Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Power buffer with model predictive control for stability of vehicular power systems with constant power loads

Electrification of vehicular systems has gained increased momentum in recent years with particular attention to constant power loads (CPLs). Since a CPL potentially threatens system stability, stability analysis of hybrid electric vehicle with CPLs becomes necessary. A new power buffer configuration with battery is introduced to mitigate the effect of instability caused by CPLs. Model predictive control (MPC) is applied to regulate the power buffer to decouple source and load dynamics. Moreover, MPC provides an optimal tradeoff between modification of load impedance, variation of dc-link voltage and battery current ripples. This is particularly important during transients or starting of system faults, since battery response is not very fast. Optimal tradeoff becomes even more significant when considering low-cost power buffer without battery. This paper analyzes system models for both voltage swell and voltage dip faults. Furthermore, a dual mode MPC algorithm is implemented in real time offering improved stability. A comprehensive set of experimental results is included to verify the efficacy of the proposed power buffer.

Does usability engineering matters for STEM education?

Technological maturity and the exponential growth of digital applications are contributing to lifestyle changes worldwide. Consequently, learning and teaching is demanding more effective sociotechnical interactions involving emerging technologies, as opposed to traditional, conventional face-to-face learning and teaching approaches. In this context, usability engineering is making significant contributions for improving computer and distance-based learning, both for learners and instructors, which have often been ignored when designing online learning and teaching applications. Usability testing is a central part of the human centered learning approach for developing sustainable STEM education from the socio-technological perspective. Our experiences with usability engineering and the impact of teaching low-cost rapid usability testing methods on knowledge translation from undergraduate to graduate courses to real-world practice (i.e. getting the methods out there in real use) are diverse and multi-modal. Our sample space has been hundreds of trained students who have learned how to do effective usability engineering in real-world situations at higher levels of realism (i.e. fidelity) and at a much lower cost than using traditional fixed usability labs. Furthermore, this low-cost rapid approach to usability engineering has been adopted by many of our graduates who are now managers, CIOs etc and who are using the methods routinely in their organizations in real world applications and scenarios. This knowledge has been used to improve design and implementation of a wide range of applications, including applications designed for teaching and learning.

Accelerometer based performance assessment of basic routines in classical ballet

Classical ballet requires dancers to exercise significant muscle control and strength both while stationary and when moving. Following the Royal Academy of Dance (RAD) syllabus, 8 male and 27 female dancers (aged 20.2 + 1.9 yr) in a fulltime university undergraduate dance training program were asked to stand in first position for 10 seconds and then perform 10 repeats of a demi-plié exercise to a counted rhythm. Accelerometer records from the wrist, sacrum, knee and ankle were compared with the numerical scores from a professional dance instructor. The sacrum mounted sensor detected lateral tilts of the torso in dances with lower scores (Spearman’s rank correlation coefficient r = -0.64, p < 0.005). The RMS acceleration amplitude of wrist mounted sensor was linearly correlated to the movement scores (Spearman’s rank correlation coefficient r = 0.63, p < 0.005). The application of sacrum and wrist mounted sensors for biofeedback during dance training is a realistic, low cost option.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Automatic inference of driving task demand from visual cues of emotion and attention

Sensing the mental, physical and emotional demand of a driving task is of primary importance in road safety research and for effectively designing in-vehicle information systems (IVIS). Particularly, the need of cars capable of sensing and reacting to the emotional state of the driver has been repeatedly advocated in the literature. Algorithms and sensors to identify patterns of human behavior, such as gestures, speech, eye gaze and facial expression, are becoming available by using low cost hardware: This paper presents a new system which uses surrogate measures such as facial expression (emotion) and head pose and movements (intention) to infer task difficulty in a driving situation. 11 drivers were recruited and observed in a simulated driving task that involved several pre-programmed events aimed at eliciting emotive reactions, such as being stuck behind slower vehicles, intersections and roundabouts, and potentially dangerous situations. The resulting system, combining face expressions and head pose classification, is capable of recognizing dangerous events (such as crashes and near misses) and stressful situations (e.g. intersections and way giving) that occur during the simulated drive.

Monitoring spatiotemporal dynamics of human movement based on MAC address data

This thesis was a step forward in extracting valuable features from human's movement behaviour in terms of space utilisation based on Media-Access-Control data. This research offered a low-cost and less computational complexity approach compared to existing human's movement tracking methods. This research was successfully applied in QUT's Gardens Point campus and can be scaled to bigger environments and societies. Extractable information from human's movement by this approach can add a significant value to studying human's movement behaviour, enhancing future urban and interior design, improving crowd safety and evacuation plans.

