Abstract
Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.
Highlights
Electromyography (EMG) provides information related to muscle activity [1,2,3]
EMG signal acquisition can be done in different ways, nowadays one of the most used methods is by means of superficial electromyography, because in comparison with other methods, e.g., needles, it is one of the less invasive methods
The main aspect of this section is to show the maximal voluntary contraction (MVC) values, gathered from the isometric exercises, in order to check if the low-cost system behaves as the commercial system as explained before and confirms the reliability of the system
Summary
EMG devices are used in many research fields, such as biomedical, ergonomics, physiotherapy or sports performance applications, where it is very important to assess the behaviour of the muscles throughout the task [4] based on the changes in the electrical signal [5,6,7,8,9,10]. This kind of technology can be used to improve other studies [11]. Some researchers [12] consider that sEMG is as valid as other methods, taking into account that the acquired signal must be denoised
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