spatial registration of hand muscle electromyography signals · spatial registration of hand muscle...
TRANSCRIPT
Results 1. Armband rotation simulation
2. Classification
Spatial Registration of Hand Muscle Electromyography Signals
Contact and more information: [email protected]
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 (http://ninapro.hevs.ch) 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 1. Hand robotic prosthetics
• usually controlled via surface electromyography (sEMG) • low control capabilities • long training times
2. Ninapro sEMG acquisition setup • 8 electrodes equally spaced around the forearm • 2 electrodes placed on fingers extensor and flexor muscles
3. Inter-subject differences in the positioning of the electrodes
• have strong impact on the signal and on the classification task • are analyzed in few papers • can be diminished with spatial registration algorithms
Methods 1. Datasets
• 4 datasets of 27 subjects, 10 repetitions of 4 & 12 movements, acquired with 8 & 10 electrodes.
2. Armband rotation simulation • linear interpolation of signals from subsequent electrodes at
steps of 1/10 of the distance between each of the electrodes • evaluation of the rotated sEMG signal similarity through the
mean value of the Euclidean distance along the timeline • identification of the signals that minimize the distance
between the inter-subject sEMG signals
3. Classification • preprocessing: synchronization; low-pass filtering at 1Hz
(zero-phase second order Butterworth); normalization (0 mean and unitary standard deviation); averaging.
• classifier: multi class Least-Squares support vector machines (LS-SVM) with RBF kernel
• training: ten repetitions of each movement • 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
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!Figure 1: Inter-individual variability of sEMG signal patterns. Example of sEMG signal patterns from three subjects doing the same movement.
Figure 2: Maximum inter-subject s ignal s imi lar i ty improvement due to rotation simulation in percentage (top). Correspondent rotat ions (bottom).
• t h e a v e r a g e s i m i l a r i t y increase over all of the subjects due to rotation simulation is 8.12%, with standard deviation 7.02 %
• t h e m a x i m a l r o t a t i o n simulation increase in the similarity of signals across subjects is 33%
8 equally spaced electrodes • 4 movements: classification improved of 4.69 percentage points, with an increase of 8.09% (p=0.01) • 12 movements: classification improved of 1.21 percentage points, with an increase of 2.09% (p=0.04) 10 electrodes: • 4 movements: classification improved by 2.87 percent, with an increase of 4.74% (p=0.01) • 12 movements: classification improved of 1.94 percent, with an increase of 3.2% (p=0.02)
!Figure 4. Mean and standard deviations of the LS-SVM classification errors on the signal of the 8 electrodes equally spaced on the elastic armband (left) and from all the electrodes (right).
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