Objective: Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods: Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography-force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue. Results: Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems. Significance: Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.

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On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement

Cited by 36 publications

References 1 publication

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Multi-Position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control

Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

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