A comparative analysis of hospital -based physician monitoring systems

The purpose of the multiple case-study was to determine how hospital subsystems (such as physician monitoring and credentialing; quality assurance; risk management; and peer review) were supporting the monitoring of physicians? Three large metropolitan hospitals in Texas were studied and designated as hospitals #1, #2, and #3. Realizing that hospital subsystems are a unique entity and part of a larger system, conclusions were made on the premises of a quality control system, in relation to the tools of government (particularly the Health Care Quality Improvement Act (HCQIA)), and in relation to itself as a tool of a hospital.^ Three major analytical assessments were performed. First, the subsystems were analyzed as to their "completeness"; secondly, the subsystems were analyzed for "performance"; and thirdly, the subsystems were analyzed in reference to the interaction of completeness and performance.^ The physician credentialing and monitoring and the peer review subsystems as quality control systems were most complete, efficient, and effective in hospitals #1 and #3. The HCQIA did not seem to be an influencing factor in the completeness of the subsystem in hospital #1. The quality assurance and risk management subsystem in hospital #2 was not representative of completeness and performance and the HCQIA was not an influencing factor in the completeness of the Q.A. or R.M. systems in any hospital. The efficiency (computerization) of the physician credentialing, quality assurance and peer review subsystems in hospitals #1 and #3 seemed to contribute to their effectiveness (system-wide effect).^ The results indicated that the more complete, effective, and efficient subsystems were characterized by (1) all defined activities being met, (2) the HCQIA being an influencing factor, (3) a decentralized administrative structure, (4) computerization an important element, and (5) staff was sophisticated in subsystem operations. However, other variables were identified which deserve further research as to their effect on completeness and performance of subsystems. They include (1) medical staff affiliations, (2) system funding levels, (3) the system's administrative structure, and (4) the physician staff "cultural" characteristics. Perhaps by understanding other influencing factors, health care administrators may plan subsystems that will be compatible with legislative requirements and administrative objectives. ^

Enhancement of a body area network to support smart health monitoring at the digital home

The deployment of home-based smart health services requires effective and reliable systems for personal and environmental data management. ooperation between Home Area Networks (HAN) and Body Area Networks (BAN) can provide smart systems with ad hoc reasoning information to support health care. This paper details the implementation of an architecture that integrates BAN, HAN and intelligent agents to manage physiological and environmental data to proactively detect risk situations at the digital home. The system monitors dynamic situations and timely adjusts its behavior to detect user risks concerning to health. Thus, this work provides a reasoning framework to infer appropriate solutions in cases of health risk episodes. Proposed smart health monitoring approach integrates complex reasoning according to home environment, user profile and physiological parameters defined by a scalable ontology. As a result, health care demands can be detected to activate adequate internal mechanisms and report public health services for requested actions.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Computer-based patient monitoring systems /

Prepared under contract no. HSM 110-70-406.

Field Testing and Development of Novel SHM systems at Queen’s University Belfast

Ageing and deterioration of infrastructure is a challenge facing transport authorities. In
particular, there is a need for increased bridge monitoring in order to provide adequate
maintenance and to guarantee acceptable levels of transport safety. The Intelligent
Infrastructure group at Queens University Belfast (QUB) are working on a number of aspects
of infrastructure monitoring and this paper presents summarised results from three distinct
monitoring projects carried out by this group. Firstly the findings from a project on next
generation Bridge Weight in Motion (B-WIM) are reported, this includes full scale field testing
using fibre optic strain sensors. Secondly, results from early phase testing of a computer
vision system for bridge deflection monitoring are reported on. This research seeks to exploit
recent advances in image processing technology with a view to developing contactless
bridge monitoring approaches. Considering the logistical difficulty of installing sensors on a
‘live’ bridge, contactless monitoring has some inherent advantages over conventional
contact based sensing systems. Finally the last section of the paper presents some recent
findings on drive by bridge monitoring. In practice a drive-by monitoring system will likely
require GPS to allow the response of a given bridge to be identified; this study looks at the
feasibility of using low-cost GPS sensors for this purpose, via field trials. The three topics
outlined above cover a spectrum of SHM approaches namely, wired monitoring, contactless
monitoring and drive by monitoring

Implementation of a drive-by monitoring system for transport infrastructure utilising GNSS

