Measurement and data transmission validity of a multi-biosensor system for real-time remote exercise monitoring among cardiac patients

Background: Remote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.

Objective: Our aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.

Methods: Participants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.

Results: Median heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all P≤.003) due to the large number of observations. Measurement reliability was generally excellent (rho=.87-.97, all P<.001; intraclass correlation coefficient [ICC]=.94-.98, all P<.001; coefficient of variation [CV]=2.24-7.94%), although respiratory rate measurement reliability was poor among AF participants (rho=.43, P<.001; ICC=.55, P<.001; CV=16.61%). Data loss was minimal (<5%) when all system components were active; however, instability of the network hosting the remote data capture server resulted in data loss at the remote Internet server during some trials.

Conclusions: System validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.

A real-time abnormality detection system for intensive care management

Detecting abnormalities from multiple correlated time series is valuable to those applications where a credible realtime event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). For example, in an intensive care scenario, anesthetists perform a vital role in monitoring the patient and adjusting the flow and type of anesthetics to the patient during an operation. An early awareness of possible complications is vital for an anesthetist to correctly react to a given situation. In this demonstration, we provide a comprehensive medical surveillance system to effectively detect abnormalities from multiple physiological data streams for assisting online intensive care management. Particularly, a novel online support vector regression (OSVR) algorithm is developed to approach the problem of discovering the abnormalities from multiple correlated time series for accuracy and real-time efficiency. We also utilize historical data streams to optimize the precision of the OSVR algorithm. Moreover, this system comprises a friendly user interface by integrating multiple physiological data streams and visualizing alarms of abnormalities. © 2013 IEEE.

Real-time quartz crystal microbalance monitoring of free docosahexaenoic acid interactions with supported lipid bilayers.

Docosahexaenoic acid (DHA) is the most abundant polyunsaturated omega-3 fatty acid found in mammalian neuronal cell membranes. Although DHA is known to be important for neuronal cell survival, little is know about how DHA interacts with phospholipid bilayers. This study presents a detailed quartz crystal microbalance with dissipation monitoring (QCM-D) analysis of free DHA interactions with individual and mixed phospholipid supported lipid bilayers (SLB). DHA incorporation and subsequent changes to the SLBs viscoelastic properties were observed to be concentration-dependent, influenced by the phospholipid species, the headgroup charge, and the presence or absence of calcium ions. It was observed that 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) SLBs incorporated the greatest amount of DHA concentration, whereas the presence of phospholipids, phosphatidylserine (PS), and phosphatidylinositol (PI) in a POPC SLB significantly reduced DHA incorporation and changed the SLBs physicochemical properties. These observations are hypothesized to be due to a substitution event occurring between DHA and phospholipid species. PS domain formation in POPC/PS 8:2 SLBs was observed in the presence of calcium ions, which favored DHA incorporation to a similar level as for a POPC only SLB. The changes in SLB thickness observed with different DHA concentrations are also presented. This work contributes to an understanding of the physical changes induced in a lipid bilayer as a consequence of its exposure to different DHA concentrations (from 50 to 200 μM). The capacity of DHA to influence the physical properties of SLBs indicates the potential for dietary DHA supplementation to cause changes in cellular membranes in vivo, with subsequent physiological consequences for cell function.

Single nucleotide polymorphism (SNP) - genotyping of Community Acquired Methicillin-Resistant Staphylococcus aureus, including the subtyping of PVL toxin producers using Real-Time PCR

