Finger motion classification using surface-electromyogram signals

金沢大学理工研究域電子情報学系The finger movement has the information about force, speed to bend and the combination of fingers. If these information is estimated, the many degrees of freedom interface can apply it. In this study, we aimed for the many degrees of freedom finger movement classification. We tried each fingers classification and the estimate of the flexural finger force using surface-electromyogram signals. In the technique, amount of characteristic are a cepstral coefficient of EMG signals and an integral calculus EMG signals. A support vector machine performs learning and classtification. Therefore, I propose the classification technique and inspected a classification each finger and the combination of fingers by offline data handling using surface EMG signals. © 2010 IEEE

Robust finger motion classification using frequency characteristics of surface electromyogram signals

Finger motion classification using surface electromyogram (EMG) signals is currently being applied to myoelectric prosthetic hands with methods of pattern classification. It can be used to classify motion with great accuracy under ideal circumstances. However, the precision of classification falling to change the quantity of EMG feature with muscle fatigue has been a problem. We addressed this problem in this study, which was aimed at robustly classifying finger motion against changes in EMG features with muscle fatigue. We tested the changes in EMG features before and after muscle fatigue and propose a robust feature that uses a methods of estimating tension in finger motion by taking muscle fatigue into consideration. © 2012 IEEE

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface

http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition

Finger Motion Classification by Forearm Skin Surface Vibration Signals

The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis

Relating forearm muscle electrical activity to finger forces

The electromyogram (EMG) signal is desired to be used as a control signal for applications such as multifunction prostheses, wheelchair navigation, gait generation, grasping control, virtual keyboards, and gesture-based interfaces [25]. Several research studies have attempted to relate the electromyogram (EMG) activity of the forearm muscles to the mechanical activity of the wrist, hand and/or fingers [41], [42], [43]. A primary interest is for EMG control of powered upper-limb prostheses and rehabilitation orthotics. Existing commercial EMG-controlled devices are limited to rudimentary control capabilities of either discrete states (e.g. hand close/open), or one degree of freedom proportional control [4], [36]. Classification schemes for discriminating between hand/wrist functions and individual finger movements have demonstrated accuracy up to 95% [38], [39], [29]. These methods may provide for increased amputee function, though continuous control of movement is not generally achieved. This thesis considered proportional control via EMG-based estimation of finger forces with the goal of identifying whether multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm. Electromyogram (EMG) activity from the extensor and flexor muscles of the forearm was sensed with bipolar surface electrodes and related to the force produced at the four fingertips during constant-posture, slowly force-varying contractions from 20 healthy subjects. The contractions ranged between 30% maximum voluntary contractions (MVC) extension and 30% MVC flexion. EMG amplitude sampling rate, least squares regularization, linear vs. nonlinear models and number of electrodes used in the system identification were studied. Results are supportive that multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm, at least in healthy subjects. An EMG amplitude sampling frequency of 4.096 Hz was found to produce models which allowed for good EMG amplitude estimates. Least squares regularization with a pseudo-inverse tolerance of 0.055 resulted in significant improvement in modeling results, with an average error of 4.69% MVC-6.59% MVC (maximum voluntary contraction). Increasing polynomial order did not significantly improve modeling results. Results from smaller electrode arrays remained fairly good with as few as six electrodes, with the average %MVC error ranging from 5.13%-7.01% across the four fingers. This study also identified challenges in the current experimental study design and subsequent system identification when EMG-force modeling is performed with four fingers simultaneously. Methods to compensate for these issues have been proposed in this thesis

Selection of suitable hand gestures for reliable myoelectric human computer interface

Background: Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. Methods: Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. Results: When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion. Conclusion: This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor