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Nanogenerator Based Self-Powered Sensors for Healthcare Applications
Published in Suresh Kaushik, Vijay Soni, Efstathia Skotti, Nanosensors for Futuristic Smart and Intelligent Healthcare Systems, 2022
Gaurav Khandelwa, Pandey Rajagopalan, Nirmal Prashanth Maria Joseph Raj, Xiaozhi Wang, Sang-Jae Kim
Zheng et al., in the same year, demonstrated self-powered cardiac monitoring via implantable TENG (i-TENG) (Zheng et al. 2016). The i-TENG consists of nanostructure PTFE as a negative layer and Al foil as the opposite electrode and a positive triboelectric layer (Figure 5a1). The stability and resistance of i-TENG to work in in vivo conditions were enhanced by PDMS and parylene encapsulation. In phosphate buffer saline (PBS) solution, the voltage and current output generated by encapsulated i-TENG are 45 V and 7.5 μA, respectively. Moreover, the i-TENG exhibit no cytotoxicity on L929 cells. The in vivo cardiac monitoring was achieved by placing the i-TENG in between the pericardium and heart of the porcine. The electric signal produced was in good synchronization with the heartbeat. The effect of implantation site on the i-TENG output was also studied. Additionally, the effect of physiological parameters on the i-TENG was also considered in the study. The implantable wireless transmitter (iWT) and power management unit (PMU) were used to design a self-powered wireless transmission system. The wireless transmitted signal (WTS) corresponding to 60, 80 and 120 bpm is shown in Figure 5a2. The developed i-TENG was stable even after 72 h of implantation.
Inhabiting practices
Published in Gretchen Coombs, Andrew McNamara, Gavin Sade, Undesign, 2018
The Smart Heart necklace seeks to address the discomfort experienced by users who need to have cardiac monitoring. Current Holter monitors typically comprise small recorders worn on a sling or belt that can continuously collect data over a 24- to 48-hour period from two or three ECG leads attached to the chest. The primary users of cardiac monitors are in the 70–85 year age range, and they often require tests or treatments that require the use of adhesives. Within this age group, many people exhibit skin conditions ranging from decreased fluid retention, thinning, xerosis (dryness), eczema, irritation and itching (Barr, 2006). Regular, repeated use of ECG diagnostic systems using conventional wet electrodes may exacerbate these symptoms. Removal of tape and adhesives can, for instance, increase the risk of eczema (Avenel-audran et al., 2003; Konya et al., 2010) and, in severe cases, can result in skin tears (Reddy, 2008). In order to sidestep the need for adhesives, a key area of product innovation in the project has been the development of a conductive woven band to house the electronics for the necklace. This band, which has been created with a team of weavers, detects the ECG at the back of the neck and seamlessly delivers this information to the jewel structures at the front of the necklace, which house signal processing equipment and battery capacity, negating the requirement for stick-on electrodes and bulky technology.
Biomedical Algorithms for Wearable Monitoring
Published in Christopher Siu, Krzysztof Iniewski, IoT and Low-Power Wireless, 2018
Su-Shin Ang, Miguel Hernandez-Silveira
To this end, biomedical problems are categorised into three typical types–optimisation of a pre-defined objective function (Type 1), static classification problems (Type 2) and predictive models (Type 3). These types will be delineated in this chapter. In addition, several case studies will be presented to illustrate the different problem types. Here we provide a brief review of state-of-the art approaches, including some created and used by us as part of our current developments. For example, these include the compression of electrocardiogram signals to reduce the bandwidth and energy requirements for cardiac monitoring, calorie expenditure estimation, arrhythmia detection, confidence level for computed physiological vital signs and fall prediction/detection. Although the level of complexity and description of these examples is simple, we hope that these cases provide initial insights for enthusiasts to start developing their own algorithms for wearable devices intended for medical and wellness applications.
Network modeling and Internet of things for smart and connected health systems—a case study for smart heart health monitoring and management
Published in IISE Transactions on Healthcare Systems Engineering, 2020
Hui Yang, Chen Kan, Alexander Krall, Daniel Finke
The key in cardiac monitoring is to detect the changes in cardiac activities and identify disease patterns in the early stage. In the literature, a variety of algorithms were designed to extract useful features and patterns from ECG signals for the detection of cardiac diseases. For example, Elmberg et al. (Elmberg et al., 2016) measured QRS prolongation in 12-lead ECGs to quantify the severity of ischemia. Meo et al. (Meo et al., 2013) characterized the variability of f-wave amplitude from 12-lead ECGs to predict the catheter ablation outcome of atrial fibrillation. Perlman et al. (Perlman et al., 2016) extracted features from QRS complex in 12-lead ECGs and developed a classification tree scheme for the identification of supraventricular tachycardia. Our previous works have also investigated nonlinear dynamics algorithms to recognize disease-altered ECG patterns for the detection and identification of myocardial infarction, atrial fibrillation, bundle branch block, and other cardiac diseases (Chen & Yang, 2013; Yang, 2011; Yang et al., 2012). For example, customized wavelet functions were designed to extract fiducial patterns of ECG signals for the detection of atrial fibrillations (Yang et al., 2007). Also, heterogeneous recurrence analysis was proposed to characterize heart rate variability from ECG signals for the identification of dynamic transitions and obstructive sleep apnea (Cheng et al., 2016; Chen & Yang, 2014, 2015). A self-organizing network was developed to characterize pattern dissimilarities among QRS complexes in the ECGs and then recognize abnormal patterns induced by the left bundle branch block (LBBB) (Yang & Leonelli, 2016).