On‐line supervising mechanism for learning data in surface electromyogram motion classifiers

Abstract

SUMMARYThis paper proposes a mechanism that supervises the learning data set for the classification from electromyogram to forearm motion. The supervising mechanism contains automatic data addition and automatic data elimination processes. It also contains manual data addition that is the same as our former algorithm. Both the automatic addition and elimination processes evaluate success or failure of classification from the continuity of the classifier's outputs. These processes assume that a person cannot change his motion within a certain interval. In experiments, a system with the supervising mechanism embedded classifies ten forearm motions from two channels of the electromyogram. First, this paper makes it clear that the proposed mechanism is effective by comparison with six settings, including the absence of a supervising pattern. Next, it is verified that the system can adapt to alteration of the operator's characteristics by a sensor-shifting test in which we move one sensor after the operator's training. From the experimental results, it is concluded that the proposed mechanism can adjust the generated decision boundaries for improvement of classification ability, and in addition is capable of tracking the alteration of the operator's characteristics through time.

Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback

“…New techniques and algorithms will allow taking full advantage of the many degrees of freedom of these artificial limbs, and hopefully will allow the user to acquire more natural and intuitive control and manipulation of the prosthesis. Different data mining methods have been used to extract and predict information from EMG sensors, being neural networks the most commonly used [1][2][3][12][13][14][15][16]. In [17], R. Ashan et al made a review on the different types of classifiers used until this day for EMG extraction for Human Computer Interaction applications.…”

“…In previous studies, it has been reported that up to 10 wrist and hand motions could be recognized from 2-3 channels of forearm electromyogram (EMG) [2,3]. Other studies have used non-stationary EMG at the beginning of motion [4] or mechanomyogram (MMG) as the signal source for the motion intention detection [5].…”

“…C. Martelloni, et al, in [12], used a Support Vector Machine based pattern recognition algorithm as an attempt to predict different grips, trying to reproduce the finding in monkeys described in [14]. They were able to discriminated 3 different grips (palmar, lateral or pinch grip), but they used data obtained from the forearm muscles activities of the flexor carpi radialis and extensor carpi radialis, which have been used in the previous studies to control prosthetic hands [2][3][4][5]16]. Therefore, this makes it difficult to determine the contribution the proximal muscles to the motion detection of the hand.…”

Robust finger motion classification using frequency characteristics of surface electromyogram signals

“…Nishikawa proposed an on-line supervising mechanism for learning data in surface electromyogram motion classifiers [6]. Kiso proposed robust discrimination of motion based on human myoelectric potential by adaptive fuzzy inference by taking muscle fatigue [7].…”

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

“…Robotic devices, which imitate the shape and function of a missing limb, are manufactured for use by people who lose their limb in such situations. In recent years, researchers have studied to design and control multifunctional prosthetics hand [1][2][3][4][5][6][7]. The complexity of the movement, that is, the number of independent movements, increases in proportion to the number of joints.…”