Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan.
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
Sensors (Basel). 2023 Jan 21;23(3):1251. doi: 10.3390/s23031251.
Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
呼吸监测是一种有效的人体健康感知和预测多种疾病的方法。文献中讨论了各种基于接触和非接触的方法来进行呼吸监测。在非接触式方法中，基于射频（RF）的呼吸监测最近变得非常流行。这种方法消除了隐私问题，并且用户无需携带设备。此外，通过提供智能数字健康技术，此类方法可以减轻医疗机构的压力。这些智能数字技术利用基于机器学习（ML）的系统来对呼吸异常进行分类。尽管基于 ML 的系统取得了进步，但数据的维度不断增加仍然是一个重大挑战，因为不相关的特征会显著影响所开发系统的性能。最优特征评分似乎是解决此问题的可行方法，因为它有可能显著提高系统性能。在本研究中，首先使用软件定义无线电（SDR）和 RF 感测技术来开发呼吸监测系统。由于呼吸运动导致无线信道状态信息（CSI）的微小变化，用于检测呼吸模式中的呼吸异常。此外，ML 算法智能地对单人及多人场景中的呼吸异常进行分类。通过参考可穿戴传感器对结果进行了验证。最后，使用最优特征评分来提高开发系统在准确性、训练时间和预测速度方面的性能。结果表明，最优特征评分可帮助实现高达 93.8%和 91.7%的单人及多人场景的最大准确性。
