Zhan Yaoliang, Wang Xue, Yang Jin
Chongqing Key Laboratory of Photo-Electronic Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China.
Chongqing Municipal Key Laboratory of Photo-Electronic Materials and Engineering of Higher Education, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China.
Sensors (Basel). 2025 Jul 6;25(13):4213. doi: 10.3390/s25134213.
The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users' daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals' daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals' physiological signals and assessing exercise intensity.
心电图（ECG）信号在医学诊断、家庭护理和运动强度评估中起着至关重要的作用。然而，传统的心电图监测设备由于缺乏便携性、穿戴性差以及电极使用方法不便，难以融入用户的日常生活。因此，利用可重复使用且经济高效的柔性生物电极（信噪比为33 dB），提出了一种用于无约束心电图监测的小型化可穿戴系统（MWS），其尺寸为65×52×12 mm，重量为69 g。鉴于这些引人注目的特性，该系统能够在个体的日常活动（包括坐着、行走和骑自行车）中实现可靠且高质量的心电图信号监测，采集到的信号呈现出可区分的QRS特征。此外，基于心电图特征和全连接神经网络（FCNN）算法开发了一种运动强度分类模型，评估准确率为98%。这些结果表明MWS在跟踪个体生理信号和评估运动强度方面具有广阔的潜力。
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