The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods. © 2013 IEEE.
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This paper describes the approach used to identify elderly people’s needs and attitudes towards applying ambient sensor systems for monitoring daily activities in the home. As elderly are typically unfamiliar with such ambient technology, interactive tools for explicating sensor monitoring –an interactive dollhouse and iPad applications for displaying live monitored sensor activity data– were developed and used for this study. Furthermore, four studies conducted by occupational therapists with more than 60 elderly participants –including questionnaires (n=41), interviews (n=6), user sessions (n=14) and field studies (n=2)– were conducted. The experiences from these studies suggest that this approach helped to democratically engage the elderly as end-user and identify acceptance issues.
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Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE.
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Sensor systems can be deployed in the homes of older adults living alone for functional health assessments. Their information is very useful for health care specialists. The problem lies in developing person independent models while facing a large variability in behavior. We address this problem by, first, proposing a new feature extraction method for data from ambient motion sensors. The method uses functional similarities between houses and daily structure to extract meaningful features. Second, we propose a change-based approach for analyzing data, taking difference scores of both the sensor features and health metrics. To evaluate our approach, experiments on longitudinal data were conducted, where the relationship between sensor data and health measurements was modeled with linear regression and (nonlinear) regression forests. These experiments show that the change-based approach yields better results and that the resulting models can be used as a reliable metric for (functional) health. In addition, feature analysis can help health care specialists understand relevant aspects of behavior. Prediction of health metrics is possible even with simple sensors. With such sensors, it is possible to detect problems and health decline in an early stage. This will have great impact on clinical practice.
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A novel type of application for the exploration of enclosed or otherwise difficult to access environments requires large quantities of miniaturized sensor nodes to perform measurements while they traverse the environment in a “go with the flow” approach. Examples of these are the exploration of underground cavities and the inspection of industrial pipelines or mixing tanks, all of which have in common that the environments are difficult to access and do not allow position determination using e.g. GPS or similar techniques. The sensor nodes need to be scaled down towards the millimetre range in order to physically fit through the narrowest of parts in the environments and should measure distances between each other in order to enable the reconstruction of their positions relative to each other in offline analysis. Reaching those levels of miniaturization and enabling reconstruction functionality requires: 1) novel reconstruction algorithms that can deal with the specific measurement limitations and imperfections of millimetre-sized nodes, and 2) improved understanding of the relation between the highly constraint hardware design space of the sensor nodes and the reconstruction algorithms. To this end, this work provides a novel and highly robust sensor swarm reconstruction algorithm and studies the effect of hardware design trade-offs on its performance. Our findings based on extensive simulations, which push the reconstruction algorithm to its breaking point, provide important guidelines for the future development of millimetre-sized sensor nodes.
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Background: Intravenous (IV) therapy using short peripheral IV catheters (PIVC) is commonplace with neonatal patients. However, this therapy is associated with high complication rates including the leakage of infused fluids from the vasculature into the surrounding tissues; a condition referred to as, peripheral IV infiltration/extravasation (PIVIE). Objective: The quality improvement project aimed to identify the prevalence of known risk factors for PIVIE in the neonatal intensive care unit (NICU) and explore the feasibility of using novel optical sensor technology to aid in earlier detection of PIVIE events. Methods: The plan, do, study, act (PDSA) model of quality improvement (QI) was used to provide a systematic framework to identify PIVIE risks and evaluate the potential utility of continuous PIVC monitoring using the ivWatch model 400® system. The site was provided with eight monitoring systems and consumables. Hospital staff were supported with theoretical education and bedside training about the system operations and best use practices. Results: In total 113 PIVIE's (graded II-IV) were recorded from 3476 PIVCs, representing an incidence of 3.25%. Lower birth weight and gestational age were statistically significant factors for increased risk of PIVIE (p = 0.004); all other known risk factors did not reach statistical significance. Piloting the ivWatch with 21 PIVCs using high-risk vesicant solutions over a total of 523.9 h (21.83 days) detected 11 PIVIEs (graded I-II). System sensitivity reached 100%; 11 out of 11 PIVIEs were detected by the ivWatch before clinician confirmation. Conclusions: Prevailing risk factors for PIVIE in the unit were comparable to those published. Continuous infusion site monitoring using the ivWatch suggests this technology offers the potential to detect PIVIE events earlier than relying on intermittent observation alone (i.e. the current standard of care). However, large-scale study with neonatal populations is required to ensure the technology is optimally configured to meet their needs.
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The creation of artifacts is one of the factors that make us human. Artifacts contribute to our continual adaptation to the world by permitting better knowledge and control of it. The focus of this chapter is on the role of one specific kind of artifact: sensors. In contrast to our immediate perception of the world from our senses, sensors provide large amount of reliable measurements of the physical world that enhance human cognitive capacities in overcoming our perceptual limitations. However, “raw” sensor data require interpretation that relies on different types of expertise and knowledge to provide relevant meaning for human (adaptive) purposes. We suggest that a cognitive approach to understanding the differences between the different types of knowledge provided by current sensors as artifacts and the human senses is of interest. This approach questions the conception of human cognition as an analytic system of processing information from the world rather than as one which interprets and gives meanings to the world. We posit that understanding the differences between human and artificial sensors can shape a new era of technological advancement that is uniquely collaborative insofar as it would rely on the partnership of scientists working in the Humanities and in the Natural Sciences. In this article we provide some data from cognitive research that outline the beginnings of a pluridisciplinary endeavor to conceive sensors which integrate performances of artifacts and the diversity and richness of human cognition, with the goal of transforming so-called “intelligent” devices into cognitive sensors.
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Wireless sensor networks are becoming popular in the field of ambient assisted living. In this paper we report our study on the relationship between a functional health metric and features derived from the sensor data. Sensor systems are installed in the houses of nine people who are also quarterly visited by an occupational therapist for functional health assessments. Different features are extracted and these are correlated with a metric of functional health (the AMPS). Though the sample is small, the results indicate that some features are better in describing the functional health in the population, but individual differences should also be taken into account when developing a sensor system for functional health assessment.
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The 5th generation of mobile communications is designed to employ both FR1 and FR2 bands throughout the world. The higher frequency bands (i.e., FR2 n257 26.50 - 29.50 GHz) are posing several challenges to operators and national telecom agencies for performing electromagnetic fields (EMF) measurements. In this work we present the design and preliminary evaluation of an FR2 sensor node to measure EMF radiations in urban environments. The design is carried out in an RF circuit design software, i.e., Keysight ADS, where the various nonidealities (i.e., nonlinearities, noise behavior and electromagnetic response) of the various sub blocks of the systems are accounted for. The sensor concept is then implemented in a prototype board technology (i.e., X-microwave) and its response is experimentally verified in the FR2 band.
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In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1◦/s for wheelchair angular velocity when compared with the reference system. The twoIMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0◦/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available.
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