Elena Gaura

CONTROL OF DRUG AEROSOL IN HUMAN AIRWAYS USING ELECTROSTATIC FORCES

Abstract A computer model has been developed for analysing the deposition of inhaled electro-aerosols in human airways. The effect of electrostatic charges on the total aerosol deposition efficiency in the human respiratory tract has been investigated. Based on measured data, a computer prediction can be made of the site of deposition in human airways.

Edge Mining the Internet of Things

This paper examines the benefits of edge mining -data mining that takes place on the wireless, battery-powered, and smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage, and remote storage requirements. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: linear Spanish inquisition protocol (L-SIP), ClassAct, and bare necessities (BN), which are all instantiations of general SIP. In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold), and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical because of other overheads, minimization of these overheads can lead up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.

Wireless Sensor Networks: Deployments and Design Frameworks

The twentieth century ended with the vision of smart dust: a network of wirelessly connected devices whose size would match that of a dust particle, each one a se- containedpackageequippedwithsensing,computation,communication,andpower. Smart dust held the promise to bridge the physical and digital worlds in the most unobtrusive manner, blending together realms that were previously considered well separated. Applications involved scattering hundreds, or even thousands, of smart dust devices to monitor various environmental quantities in scenarios ranging from habitat monitoring to disaster management. The devices were envisioned to se- organize to accomplish their task in the most ef?cient way. As such, smart dust would become a powerful tool, assisting the daily activities of scientists and en- neers in a wide range of disparate disciplines. Wireless sensor networks (WSNs), as we know them today, are the most no- worthy attempt at implementing the smart dust vision. In the last decade, this ?eld has seen a fast-growing investment from both academia and industry. Signi?cant ?nancial resources and manpower have gone into making the smart dust vision a reality through WSNs. Yet, we still cannot claim complete success. At present, only specialist computerscientists or computerengineershave the necessary background to walk the road from conception to a ?nal, deployed, and running WSN system.

Smart MEMS and Sensor Systems

Markets and Applications Microfabrication Technologies Sensor Electronics Sensor Signal Enhancement Case Study: Control Systems for Capacitive Inertial Sensors Case Study: Adaptive Optics and Smart VLSI/MEMS Systems Artificial Intelligence Techniques for Microsensors Identification and Compensation Smart, Intelligent and Cogent MEMS Based Sensors Sensor Arrays and Networks Wireless and Ad Hoc Sensor Networks Realising the Dream -- A Case Study.

Postural activity monitoring for increasing safety in bomb disposal missions

In enclosed suits, such as those worn by explosive ordnance disposal (EOD) experts, evaporative cooling through perspiration is less effective and, particularly in hot environments, uncompensable heat stress (UHS) may occur. Although some suits have cooling systems, their effectiveness during missions is dependent on the operatives posture. In order to properly assess thermal state, temperature-based assessment systems need to take posture into account. This paper builds on previous work for instrumenting EOD suits with regard to temperature monitoring and proposes to also monitor operative posture with MEMS accelerometers. Posture is a key factor in predicting how body temperature will change and is therefore important in providing local or remote warning of the onset of UHS. In this work, the C4.5 decision tree algorithm is used to produce an on-line classifier that can differentiate between nine key postures from current acceleration readings. Additional features that summarize how acceleration is changing over time are used to improve average classification accuracy to around 97.2%. Without such temporal feature extraction, dynamic postures are difficult to classify accurately. Experimental results show that training over a variety of subjects, and in particular, mixing gender, improves results on unseen subjects. The main advantages of the on-line posture classification system described here are that it is accurate, does not require integration of acceleration over time, and is computationally lightweight, allowing it to be easily supported on wearable microprocessors.

MuMHR: Multi-path, Multi-hop Hierarchical Routing

This paper proposes a self-organizing, cluster based protocol - multi-path, multi-hop hierarchical routing (MuMHR) - for use in large scale, distributed wireless sensor networks (WSN). With MuMHR, robustness is achieved by each node learning multiple paths and election of cluster-head backup node(s). Energy expenditure is reduced by shortening the distance between the node and its cluster-head and by reducing the setup communication overhead. This is done through incorporating the number-of-hops metric in addition to the back-off waiting time. Simulation results show that MuMHR performs better than LEACH, which is the most promising hierarchical routing algorithm to date; MuMHR reduces the total number of set-up messages by up to 65% and enhances the data delivery ratio by up to 0.83.

Leveraging Knowledge From Physiological Data: On-Body Heat Stress Risk Prediction With Sensor Networks

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1 ± 2.9% and 94.4 ± 2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.

Fall Detection with Wearable Sensors--Safe (Smart Fall Detection)

The high rate of falls incidence among the elderly calls for the development of reliable and robust fall detection systems. A number of such systems have been proposed, with claims of fall detection accuracy of over 90% based on accelerometers and gyroscopes. However, most such fall detection algorithms have been developed based on observational analysis of the data gathered, leading to thresholds setting for fall/non-fall situations. Whilst the fall detection accuracies reported appear to be high, there is little evidence that the threshold based methods proposed generalise well with different subjects and different data gathering strategies or experimental scenarios. Moreover, few attempts appear to have been made to validate the proposed methods in real-life scenarios or to deliver robust fall decisions in real-time. The research here uses machine learning and particularly decision trees to detect 4 types of falls (forward, backward, right and left). When applied to experimental data from 8 male subjects, the accelerometers and gyroscopes based system discriminates between activities of daily living (ADLs) and falls with a precision of 81% and recall of 92%. The performance and robustness of the method proposed has been further analysed in terms its sensitivity to subject physical profile and training set size.

Data set for fall events and daily activities from inertial sensors

Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets. This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.

Increasing Safety of Bomb Disposal Missions: A Body Sensor Network Approach

During manned bomb disposal missions, the combination of the protective suits weight (37 kg), physical activity, high ambient temperatures, and restricted airflow can cause the operatives temperature to rise to dangerous levels during missions, impairing their physical and mental ability. This work proposes to use body sensor networks (BSNs) to increase the safety of operatives in such missions through detailed physiological monitoring, fusion of health information, and remote alerts. Previous trials conducted by the authors have shown no correlation between the suit wearers temperature at any single skin site and their core temperature, nor between single-point temperature variations and subjective thermal sensation. This paper reports on the development of a wearable, wireless, networked sensing system suitable for integration within the suit and deployment in manned missions. A sensor fusion and modeling approach is proposed that estimates the overall thermal sensation of the suit wearer, in real time, based on the multipoint temperature data. Zhangs thermal sensation model was used in this work. Modeling is performed locally to enable cooling system actuation, provide local feedback, and accommodate application specific constraints. Experimentation with the prototype confirms the importance of multisite skin measurement, timely cooling actuation, and monitoring the operatives thermal state. Evaluation of Zhangs model highlights the need for a bespoke model to account for suit and mission specific factors. The deployed BSN has been evaluated through experimental trials using a number of subjects in mission-like conditions and has been shown to be appropriate for the target application.