School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.
Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.
Med Biol Eng Comput. 2021 Feb;59(2):431-447. doi: 10.1007/s11517-021-02319-9. Epub 2021 Jan 26.
Wearable electronics and sensors are increasingly popular for personal health monitoring, including smart shirts containing electrocardiography (ECG) electrodes. Optimal electrode performance requires careful selection of the electrode position. On top of the electrophysiological aspects, practical aspects must be considered due to the dynamic recording environment. We propose a new method to obtain optimal electrode placement by considering multiple dimensions. The electrophysiological aspects were represented by P-, R-, and T-peak of ECG waveform, while the shirt-skin gap, shirt movement, and regional sweat rate represented the practical aspects. This study employed a secondary data set and simulations for the electrophysiological and practical aspects, respectively. Typically, there is no ideal solution that maximizes satisfaction degrees of multiple electrophysiological and practical aspects simultaneously; a compromise is the most appropriate approach. Instead of combining both aspects-which are independent of each other-into a single-objective optimization, we used multi-objective optimization to obtain a Pareto set, which contains predominant solutions. These solutions may facilitate the decision-makers to decide the preferred electrode locations based on application-specific criteria. Our proposed approach may aid manufacturers in making decisions regarding the placement of electrodes within smart shirts.
可穿戴电子设备和传感器在个人健康监测中越来越受欢迎，包括含有心电图 (ECG) 电极的智能衬衫。为了获得最佳的电极性能，需要仔细选择电极位置。除了电生理方面，由于动态记录环境，还必须考虑实际方面。我们提出了一种新的方法，通过考虑多个维度来获得最佳的电极放置。电生理方面由心电图波形的 P 波、R 波和 T 波峰表示，而衬衫-皮肤间隙、衬衫运动和区域出汗率则代表实际方面。这项研究分别使用了二次数据集和模拟来研究电生理和实际方面。通常情况下，没有一个理想的解决方案可以同时最大化多个电生理和实际方面的满意度，妥协是最合适的方法。我们没有将这两个独立的方面结合到单个目标优化中，而是使用多目标优化来获得包含主要解决方案的 Pareto 集。这些解决方案可以帮助决策者根据特定应用的标准来决定首选的电极位置。我们提出的方法可以帮助制造商在智能衬衫内的电极放置方面做出决策。
