Abstract
This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.
Highlights
Background & SummaryHigh-density surface electromyography (HD-sEMG) is a method for the recording of Motor Unit Action Potentials (MUAP) over a muscle, using 2D arrays of closely-spaced electrodes
Unlike traditional surface electromyography, it accounts for both the spatial and temporal characteristics of the signal allowing a broader assessment of muscle electrophysiological activity
This paper aims to describe and provide a database of high-density surface EMG signals (HD-sEMG) signals[5,6] during voluntary isometric contractions of arm and forearm muscles of 12 healthy subjects
Summary
High-density surface electromyography (HD-sEMG) is a method for the recording of Motor Unit Action Potentials (MUAP) over a muscle, using 2D arrays of closely-spaced electrodes. The recorded signal has three dimensions: two in the space and one in the time This technique has gained attention during the last years for different applications such as signal decomposition (i.e., isolation and classification of individual MUAPs from the sEMG signal)[1], the study of neuromuscular compartmentalization[2], the analysis of changes in the spatial distribution of MUAPs with exercise or pain[3], and pattern recognition for identification of movement intention[4], among others. By using combinations of features based on spatial distribution (that is, in the spatial domain) and intensity of HD-sEMG7,8, it was possible to obtain higher performance in the classification than using traditional time-domain (TD) features or frequency-domain (FD) features (examples of these last can be found in[9]) Other potential applications are the design and evaluation of methods for the automatic detection of innervation zones and the exploration of other spatial features to improve the identification of movement intention
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