Tapwal Riya, Misra Sudip, Deb Pallav Kumar
Department of Computer Science and EngineeringIndian Institute of Technology Kharagpur Kharagpur 721302 India.
IEEE Sens J. 2022 Aug 17;23(2):906-913. doi: 10.1109/JSEN.2022.3198140. eCollection 2023 Jan.
In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.
在本文中,我们提出了一种智能床单——i-Sheet,用于远程监测新冠肺炎患者的健康状况。通常,实时健康监测对于新冠肺炎患者预防健康状况恶化至关重要。传统的医疗监测系统是人工操作的,需要患者输入信息才能开始监测健康状况。然而,患者在危急情况下以及夜间很难进行输入。例如,如果睡眠期间血氧饱和度下降,就很难监测到。此外,由于各种生命体征受到影响,而且即使康复后也有出现问题的可能性,因此需要一个能够监测新冠后遗症的系统。i-Sheet利用了这些特点,根据患者对床单的压力对新冠肺炎患者进行健康监测。它分三个阶段工作:1)感知患者施加在床单上的压力;2)根据数据波动将数据分类为不同组(舒适和不舒适);3)向护理人员提醒患者的状况。实验结果证明了i-Sheet在监测患者健康方面的有效性。i-Sheet能够有效地对患者状况进行分类,准确率达99.3%,功耗为17.5瓦。此外,使用i-Sheet监测患者健康状况时的延迟为2秒,这非常微小且可以接受。