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Smart Sensors And Remote Health Monitoring

 Mar 20, 2023
Smart Sensors and Remote Health Monitoring

Electronics are becoming a crucial component of Bio-medicine. The market for sensors, a quick-decision healthcare system based on early diagnosis, has developed quickly due to real-time fitness tracking, health monitoring, and the aim to detect early-stage disease. Hence, there is a constant need for diagnostic techniques to upgrade medical facilities. Accurately performing these studies has been made possible because of biosensor innovation. Biosensors look at POC applications' potential for enhanced healthcare management and work to make them more effective. Biosensors can now be made more automated and accurate with greater precision data sensing systems by integrating with MEMS and NEMS technologies.

Smart sensors self diagnose by looking for signs of abnormalities in internal signals. These sensors frequently carry out basic probes to find numerous potential flaws. Each form of possible fault is indicated by a unique code. Distinguishing between standard measurement errors and sensor malfunctions is a significant challenge that frequently occurs in self-diagnosis. The effect of a sensor defect on the accuracy of the measurement can be assessed using several methodologies. In some cases, even after an error has developed, the sensor can still be used. Henry's self-verification model offers a practical method for creating a validity index.

The use of biosensors with POC has boosted research into nanotechnology, cutting-edge functional sensing materials, and the creation of miniature sensing systems. Real-time monitoring is made possible by the Internet of Things (IoT), artificial intelligence (AI), and biosensors. One such application for biosensors is breath analysis. Breath analysis can be used to identify a wide range of disorders, including diabetes, urogenital infections, pancreatic infections, lung cancer, etc.

Due to variations in tobacco usage and air quality, lung cancer rates (13%) vary globally. 9.3% of lung cancer fatalities and 6.9% of lung cancer incidences worldwide occur in India. Lung cancer affected 1.8 million people in 2012. According to estimates, there were 112,350 cases of lung cancer in women and 121,680 cases in men in the United States in 2018. Whilst there are exceptions, lung cancer is more common in developed countries and is more often found in males who smoke. Female lung cancer cases are rising throughout Europe. The main causes, particularly in Asia, are occupational exposure and air pollution. Lung cancer can result from any infection that has been ignored in the past. Chemical exposure (to radon, asbestos, etc.) may be a major factor. Another risk factor is low-dose computed tomography (CT), which is used for screening. 

Single-walled carbon nanotubes (CNTs) for lung cancer detection, colorimetric sensor arrays, and glucose monitoring biosensors for diabetes are a few examples. Based on their material and construction, these sensors have great qualities, such as high sensitivity. As a result, these sensors are able to detect microscopic levels of biomarkers that are present at the beginning of a disease. While an array adds more dimensions to the observation, a collection of sensors offers a superior benefit than a single sensor. It enhances performance and supports the estimate of more parameters. Many diseases have distinct patterns of volatile organic compounds (VOCs). For volatile substances, the breath test is the only endogenous method.

With the use of appropriate machine learning (ML) algorithms and predictive logic models, data is gathered through these sensors. Significant scale integration, embedded systems, big data, ML, cloud computing, and AI, are also components of smart healthcare. The use of sensors that can collect and transmit data over a wide region with little financial outlay is made possible by wireless technology. Further technological advances are needed, including improvements to the employed sensors and logistic algorithms. Sometimes, illnesses like cancer go undetected until they are far advanced. Reduced cancer survival may be a result of this delay in symptomatic presentation detection. It impacted 285 million people in 2010. Negligence caused this estimate to rise to 430 million. Recent estimates indicate that 50% of diabetes cases do not receive an early diagnosis. One of the causes of the rise in diabetes mellitus cases and subsequent rise in human mortality is a lack of appropriate healthcare. 60% to 70% of older adults have Alzheimer's disease, a neurodegenerative condition (ND) that progresses over time and eventually results in dementia and death.

Hence, research is focused on creating noninvasive methods, such as biomarkers, for early diagnosis. According to the current situation, a mutation profile can be used to classify 50% of lung adenocarcinomas and around one-third of squamous cell carcinomas. Therapeutic approaches have been made easier by this molecular classification. In a perfect world, the discovery of medicinal chemicals and predictive analysis would go hand in hand. Predictive analysis can be done in a variety of ways, including immunohistochemistry, sample preservation, molecular testing, cell-free circulating tumor DNA, and breath analysis with breath sensors. The primary elements of breath analysis are e-noses, which are based on olfactory perception.

They have olfactory receptor cells, which have particular odor receptors of their own. The sensing system and pattern recognition are the two components that makeup these electronic noses (PR). Either a single sensor or an array of sensors may be used. To identify several substances, a group of sensors would be considerably more useful. Artificial neural networks can examine the obtained complex data (ANN). In terms of pattern recognition, ANN has been extremely successful. The future of health monitoring systems due to biosensors' involvement will revolutionize how medical science is developed and healthcare is managed, including data science, electronics, and IOT.


  1. https://www.sciencedirect.com/science/article/pii/B9780323851725000186
  2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537585/