Abstract
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.
Highlights
Muscle fatigue is a very common occurrence in daily life
The information gathered in the experiment is presented below
We will look at the information related to the volunteers’ classification, and later the EMG signal and the indicators needed to define the behaviour of muscle fatigue
Summary
Muscle fatigue is a very common occurrence in daily life. Human-computer interfaces using bioelectrical signals as inputs can become valuable tools in order to improve the life quality of people, especially for people who have disabilities or injuries. These interfaces provide communication, situation control and feedback between users and their surroundings. Common classes of bio-signals used to control assistive devices are [2]: electromyography (EMG) [3], electroencephalography (EEG) [4], electrooculography (EOG) [5], and fusions of these signals [6]
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