Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Republic of Korea.
Department of Smart Wearable Engineering, Soongsil University, Seoul 156-743, Republic of Korea.
Sensors (Basel). 2023 Mar 14;23(6):3106. doi: 10.3390/s23063106.
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.
近年来，可穿戴设备已应用于健身领域，通过记录肌电图 (EMG) 信号来监测人体肌肉。了解运动过程中的肌肉激活情况可以使力量运动员获得最佳效果。水凝胶由于其一次性和皮肤附着力的特点，不适合可穿戴设备，因此，人们广泛研究了开发可替代水凝胶的干电极。在这项研究中，为了使其可穿戴，氯丁橡胶被浸渍有高纯度单壁碳纳米管，以开发出一种比水凝胶噪声更小的干电极。由于 COVID-19 的影响，人们对改善肌肉力量的锻炼（如家庭健身房和私人教练 (PT)）的需求有所增加。虽然有很多与有氧运动相关的研究，但缺乏可用于改善肌肉力量的可穿戴设备。这项初步研究提出了开发一种可穿戴设备的方案，该设备以臂套的形式出现，可通过使用九个基于纺织品的传感器记录手臂的 EMG 信号来监测肌肉活动。此外，一些机器学习模型被用于从基于纤维的传感器记录的 EMG 信号中分类三种手臂目标运动，如手腕卷曲、二头肌卷曲和哑铃后摆。结果表明，与湿电极相比，所提出电极记录的 EMG 信号噪声更小。这也被用于分类三种手臂运动的分类模型的高精度所证明。这个工作分类设备是可替代下一代 PT 的可穿戴设备的重要一步。
