Biomedical Engineering is an emerging field of research that takes principles from physical and applied science and applies them to biology and medicine. Biomedical engineers and specialists’ works at the intersection of engineering, healthcare, and life sciences. Recent advancement in the semiconductor industry like scaling down of the electronic devices together with the progress in the computer sciences such as the emergence of data science, cloud computing and so forth has activated numerous new biomedical applications. The impact is progressive and the entire field of medical care got benefited from this. We, nowadays, see personalization in healthcare based on individual pathological needs, storage of long-time information (ECG, SPO2 etc.) and analysis for identification of pathological indicators (blood pressure, perfusion index, glucose level etc.). All in all, continuous and real-time monitoring of the patients with chronic diseases have become easier than before and taking advantage of this we soon will be able to do early prediction of diseases too.
The figure below illustrates a basic model of IOT based biomedical solution with monitoring, storage, complex decision making and feedback ability. Usually, a device is attached to the subjects’ body for data collection and transmission. Such device comes with analog front-end circuit containing sensors, amplifiers and filters used for obtaining physiological signals (ECG, PPG, and EEG etc.), a processing unit and a communication module. Availability of miniaturized integrated circuits (IC) at a low cost and low power operability are the reasons we now have wearable and implantable biosensors thanks to the evolution of microelectronics technology. To reduce the burden of processing bulk data acquired from the subject as well as to minimize power consumption, most of the longterm information is sent to the cloud storage through the Internet. Progress in the “Internet of Things” technologies have made it easier than ever to establish such links between front-end module and storage. A server with biomedical signal processing ability can then perform primary processing tasks such as noise cleaning (filtering), identification of basic pathological indicators (QRS complex detection, R-R interval measurement etc.). “Machine Learning” and “Big Data” approach can also be followed for more complex decision making (ex. abnormal heart activity), classification of diseases (ex. Cardiac disease detection) and early prediction of a pathological condition (ex. Stroke prediction). Finally, this analysis result can readily be available to the general user, authorized doctors and clinics with the help of the mobile based application, in some cases almost immediately after the attachment of the device to the subject’s body.
Since 2017, TnR Lab at NSU had been working on IOT based Biomedical Systems Development. One of our research focus is on reducing Stillbirth locally and globally. We are trying to build up a framework to tackle stillbirth that causes annually 2.65 million death of fetus in mother’s womb before they even see the sunshine of this world. It’s been known that absence of continuous and real-time monitoring and analysis of fetus wellbeing throughout gestation is one of the reasons behind this. Like stillbirth, many other chronic health challenges can be met with the help of IOT based biomedical systems and solutions and are up there for us to grab.
Ex-Assistant Professor & Principal Investigator
TnR Lab, North South University