Advancing Muscle-Computer Interfaces with High-Density Electromyography

Abstract

In this paper we present our results on using electromyographic (EMG) sensor arrays for finger gesture recognition. Sensing muscle activity allows to capture finger motion without placing sensors directly at the hand or fingers and thus may be used to build unobtrusive body-worn interfaces. We use an electrode array with 192 electrodes to record a highdensity EMG of the upper forearm muscles. We present in detail a baseline system for gesture recognition on our dataset, using a naive Bayes classifier to discriminate the 27 gestures. We recorded 25 sessions from 5 subjects. We report an average accuracy of 90% for the within-session scenario, showing the feasibility of the EMG approach to discriminate a large number of subtle gestures. We analyze the effect of the number of used electrodes on the recognition performance and show the benefit of using high numbers of electrodes. Crosssession recognition typically suffers from electrode position changes from session to session. We present two methods to estimate the electrode shift between sessions based on a small amount of calibration data and compare it to a baseline system with no shift compensation. The presented methods raise the accuracy from 59% baseline accuracy to 75% accuracy after shift compensation. The dataset is publicly available.

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…Features extracted from HD-sEMG maps can be based on intensity information (any signal magnitude and power feature [18,22]) and spatial information (e.g., the mean shift [42] or the coordinates of the centre of gravity and maximum values [44]). These maps and additional spatial-based features can be used to reduce the effect of confounding factors that influence the performance of EMG pattern recognition such as the changing characteristics of the signal itself over time and electrode location shift [45] as well as variations in muscle contraction intensity [44]. However, this remains a relatively new sub-field, and novel image segmentation and spatial feature extraction methods are still needed to improve the performance of robust EMG pattern recognition.…”

“…In fact, several studies have shown that there is little need to use all EMG channels (over 100 electrodes), and instead, a properly positioned smaller set of electrodes (e.g., 9 [44] and 20-80 [45]) can provide comparable results. There is, however, no consensus on the global optimum number of electrodes yielding maximum recognition performance.…”

Gesture Recognition Method Utilizing Ultrasonic Active Acoustic Sensing

“…EMG sensors can recognize muscle activities. Amma et al [8] presented their results on using EMG sensor arrays for finger gesture recognition. However, changes in the EMG sensor values are small, and EMG sensors are adversely affected by electrical noise.…”

“…We performed fast Fourier transform (FFT) on the input from the contact microphone. In this study, the sampling rate was 96 kHz, and the number of FFT samples was 8,192. As shown in Fig.…”

“…Recognizing aspects of the user's internal state, such as muscle activity, is expected to enrich the user's experience in wearable computing. Although electromyography (EMG) sensors [8] are typically used to recognize the aspects of the internal state of the body, the changes in the EMG sensor values are small, and EMG sensors are adversely affected by electrical noise. Thus, we consider that it is difficult to utilize EMG sensors in daily life.…”