Robotics, IoT and AI Technologies in Bioengineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1335

Special Issue Editors

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Department of Civil, Energy, Environmental and Materials Engineering (DICEAM), Mediterranean University of Reggio Calabria, Reggio Calabria, Italy
Interests: biomedical signal processing and sensors; photonics; optical fibers; MEMS; metamaterials; nanotechnology; artificial intelligence; neural network; virtual reality; augmented reality; indoor navigation
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Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
Interests: artificial intelligence; machine learning; image processing; neural networks; machine intelligence

Special Issue Information

Dear Colleagues,

Bioengineering is a discipline that blends many aspects of traditional engineering fields with health care issues. The main objective is the creation of digital tools, devices and software platforms; as well as the implementation of advanced tools, from IoT, Artificial Intelligence and robotics to Cloud computing, smart wearables and intelligent analytics, with the ultimate aim of improving the quality and duration of life for patients. The evolution of Bioengineering is closely connected with developments in automation, nanomaterials engineering, artificial intelligence and neuroscience. From an application point of view, for example, Artificial Intelligence has proven to be efficient in many ways in the medical field, from the improvement of image-based diagnostics, analysis of biological signals, recognition of human activities through accelerometric signals, navigation guidance for subjects with cognitive problems, to the design of neuro-integrated prosthetic systems and compatible organ tissues for transplantation, surgery, prediction of behavior and nervous responses to stimuli. All this was possible thanks to the acquisition of huge volumes of digitized data and the machine learning technique. Robotics is also key branch in the field of surgery, enabling for minimally invasive surgeries and for the automatic monitoring of surgical instruments to assist the operator. Telepresence robots have also been designed to help socially isolated people as well as aid with rehabilitation. They are also used as wearable devices for injury prevention. The biomedical applications of IoT are now present in remote patient management, the monitoring of Parkinson's and Alzheimer's patients, vital data monitoring, depression monitoring via smartwatch, glucose monitoring and efficient drug management. It is essential that certain functionalities such as interoperability between all devices, platforms and technologies, and data security are ensured. In the literature, there are different application systems oriented toward health care that can help a sick person maintain or improve their independence and security.

The aim of this research topic is to improve the opportunities that different technologies can offer in improving the quality and duration of life.

Dr. Luigi Bibbò
Dr. Alessia Bramanti
Guest Editors

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  • Artificial Intelligence
  • IoT
  • human–robot interactions
  • wearable sensors
  • virtual reality/augmented reality (VR/AR)

Published Papers (1 paper)

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25 pages, 2289 KiB  
Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification
by Mohammud Shaad Ally Toofanee, Mohamed Hamroun, Sabeena Dowlut, Karim Tamine, Vincent Petit, Anh Kiet Duong and Damien Sauveron
Appl. Sci. 2023, 13(23), 12776; - 28 Nov 2023
Viewed by 859
It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly [...] Read more.
It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models. Full article
(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)
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