Ageing and deterioration of infrastructure is a challenge facing transport authorities. In particular, there is a need for increased bridge monitoring in order to provide adequate maintenance, prioritise allocation of funds and guarantee acceptable levels of transport safety. Existing bridge structural health monitoring (SHM) techniques typically involve direct instrumentation of the bridge with sensors and equipment for the measurement of properties such as frequencies of vibration. These techniques are important as they can indicate the deterioration of the bridge condition. However, they can be labour intensive and expensive due to the requirement for on-site installations. In recent years, alternative low-cost indirect vibrationbased SHM approaches have been proposed which utilise the dynamic response of a vehicle to carry out “drive-by” pavement and/or bridge monitoring. The vehicle is fitted with sensors on its axles thus reducing the need for on-site installations. This paper investigates the use of low-cost sensors incorporating global navigation satellite systems (GNSS) for implementation of the drive-by system in practice, via field trials with an instrumented vehicle. The potential of smartphone technology to be harnessed for drive by monitoring is established, while smartphone GNSS tracking applications are found to compare favourably in terms of accuracy, cost and ease of use to professional GNSS devices.

Development of a Wireless MEMS Multifunction Sensor System and Field Demonstration of Embedded Sensors for Monitoring Concrete Pavements, TR-637, 2016

Pavements tend to deteriorate with time under repeated traffic and/or environmental loading. By detecting pavement distresses and damage early enough, it is possible for transportation agencies to develop more effective pavement maintenance and rehabilitation programs and thereby achieve significant cost and time savings. The structural health monitoring (SHM) concept can be considered as a systematic method for assessing the structural state of pavement infrastructure systems and documenting their condition. Over the past several years, this process has traditionally been accomplished through the use of wired sensors embedded in bridge and highway pavement. However, the use of wired sensors has limitations for long-term SHM and presents other associated cost and safety concerns. Recently, micro-electromechanical sensors and systems (MEMS) and nano-electromechanical systems (NEMS) have emerged as advanced/smart-sensing technologies with potential for cost-effective and long-term SHM. This two-pronged study evaluated the performance of commercial off-the-shelf (COTS) MEMS sensors embedded in concrete pavement (Final Report Volume I) and developed a wireless MEMS multifunctional sensor system for health monitoring of concrete pavement (Final Report Volume II).

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Structural Data Acquisition Using Sensor Network

The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Integral Piezoactuator System with Optimum Placement of Functionally Graded Material - A Topology Optimization Paradigm

Piezoactuators consist of compliant mechanisms actuated by two or more piezoceramic devices. During the assembling process, such flexible structures are usually bonded to the piezoceramics. The thin bonding layer(s) between the compliant mechanism and the piezoceramic may induce undesirable behavior, including unusual interfacial nonlinearities. This constitutes a drawback of piezoelectric actuators and, in some applications, such as those associated to vibration control and structural health monitoring (e. g., aircraft industry), their use may become either unfeasible or at least limited. A possible solution to this standing problem can be achieved through the functionally graded material concept and consists of developing `integral piezoactuators`, that is those with no bonding layer(s) and whose performance can be improved by tailoring their structural topology and material gradation. Thus, a topology optimization formulation is developed, which allows simultaneous distribution of void and functionally graded piezoelectric materials (including both piezo and non-piezoelectric materials) in the design domain in order to achieve certain specified actuation movements. Two concurrent design problems are considered, that is the optimum design of the piezoceramic property gradation, and the design of the functionally graded structural topology. Two-dimensional piezoactuator designs are investigated because the applications of interest consist of planar devices. Moreover, material gradation is considered in only one direction in order to account for manufacturability issues. To broaden the range of such devices in the field of smart structures, the design of integral Moonie-type functionally graded piezoactuators is provided according to specified performance requirements.

Structural refinement of systems specified in object-z and CSP

This paper is concerned with methods for refinement of specifications written using a combination of Object-Z and CSP. Such a combination has proved to be a suitable vehicle for specifying complex systems which involve state and behaviour, and several proposals exist for integrating these two languages. The basis of the integration in this paper is a semantics of Object-Z classes identical to CSP processes. This allows classes specified in Object-Z to be combined using CSP operators. It has been shown that this semantic model allows state-based refinement relations to be used on the Object-Z components in an integrated Object-Z/CSP specification. However, the current refinement methodology does not allow the structure of a specification to be changed in a refinement, whereas a full methodology would, for example, allow concurrency to be introduced during the development life-cycle. In this paper, we tackle these concerns and discuss refinements of specifications written using Object-Z and CSP where we change the structure of the specification when performing the refinement. In particular, we develop a set of structural simulation rules which allow single components to be refined to more complex specifications involving CSP operators. The soundness of these rules is verified against the common semantic model and they are illustrated via a number of examples.