Staphylococcus aureus is a common pathogen that causes a variety of infections including soft tissue infections, impetigo, septicemia toxic shock and scalded skin syndrome. Traditionally, Methicillin-Resistant Staphylococcus aureus (MRSA) was considered a Hospital-Acquired (HA) infection. It is now recognised that the frequency of infections with MRSA is increasing in the community, and that these infections are not originating from hospital environments. A 2007 report by the Centers for Disease Control and Prevention (CDC) stated that Staphylococcus aureus is the most important cause of serious and fatal infections in the USA. Community-Acquired MRSA (CA-MRSA) are genetically diverse and distinct, meaning they are able to be identified and tracked by way of genotyping. Genotyping of MRSA using Single nucleotide polymorphisms (SNPs) is a rapid and robust method for monitoring MRSA, specifically ST93 (Queensland Clone) dissemination in the community. It has been shown that a large proportion of CA-MRSA infections in Queensland and New South Wales are caused by ST93. The rationale for this project was that SNP analysis of MLST genes is a rapid and cost-effective method for genotyping and monitoring MRSA dissemination in the community. In this study, 16 different sequence types (ST) were identified with 41% of isolates identified as ST93 making it the predominate clone. Males and Females were infected equally with an average patient age of 45yrs. Phenotypically, all of the ST93 had an identical antimicrobial resistance pattern. They were resistant to the β-lactams – Penicillin, Flu(di)cloxacillin and Cephalothin but sensitive to all other antibiotics tested. Virulence factors play an important role in allowing S. aureus to cause disease by way of colonising, replication and damage to the host. One virulence factor of particular interest is the toxin Panton-Valentine leukocidin (PVL), which is composed of two separate proteins encoded by two adjacent genes. PVL positive CA-MRSA are shown to cause recurrent, chronic or severe skin and soft tissue infections. As a result, it is important that PVL positive CA-MRSA is genotyped and tracked. Especially now that CA-MRSA infections are more prevalent than HA-MRSA infections and are now deemed endemic in Australia. 98% of all isolates in this study tested positive for the PVL toxin gene. This study showed that PVL is present in many different community based ST, not just ST93, which were all PVL positive. With this toxin becoming entrenched in CA-MRSA, genotyping would provide more accurate data and a way of tracking the dissemination. PVL gene can be sub-typed using an allele-specific Real-Time PCR (RT-PCR) followed by High resolution meltanalysis. This allows the identification of PVL subtypes within the CA-MRSA population and allow the tracking of these clones in the community.

Connecting people and resource consumption in real time

The authors currently engage in two projects to improve human-computer interaction (HCI) designs that can help conserve resources. The projects explore motivation and persuasion strategies relevant to ubiquitous computing systems that bring real-time consumption data into the homes and hands of residents in Brisbane, Australia. The first project seeks to increase understanding among university staff of the tangible and negative effects that excessive printing has on the workplace and local environment. The second project seeks to shift attitudes toward domestic energy conservation through software and hardware that monitor real-time, in situ electricity consumption in homes across Queensland. The insights drawn from these projects will help develop resource consumption user archetypes, providing a framework linking people to differing interface design requirements.

Geometry‐specified troposphere decorrelation for subcentimeter real‐time kinematic solutions over long baselines

Real‐time kinematic (RTK) GPS techniques have been extensively developed for applications including surveying, structural monitoring, and machine automation. Limitations of the existing RTK techniques that hinder their applications for geodynamics purposes are twofold: (1) the achievable RTK accuracy is on the level of a few centimeters and the uncertainty of vertical component is 1.5–2 times worse than those of horizontal components and (2) the RTK position uncertainty grows in proportional to the base‐torover distances. The key limiting factor behind the problems is the significant effect of residual tropospheric errors on the positioning solutions, especially on the highly correlated height component. This paper develops the geometry‐specified troposphere decorrelation strategy to achieve the subcentimeter kinematic positioning accuracy in all three components. The key is to set up a relative zenith tropospheric delay (RZTD) parameter to absorb the residual tropospheric effects and to solve the established model as an ill‐posed problem using the regularization method. In order to compute a reasonable regularization parameter to obtain an optimal regularized solution, the covariance matrix of positional parameters estimated without the RZTD parameter, which is characterized by observation geometry, is used to replace the quadratic matrix of their “true” values. As a result, the regularization parameter is adaptively computed with variation of observation geometry. The experiment results show that new method can efficiently alleviate the model’s ill condition and stabilize the solution from a single data epoch. Compared to the results from the conventional least squares method, the new method can improve the longrange RTK solution precision from several centimeters to the subcentimeter in all components. More significantly, the precision of the height component is even higher. Several geosciences applications that require subcentimeter real‐time solutions can largely benefit from the proposed approach, such as monitoring of earthquakes and large dams in real‐time, high‐precision GPS leveling and refinement of the vertical datum. In addition, the high‐resolution RZTD solutions can contribute to effective recovery of tropospheric slant path delays in order to establish a 4‐D troposphere tomography.

Real-time evaluation of driver’s alertness on highways

Suburbanisation has been internationally a major phenomenon in the last decades. Suburb-to-suburb routes are nowadays the most widespread road journeys; and this resulted in an increment of distances travelled, particularly on faster suburban highways. The design of highways tends to over-simplify the driving task and this can result in decreased alertness. Driving behaviour is consequently impaired and drivers are then more likely to be involved in road crashes. This is particularly dangerous on highways where the speed limit is high. While effective countermeasures to this decrement in alertness do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behaviour in real-time. The aim of this study is to evaluate in real-time the level of alertness of the driver through surrogate measures that can be collected from in-vehicle sensors. Slow EEG activity is used as a reference to evaluate driver's alertness. Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). Four different types of highways (driving scenario of 40 minutes each) are implemented through the variation of the road design (amount of curves and hills) and the roadside environment (amount of buildings and traffic). We show with Neural Networks that reduced alertness can be detected in real-time with an accuracy of 92% using lane positioning, steering wheel movement, head rotation, blink frequency, heart rate variability and skin conductance level. Such results show that it is possible to assess driver's alertness with surrogate measures. Such methodology could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring in real-time drivers' behaviour on highways, and therefore it could result in improved road safety.

History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

Determining the detection capabilities of existing real time measurement techniques for different engineered nanoparticles

The application of nanotechnology products has increased significantly in recent years. With their broad range of applications, including electronics, food and agriculture, power and energy, scientific instruments, clothing, cosmetics, buildings, biomedical and health, etc (Catanzariti, 2008), nanomaterials are an indispensible part of human life.

Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

Integrating real time positioning systems to improve blind lifting and loading crane operations

Mobile/tower cranes are the most essential forms of construction plant in use in the construction industry but are also the subject of several safety issues. Of these, blind lifting has been found to be one of the most hazardous of crane operations. To improve the situation, a real-time monitoring system that integrates the use of a Global Positioning System (GPS) and Radio Frequency Identification (RFID) is developed. This system aims to identify unauthorized work or entrance of personnel within a pre-defined risk zone by obtaining positioning data of both site workers and the crane. The system alerts to the presence of unauthorized workers within a risk zone——currently defined as 3m from the crane. When this happens, the system suspends the power of the crane and a warning signal is generated to the safety management team. In this way the system assists the safety management team to manage the safety of hundreds of workers simultaneously. An onsite trial with debriefing interviews is presented to illustrate and validate the system in use.

Locked nucleic acid (LNA) single nucleotide polymorphism (SNP) genotype analysis and validation using real-time PCR

With an increased emphasis on genotyping of single nucleotide polymorphisms (SNPs) in disease association studies, the genotyping platform of choice is constantly evolving. In addition, the development of more specific SNP assays and appropriate genotype validation applications is becoming increasingly critical to elucidate ambiguous genotypes. In this study, we have used SNP specific Locked Nucleic Acid (LNA) hybridization probes on a real-time PCR platform to genotype an association cohort and propose three criteria to address ambiguous genotypes. Based on the kinetic properties of PCR amplification, the three criteria address PCR amplification efficiency, the net fluorescent difference between maximal and minimal fluorescent signals and the beginning of the exponential growth phase of the reaction. Initially observed SNP allelic discrimination curves were confirmed by DNA sequencing (n = 50) and application of our three genotype criteria corroborated both sequencing and observed real-time PCR results. In addition, the tested Caucasian association cohort was in Hardy-Weinberg equilibrium and observed allele frequencies were very similar to two independently tested Caucasian association cohorts for the same tested SNP. We present here a novel approach to effectively determine ambiguous genotypes generated from a real-time PCR platform. Application of our three novel criteria provides an easy to use semi-automated genotype confirmation protocol.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.