Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

A Non-invasive Approach to Detection Blood Glucose Levels with Hand Skin Image Processing Using Smartphone

Measuring blood sugar levels today still use invasive techniques that are painful so non-invasive monitoring is needed. This study aims to develop a non-invasive technique to identify and detect blood glucose through hand-skin image processing. This development method is by taking invasive blood glucose hand images and 30 participants aged 20-60 years, data analysis is done by image preprocessing, determining the Gray level co-occurrence matrix (GLCM) value, using the backpropagation algorithm to conduct training and data testing. to define a blood glucose monitoring model. The blood glucose detection model is implemented through the android operating system on smartphones by developing the GULAABLE application on smartphones which is simple and easy to use and without blood sampling. This GULAABLE application is to determine the condition of low or high blood glucose and can be used routinely at a low cost. Validating the results by identifying this non-invasive application compared with the results of invasive glucose measurements by applying to 10 participants, the identification results show an accuracy of 80%, so it can be concluded that the GULAABLE application method on smartphones can be used to monitor blood glucose conditions at any time by simply taking hand skin image

Automated Medical Device Display Reading Using Deep Learning Object Detection

Telemedicine and mobile health applications, especially during the quarantine imposed by the covid-19 pandemic, led to an increase on the need of transferring health monitor readings from patients to specialists. Considering that most home medical devices use seven-segment displays, an automatic display reading algorithm should provide a more reliable tool for remote health care. This work proposes an end-to-end method for detection and reading seven-segment displays from medical devices based on deep learning object detection models. Two state of the art model families, EfficientDet and EfficientDet-lite, previously trained with the MS-COCO dataset, were fine-tuned on a dataset comprised by medical devices photos taken with mobile digital cameras, to simulate real case applications. Evaluation of the trained model show high efficiency, where all models achieved more than 98% of detection precision and more than 98% classification accuracy, with model EfficientDet-lite1 showing 100% detection precision and 100% correct digit classification for a test set of 104 images and 438 digits.Comment: 6 pages, 5 figure

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

A Survey of AI-based Approaches for Processing Photoplethysmography Signals

Photoplethysmography (PPG) is a non-invasive optical technique that measures physiological parameters like heart rate, blood oxygen saturation, and blood volume. However, PPG signals are often noisy and contaminated with artifacts, posing challenges to inaccurate measurements. To address this, artificial intelligence (AI) techniques have been employed by many researchers to improve  PPG signal processing. This paper presents a comprehensive survey of AI-based approaches for processing PPG signals in recent years. Various AI techniques, including machine learning, deep learning, and natural language processing, are discussed in relation to their application in PPG signal analysis.  The limitations and challenges associated with AI-based approaches in this context are also explored. Furthermore, future research directions are highlighted to leverage AI’s potential for revolutionizing PPG signal processing and expanding its applications. By examining the latest advancements, this survey aims to guide researchers and practitioners in understanding and harnessing AI-based methods for enhanced PPG signal processing, contributing to improved healthcare monitoring and diagnosis