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Spatial Registration of Hand Muscle Electromyography Signals


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Currently, surface electromyography (sEMG) prostheses are characterized by low control capabilities and long training times. This is in contrast with recent advances in mechatronics, thanks to which mechanical hands have often many degrees-of-freedom and force control. Therefore, there is a need of techniques able to increase control capabilities with sEMG signals. Several reasons determine differences in the signal patterns, and make the classification of sEMG signals a challenging task. One of the reasons is the positioning of the electrodes on the subjects. In this paper we evaluate the positioning effect on the Ninapro database using automatic classification of the data for its evaluation.

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Spatial Registration of Hand Muscle Electromyography Signals

  1. 1. Spatial Registration of Hand Muscle Electromyography Signals Manfredo Atzori1, Claudio Castellini2 and Henning Müller1 1 Department of Business Information Systems at the HES- SO Valais, Sierre, Switzerland 2 Institute of Robotics and Mechatronics, German Aerospace Research Center, Wessling, Germany Summary 1. sEMG hand prostheses have low control capabilities compared to recent advances in mechatronics 2. the Ninapro database ( has been realized to test sEMG prosthetics control algorithms 3. inter-subject differences in the positioning of the electrodes can determine substantial signal differences 4. increasing inter-subject similarity with spatial registration improves the classification results with statistical significance Introduction Results 1.  Hand robotic prosthetics 1.  Armband rotation simulation •  usually controlled via surface electromyography (sEMG) Subject 1 •  t h e a v e r a g e s i m i l a r i t y 2.5 •  low control capabilities electrode 1 electrode 2 2 electrode 3 sEMG Activity (V) electrode 4 1.5 electrode 5 increase over all of the electrode 6 •  long training times 1 electrode 7 electrode 8 0.5 2.  Ninapro sEMG acquisition setup subjects due to rotation 0 0 50 100 150 time (seconds/100) Subject 2 200 250 300 simulation is 8.12%, with 5 •  8 electrodes equally spaced around the forearm electrode 1 electrode 2 4 electrode 3 sEMG Activity (V) electrode 4 3 electrode 5 •  2 electrodes placed on fingers extensor and flexor muscles standard deviation 7.02 % electrode 6 2 electrode 7 electrode 8 1 3.  Inter-subject differences in the positioning of the •  t h e m a x i m a l r o t a t i o n 0 0 50 100 150 time (seconds/100) Subject 3 200 250 300 simulation increase in the 2.5 electrodes electrode 1 electrode 2 2 electrode 3 sEMG Activity (V) electrode 4 1.5 electrode 5 similarity of signals across electrode 6 •  have strong impact on the signal and on the classification task 1 electrode 7 electrode 8 0.5 •  are analyzed in few papers subjects is 33% 0 0 50 100 150 time (seconds/100) 200 250 300 ! •  can be diminished with spatial registration algorithms Figure 1: Inter-individual ! Figure 2: Maximum inter- variability of sEMG signal subject signal similarity patterns. Example of sEMG improvement due to rotation 2.  Classification signal patterns from three simulation in percentage (top). Methods subjects doing the same Correspondent rotations movement. (bottom). 8 equally spaced electrodes 1.  Datasets • 4 movements: •  4 datasets of 27 subjects, 10 repetitions of 4 & 12 movements, classification improved of 4.69 acquired with 8 & 10 electrodes. percentage points, with an increase of 8.09% (p=0.01) 2.  Armband rotation simulation • 12 movements: •  linear interpolation of signals from subsequent electrodes at classification improved of 1.21 steps of 1/10 of the distance between each of the electrodes percentage points, with an •  evaluation of the rotated sEMG signal similarity through the increase of 2.09% (p=0.04) mean value of the Euclidean distance along the timeline •  identification of the signals that minimize the distance 10 electrodes: between the inter-subject sEMG signals • 4 movements: classification improved by 2.87 3.  Classification 4 12 4 12 ! percent, with an increase of Figure 4. Mean and standard deviations of the LS-SVM •  preprocessing: synchronization; low-pass filtering at 1Hz 4.74% (p=0.01) classification errors on the signal of the 8 electrodes equally (zero-phase second order Butterworth); normalization (0 mean spaced on the elastic armband (left) and from all the electrodes and unitary standard deviation); averaging. • 12 movements: (right). •  classifier: multi class Least-Squares support vector machines classification improved of 1.94 (LS-SVM) with RBF kernel percent, with an increase of •  training: ten repetitions of each movement 3.2% (p=0.02) •  test: on all the other movements, subjects and repetitions Conclusions •  we performed spatial registration simulating the rotation of equally spaced electrodes •  spatial registration of the sEMG signal of finger movements augments inter-subject LS-SVM classification with statistical significance •  the difference between the average improvement of the similarity of the signals (8.12 ± 7.02)% and the average improvement in the classification (2.99 ± 1.43)% encourages to do further analyses about spatial registration and the features used for the classification Contact and more information: manfredo.atzori@hevs.chPlease, cite:Manfredo Atzori, Claudio Castellini and Henning Müller, Spatial Registration of Hand Muscle Electromyography Signals, in: 7th International Workshop on Biosignal Interpretation,Como, Italy, 2012Full paper link: