Implementations of Wireless and Wired Intelligent Systems for Healthcare with Focus on Diabetes and Ultrasound Applications

The research and implementations presented in this thesis focuses mainly on healthcare applications utilizing the wireless and wired communication and “Micro-Electro-Mechanical Systems” (MEMS) technologies, and secondly on security aspects. Chapters four and five presents new work in intelligent diabetes remote monitoring front-end system and into the corresponding new ultrasound simulator training systems. The motivation from the University of Sheffield of Electronic and Electrical Engineering Department and Sheffield Children Hospital with the partial grant scholarship from “Engineering and Physical Sciences Research Council” (EPSRC) for involvement in one “Collaborations for Leadership in Applied Health Research and Care” (CLAHRC) projects, was to improve the existing WithCare+ system and also the development of multiple new front-end solutions for it. My motivation to create solutions which will improve the life of patients who suffer from chronic disease such as type-1 diabetes, and also to provide new methods in management of that illness by clinicians and possible resulting annual government money saving, drives me to the successful result. From the other side, the motivation from the department of Neonatal in Sheffield Royal Hallamshire Hospital and the University of Sheffield of Electronic and Electrical Engineering Department drives me to the creation of a new, very low cost ultrasound simulation training system, using new components such as MEMS sensors. The hardware design and embedded source code was created in order to provide a ready library, for use by other projects, where 3D space orientation is required through exploitation of MEMS sensors and intelligent fusion filter algorithm. The third contribution affects the cryptographic aspects. The new implementation of fast and very efficient portable C code algorithm for t-adic NAF Key generation in ECC cryptographic principle for utilization of it with Koblitz curves presented in Appendix I

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet’s 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F’s superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time–frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches