Abstract
Recently, surface electromyography (sEMG) has been used to detect running-related works. sEMG provides a non-invasive and real-time method that allows quantification of muscle energy. However, noises in sEMG signals are a serious issue to be considered as these will interrupt the analysis of muscular activity. Hence, this work aims at distinguishing between sEMG valid signals and noises during running exercise by taking advantage of the combination of 3D-CNN and LSTM, which we called 3D-LCNN. Furthermore, according to the possible cases that happen in the sEMG data-collection procedure, we proposed two data-augmentation approaches to expend our sEMG dataset, which are the simulation of the surface electrodes displacement on the skin and the muscle fatigue. Experiment results show that the classification accuracy of the proposed 3D-LCNN can achieve 90.52%. Additionally, this work provides excellent service-oriented architecture (SOA). The recognition process can be done after the subject placed the sEMG sensors and performed a trial. Therefore, the process can help clinicians or therapists to distinguish between sEMG valid signals and noises more efficiently.
Highlights
In recent years, the number of runners keeps increasing
Running is a great way to achieve health, it is associated with a high risk of running-related injuries. surface electromyography (sEMG) signals are biomedical signals that measure electrical signals generated from muscles during muscle contraction [1]
ACCURACY COMPARISON WITH THE DIFFERENT DNN MODELS The experiment conducted is mainly to compare the performance of CNN, 3D-CNN, CNN+LSTM and 3D-LCNN models based on the self-collected sEMG dataset
Summary
The number of runners keeps increasing. running is a great way to achieve health, it is associated with a high risk of running-related injuries. sEMG signals are biomedical signals that measure electrical signals generated from muscles during muscle contraction [1]. The majority of research in sEMGbased muscle evaluation have focused on the isokinetic and isometric contraction [3]. The subject is asked to hold a dumbbell, the subject uses biceps to curl the dumbbell, and lower the dumbbell to his/her side repeatedly. The subject is asked to hold a dumbbell in a static position. There has been a dramatic increase in the number of publications on sEMG pattern recognition for hand gestures [5]–[7].
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