As the world is going through an existential global health crisis, i.e., the outbreak of novel coronavirus-caused respiratory disease , the healthcare systems of all the countries require readily available, low cost and highly precise equipment for the rapid diagnostics, monitoring, and treatment of the disease. The performance and precision of this equipment are solely dependent on the sensors being used. The advancement in research and development of micro-electro-mechanical systems (MEMS) based sensors during recent years, has resulted in the improvement of the conventional equipment being used in biomedical and health care applications. Microfluidics (Lab-on-a-chip) and MEMS sensors are now being used extensively for quick and accurate detection, progression monitoring, and treatment of various diseases including Covid-19. The ongoing miniaturization and design improvements have resulted in more precise sensors and actuators for healthcare applications, even for micro and nanoscale measurements in drug delivery and other invasive applications. This article aims at reviewing the MEMS sensors being used or which can be used in the important equipment for the detection and treatment of Covid-19 or other pandemics. An insight into various designs and working principles of the research-based and commercially available MEMS sensors is presented. The study highlights the role and importance of MEMS sensors in the improvement of equipment with conventional sensors. MEMS sensors outperform the conventional sensors due to their small size (1µm-1mm), negligible weight, prompt response, precise measurements, portability, and ease of integration with electronic circuitry.
Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.
Physiological signals retrieve the information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time and thus proper treatment can be made possible. With the addition of Internet of Things in healthcare, real-time data collection and pre-processing for signal analysis has reduced burden of in-person appointments and decision making on healthcare. Recently, Deep learning-based algorithms have been implemented by researchers for recognition, realization and prediction of diseases by extracting and analyzing the important features. In this research real-time 1-D timeseries data of on-body non-invasive bio-medical sensors have been acquired and pre-processed and analyzed for anomaly detection. Feature engineered parameters of large and diverse dataset have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring the implemented system uses wavelet time scattering features for classification and deep learning based autoencoder for anomaly detection of time series signals for assisting the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through bio-medical sensors using signal analysis techniques for anomaly detection and timeseries classification has been done for the disease prognosis in real-time. Wavelet time scattering based signals classification accuracy of 99.88% is achieved. In real time signals anomaly detection, 98% accuracy is achieved. The average Mean Absolute Error loss of 0.0072 for normal signals and 0.078 is achieved for anomaly signals.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.