Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.
In this study, the authors proposed a method to fabricate a resistive stretch textile sensor from polyester spandex (PET/SP) fabric and commercial single-walled carbon nanotube (SWCNT). In addition, we designed and trained a one-dimension convolutional neural network to classify four resistance workouts, which employed data acquired from the proposed sensor as the input. To figure out the most appropriate PET/SP sample for the deep learning application, we investigated morphologies and characterization of three samples in distinct conditions of the coating process. Data acquired from the proposed sensor illustrated the significant difference between activated and non-activated muscle groups in each specific exercise. With the PET/SP sample which met the requirements of the application, after 100 epochs, the deep learning model achieved 97.2% training accuracy and 90% test accuracy. This study demonstrates that the SWCNT-coated PET/SP stretch textile sensor can be utilized effectively to track the activity of forearm muscles during resistance training. Other than that, the proposed 1D-CNN, with the advantage of training time and computational cost, is able to classify time series data with high performance and thus can be applied widely in various deep learning applications, especially in the healthcare and sports industries.
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