A smart wireless inertial measurement unit system: simplifying & encouraging usage of WIMU technology

Wireless Inertial Measurement Units (WIMUs) combine motion sensing, processing & communications functionsin a single device. Data gathered using these sensors has the potential to be converted into high quality motion data. By outfitting a subject with multiple WIMUs full motion data can begathered. With a potential cost of ownership several orders of magnitude less than traditional camera based motion capture, WIMU systems have potential to be crucially important in supplementing or replacing traditional motion capture and opening up entirely new application areas and potential markets particularly in the rehabilitative, sports & at-home healthcarespaces. Currently WIMUs are underutilized in these areas. A major barrier to adoption is perceived complexity. Sample rates, sensor types & dynamic sensor ranges may need to be adjusted on multiple axes for each device depending on the scenario. As such we present an advanced WIMU in conjunction with a Smart WIMU system to simplify this aspect with 3 usage modes: Manual, Intelligent and Autonomous. Attendees will be able to compare the 3 different modes and see the effects of good andbad set-ups on the quality of data gathered in real time.

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.

Assessment of the Activity of Photocatalytic Paint Using a Simple Smart Ink Designed for High Activity Surfaces

The use of an acid violet 7 (AV7) smart ink to assess the activity of photocatalytic paint is demonstrated. A linear correlation is established between the change in oxidized dye concentration, as measured by diffuse reflectance, and the change in the green component of the RGB color values, obtained using a portable hand-held scanner, suggesting that such tests can be monitored easily using an inexpensive piece of hand-held office equipment, as opposed to an expensive lab-based instrument, such as a diffuse reflectance UV/vis spectrophotometer. The bleaching of the AV7 follows first order kinetics, at a rate that is linearly dependent upon the UVA irradiance (0.30–3.26 mW cm–2). A comparison of relative rate of bleaching of the AV7 ink with the relative rate of removal of NOx, as determined using the ISO test (ISO 22197-1:2007), established a linear relationship between the two sets of results and the relevance of this correlation is discussed briefly.

A smart ink for the assessment of low activity photocatalytic surfaces

The azo dye, basic blue 66 (BB66) is used in a photocatalyst activity indicator ink (paii) to assess the activity of low activity photocatalytic surfaces, such as commercial photocatalytic tiles and silicone contaminated self-cleaning glass. The BB66 paii is shown to respond much faster than a previously reported, resazurin (Rz) based paii, i.e. the use of a BB66 paii on low activity self-cleaning tiles was found to be >6 times faster than the Rz paii. The BB66 paii is also shown to be effective at assessing the activity of piece of commercial self-cleaning glass contaminated with a coating of silicone, on which the Rz ink, in contrast, failed to show any significant change in colour over the same time period.

Towards a Cyber-physical Resilience Framework for Smart Grids

As modern power grids move towards becoming a smart grid, there is an increasing reliance on the data that is transmitted and processed by ICT systems. This reliance introduces new digital attack vectors. Many of the proposed approaches that aim to address this problem largely focus on applying well-known ICT security solutions. However, what is needed are approaches that meet the complex concerns of the smart grid as a cyber-physical system. Furthermore, to support the automatic control loops that exist in a power grid, similarly automatic security and resilience mechanisms are needed that rely on minimal operator intervention. The research proposed in this paper aims to develop a framework that ensures resilient smart grid operation in light of successful cyber-attacks.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Layered smart grid architecture approach and field tests by ZigBee technology

This paper presents a layered Smart Grid architecture enhancing security and reliability, having the ability to act in order to maintain and correct infrastructure components without affecting the client service. The architecture presented is based in the core of well design software engineering, standing upon standards developed over the years. The layered Smart Grid offers a base tool to ease new standards and energy policies implementation. The ZigBee technology implementation test methodology for the Smart Grid is presented, and provides field tests using ZigBee technology to control the new Smart Grid architecture approach. (C) 2014 Elsevier Ltd. All rights reserved.

Smart Plastic Antibody Material (SPAM) tailored on disposable screen printed electrodes for protein recognition: application to Myoglobin detection

This work introduces two major changes to the conventional protocol for designing plastic antibodies: (i) the imprinted sites were created with charged monomers while the surrounding environment was tailored using neutral material; and (ii) the protein was removed from its imprinted site by means of a protease, aiming at preserving the polymeric network of the plastic antibody. To our knowledge, these approaches were never presented before and the resulting material was named here as smart plastic antibody material (SPAM). As proof of concept, SPAM was tailored on top of disposable gold-screen printed electrodes (Au-SPE), following a bottom-up approach, for targeting myoglobin (Myo) in a point-of-care context. The existence of imprinted sites was checked by comparing a SPAM modified surface to a negative control, consisting of similar material where the template was omitted from the procedure and called non-imprinted materials (NIMs). All stages of the creation of the SPAM and NIM on the Au layer were followed by both electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). AFM imaging was also performed to characterize the topography of the surface. There are two major reasons supporting the fact that plastic antibodies were effectively designed by the above approach: (i) they were visualized for the first time by AFM, being present only in the SPAM network; and (ii) only the SPAM material was able to rebind to the target protein and produce a linear electrical response against EIS and square wave voltammetry (SWV) assays, with NIMs showing a similar-to-random behavior. The SPAM/Au-SPE devices displayed linear responses to Myo in EIS and SWV assays down to 3.5 μg/mL and 0.58 μg/mL, respectively, with detection limits of 1.5 and 0.28 μg/mL. SPAM materials also showed negligible interference from troponin T (TnT), bovine serum albumin (BSA) and urea under SWV assays, showing promising results for point-of-care applications when applied to spiked biological fluids.