Simultaneous and proportional control of 2D wrist movements with myoelectric signals

Hahne, Janne M.; Rehbaum, Hubertus; Bießmann, Felix; Meinecke, Frank C.; Müller, K-R; Jiang, Ning; Farina, Dario; Parra, Lucas C.

doi:10.1109/mlsp.2012.6349712

Cited by 36 publications

(30 citation statements)

References 11 publications

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“…1) Calibration Runs: In our previous offline-study [11] we found that even when trained with single DoF movements only, combined movements of the two DoFs investigated here can be estimated with relatively high accuracy by a linear regressor. For people with limb deficiency who are potential users of the proposed method it is very difficult to perform accurate combined movements without the intrinsic feedback of the limb.…”

Section: Experimental Paradigmmentioning

confidence: 98%

“…We have shown in an offline-study that the log-variance of the band-pass filtered EMG is approximately linearly related to the joint angle and thus allows for using computationally efficient linear regression techniques [11]. Therefore this feature was extracted for each channel, resulting in a 16-dimensional feature-vector .…”

Section: A Signal Processing Chainmentioning

confidence: 99%

“…This allows for a smooth, natural and intuitive control which comes closer to the locomotion of an intact limb. A comparison of different regression techniques for myoelectric control is presented in [11]. With the appropriate selection of EMG features, simple linear models perform as well as more complex and computationally expensive methods such as artificial neuronal networks or kernel ridge regression.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

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Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.

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Section: Experimental Paradigmmentioning

confidence: 98%

Section: A Signal Processing Chainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Hahne

Dähne

Hwang

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

121

View full text Add to dashboard Cite

show abstract

“…Consequently, the regression approach provides greater flexibility for the user, leading towards a more intuitive prosthesis control paradigm. Examples for this control concept found in the literature are based on NonNegative Matrix Factorization [54,55,56 • ], Artificial Neural Networks [57][58][59][60][61][62], or Linear Regression [63], allowing two or more degrees of freedom to be activated simultaneously and proportionally. Notably, some of the limitations of pattern recognition systems have been addressed using regression approaches, such as electrode shift [64].…”

Section: Regression Controlmentioning

confidence: 99%

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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Control algorithms for upper limb myoelectric prostheses have been in development since the mid-1940s. Despite advances in computing power and in the performance of these algorithms, clinically available prostheses are still based on the earliest control strategies. The aim of this review paper is to detail the development, advantages and disadvantages of prosthetic control systems and to highlight areas that are current barriers for the transition from laboratory to clinical practice. Current surgical strategies and future research directions to achieve multifunctional control will also be discussed. The findings from this review suggest that regression algorithms may offer an alternative feed-forward approach to direct and pattern recognition control, while virtual rehabilitation environments and tactile feedback could improve the overall prosthetic control.

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“…However, even if classification based approaches allow a direct access of all functions, complex movements still need to be split into subtasks which have to be executed in a sequential manner. Recently, regression techniques have been introduced to simultaneously control a prosthetic device with two or three DoFs [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Channel selection for simultaneous myoelectric prosthesis control

Hwang

Hahne

Müller

2014

2014 International Winter Workshop on Brain-Computer Interface (BCI)

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To develop a clinic ally available prosthesis base d on electromyography (EM G) signals, t he n umber of r ecording electrodes should be as small a s p ossible. In t his stud y, we investigate the po ssibility of th e least absolute s hrinkage and selection operator (LASSO) for finding electrode subsets suitable for regression based myoelectric prosthesis control. EMG signals were re corded using 192 electrodes while ten s ubjects were performing tw o degree-of-freedom (DoF ) wrist movem ents. Among the w hole c hannels, we s elected subsets consisting of 96, 64, 48, 32, 24, 16, 12, and 8 ele ctrodes, respectively, u sing the LASSO metho d. As a baseline method, electrode subset s ha ving the same numbers of electrodes w ere a rbitrary s elected w ith regular spacing (uniform selection method). The performance of decoding the movements was estimated using the r-square value. The e lectrode su bsets s elected b y the LAS SO met hod g enerally outperformed those chosen by the arbitrary selection method. In particular, th e performance of th e LA SSO method was significantly higher th an that of the a rbitrary selection me thod when using the subsets of 8 e lectrodes. From the analysis results, we could confir m that the LASSO method can b e us ed to s elect reasonable el ectrode subsets for reg ression b ased m yoelectric prosthesis control.Keywords-electromyography ( EMG); my oelectric control; prosthetic hand; regression; least absolute shrinkage and selection operator (LASSO)

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Simultaneous and proportional control of 2D wrist movements with myoelectric signals

Cited by 36 publications

References 11 publications

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Channel selection for simultaneous myoelectric prosthesis control

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