Application of machine learning algorithms to the discretization problem in wearable electrical tomography imaging for bladder tracking

The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation

Application of machine learning algorithms to the discretization problem in wearable electrical tomography imaging for bladder tracking

The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease”

In this paper we investigated the use of smartphone sensors and Artificial Intelligence techniques for the automatic quantification of the MDS-UPDRS-Part III Leg Agility (LA) task, representative of lower limb bradykinesia. Methods: We collected inertial data from 93 PD subjects. Four expert neurologists provided clinical evaluations. We employed a novel Artificial Neural Network approach in order to get a continuous output, going beyond the MDS-UPDRS score discretization. Results: We found a Pearson correlation of 0.92 between algorithm output and average clinical score, compared to an inter-rater agreement index of 0.88. Furthermore, the classification error was less than 0.5 scale point in about 80% cases.Conclusions:Weproposedanobjectiveandreliabletoolfor theautomaticquantificationoftheMDS-UPDRSLegAgilitytask. In perspective, this tool is part of a larger monitoring program to be carried out during activities of daily living, and managed by the patients themselves

Evidence-based Development of Trustworthy Mobile Medical Apps

abstract: Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging. This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes: • Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment. • Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology. • Automatic code generation for MMAs: Investigating methods for automatically • Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Wearable sensors networks for safety applications in industrial scenarios

Industrial contexts, and in particular the port areas, are very complex systems to be monitored and controlled due to the combined presence of vehicles and people. The port areas are the gateway between navigation and terrestrial transportation and are of great importance in transport logistics. Unfortunately, the management of port areas is quite complex because the safety of the workers must be always assured. Therefore, in such a context, a centralized control system for the monitoring and the prevention of risks is of particular importance. In this thesis, a real-time control system for the monitoring of people and vehicles in industrial areas is proposed. The proposed system is based on the Internet of Things paradigm, i.e. a network of “things” (such as sensors, tag RFID, actuators etc.) which can communicate and interact with each other within a shared IP addressing range, in order to share data and contribute to the management and development of advanced applications. Specifically, the thesis is focused on the design of a wearable sensors network based on RFID technology, and specifically on WISP sensors, for assuring the safety of the workers. In this network, wearable devices that can be inserted directly on the textile have been selected. Differently from conventional sensors, wearable sensors ensure a higher level of comfort, and provide higher electromagnetic performance. Furthermore, textile materials are easily available. Microstrips are good candidates for these applications because they mainly radiate perpendicularly to the planar structure, and their ground plane allows a good shielding on the body tissues. Therefore, I have designed specific antennas for RFID, that unlike the classical microstrip antennas have the radiating surface composed of several "side by side" conductive "threads of textile". Since the microwave model does not allow the design of an antenna with these characteristics with a good approximation, a specific microwave model for coupled lines has been designed. With this model, the specific antenna for RFID has been designed, with Jeans as substrate. The particular antenna’s substrate allows direct integration into garments, but since the wearable antennas are placed very close to the human body, biological issues which may arise on the human body from the use of these sensors have been analysed. The Specific Absorption Rate (SAR) has been considered and simulations have been conducted for evaluating the effects on the human body, and especially on the head, when irradiated with the electromagnetic waves generated by the wearable antenna realized with different materials. Dosimetric effects have been evaluated in function of the distance from the body, in order to define a safe distance for placing the antenna on the human body. The SAR has been evaluated also for full patches with different textile substrates, whose surface is larger than that of the proposed model of coupled lines. Therefore, if the SAR values evaluated for the full patch are satisfying, the SAR values for the model of coupled lines will surely be acceptable

Wearable sensors networks for safety applications in industrial scenarios

Industrial contexts, and in particular the port areas, are very complex systems to be monitored and controlled due to the combined presence of vehicles and people. The port areas are the gateway between navigation and terrestrial transportation and are of great importance in transport logistics. Unfortunately, the management of port areas is quite complex because the safety of the workers must be always assured. Therefore, in such a context, a centralized control system for the monitoring and the prevention of risks is of particular importance. In this thesis, a real-time control system for the monitoring of people and vehicles in industrial areas is proposed. The proposed system is based on the Internet of Things paradigm, i.e. a network of “things” (such as sensors, tag RFID, actuators etc.) which can communicate and interact with each other within a shared IP addressing range, in order to share data and contribute to the management and development of advanced applications. Specifically, the thesis is focused on the design of a wearable sensors network based on RFID technology, and specifically on WISP sensors, for assuring the safety of the workers. In this network, wearable devices that can be inserted directly on the textile have been selected. Differently from conventional sensors, wearable sensors ensure a higher level of comfort, and provide higher electromagnetic performance. Furthermore, textile materials are easily available. Microstrips are good candidates for these applications because they mainly radiate perpendicularly to the planar structure, and their ground plane allows a good shielding on the body tissues. Therefore, I have designed specific antennas for RFID, that unlike the classical microstrip antennas have the radiating surface composed of several "side by side" conductive "threads of textile". Since the microwave model does not allow the design of an antenna with these characteristics with a good approximation, a specific microwave model for coupled lines has been designed. With this model, the specific antenna for RFID has been designed, with Jeans as substrate. The particular antenna’s substrate allows direct integration into garments, but since the wearable antennas are placed very close to the human body, biological issues which may arise on the human body from the use of these sensors have been analysed. The Specific Absorption Rate (SAR) has been considered and simulations have been conducted for evaluating the effects on the human body, and especially on the head, when irradiated with the electromagnetic waves generated by the wearable antenna realized with different materials. Dosimetric effects have been evaluated in function of the distance from the body, in order to define a safe distance for placing the antenna on the human body. The SAR has been evaluated also for full patches with different textile substrates, whose surface is larger than that of the proposed model of coupled lines. Therefore, if the SAR values evaluated for the full patch are satisfying, the SAR values for the model of coupled lines will surely be acceptable

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area