Systematic integration of human factors in the specification of requirements for new railway technologies

Engineering design processes are necessary to attain the requisite standards of integrity for high-assurance safety-related systems. Additionally, human factors design initiatives can provide critical insights that parameterise their development. Unfortunately, the popular perception of human factors as a “forced marriage” between engineering and psychology often provokes views where the ‘human factor’ is perceived as a threat to systems design. Some popular performance-based standards for developing safety-related systems advocate identifying and managing human factors throughout the system lifecycle. However, they also have a tendency to fall short in their guidance on the application of human factors methods and tools, let alone how the outputs generated can be integrated in to various stages of the design process. This case study describes a project that converged engineering with human factors to develop a safety argument for new low-cost railway level crossing technology for system-wide implementation in Australia. The paper enjoins the perspectives of a software engineer and cognitive psychologist and their involvement in the project over two years of collaborative work to develop a safety argument for low-cost level crossing technology. Safety and reliability requirements were informed by applying human factors analytical tools that supported the evaluation and quantification of human reliability where users interfaced with the technology. The project team was confronted with significant challenges in cross-disciplinary engagement, particularly with the complexities of dealing with incongruences in disciplinary language. They were also encouraged to think ‘outside the box’ as to how users of a system interpreted system states and ehaviour. Importantly, some of these states, while considered safe within the boundary of the constituent systems that implemented safety-related functions, could actually lead the users to engage in deviant behaviour. Psychology explained how user compliance could be eroded to levels that effectively undermined levels of risk reduction afforded by systems. Linking the engineering and psychology disciplines intuitively, overall safety performance was improved by introducing technical requirements and making design decisions that minimized the system states and behaviours that led to user deviancy. As a commentary on the utility of transdisciplinary collaboration for technical specification, the processes used to bridge the two disciplines are conceptualised in a graphical model.

In-vehicle railway level crossing warning systems : can intelligent transport systems deliver?

Intelligent Transport System (ITS) technology is seen as a cost-effective way to increase the conspicuity of approaching trains and the effectiveness of train warnings at level crossings by providing an in-vehicle warning of an approaching train. The technology is often seen as a potential low-cost alternative to upgrading passive level crossings with traditional active warning systems (flashing lights and boom barriers). ITS platforms provide sensor, localization and dedicated short-range communication (DSRC) technologies to support cooperative applications such as collision avoidance for road vehicles. In recent years, in-vehicle warning systems based on ITS technology have been trialed at numerous locations around Australia, at level crossing sites with active and passive controls. While significant research has been conducted on the benefits of the technology in nominal operating modes, little research has focused on the effects of the failure modes, the human factors implications of unreliable warnings and the technology adoption process from the railway industry’s perspective. Many ITS technology suppliers originate from the road industry and often have limited awareness of the safety assurance requirements, operational requirements and legal obligations of railway operators. This paper aims to raise awareness of these issues and start a discussion on how such technology could be adopted. This paper will describe several ITS implementation cenarios and discuss failure modes, human factors considerations and the impact these scenarios are likely to have in terms of safety, railway safety assurance requirements and the practicability of meeting these requirements. The paper will identify the key obstacles impeding the adoption of ITS systems for the different implementation scenarios and a possible path forward towards the adoption of ITS technology.

Precise relative clock synchronization for distributed control using TSC registers

Precise clock synchronization is essential in emerging time-critical distributed control systems operating over computer networks where the clock synchronization requirements are mostly focused on relative clock synchronization and high synchronization precision. Existing clock synchronization techniques such as the Network Time Protocol (NTP) and the IEEE 1588 standard can be difficult to apply to such systems because of the highly precise hardware clocks required, due to network congestion caused by a high frequency of synchronization message transmissions, and high overheads. In response, we present a Time Stamp Counter based precise Relative Clock Synchronization Protocol (TSC-RCSP) for distributed control applications operating over local-area networks (LANs). In our protocol a software clock based on the TSC register, counting CPU cycles, is adopted in the time clients and server. TSC-based clocks offer clients a precise, stable and low-cost clock synchronization solution. Experimental results show that clock precision in the order of 10~microseconds can be achieved in small-scale LAN systems. Such clock precision is much higher than that of a processor's Time-Of-Day clock, and is easily sufficient for most distributed real-time control applications over LANs.

Atmospheric-microplasma-assisted nanofabrication : metal and metal–oxide nanostructures and nanoarchitectures

In this paper, we report on the fabrication of Mo-oxide nanostructures and nanoarchitectures using an atmospheric-microplasma (AMP) system. This AMP system shows a high degree of flexibility and is capable of producing several different nanostructures and nanoarchitectures by varying the process parameters. The low-cost and simplicity of the process are important characteristics for nanomanufacturing, and AMPs offer such advantages. In addition, AMPs have shown the ability of promoting self-organization of nanostructures. © 2009 IEEE.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

High-yield atmospheric-pressure CVD of highly-uniform carbon nanocoils using Co–P catalyst nanoparticles prepared by electroless plating

A simple, fast and low-cost atmospheric-pressure chemical vapor deposition technique is developed to synthesize high-yield carbon nanocoils (CNCs) using amorphous Co–P alloy as catalyst and thiophene as nucleation agent. The uniform catalyst pattern with the mean particle size of 350 nm was synthesized using a simple electroless plating process. This uniformity of the Co–P nanoparticles results in a high yield, very uniform size/shape distribution and regular structure of CNCs at the optimum growth temperature of 800 ◦C. The yield of CNCs reaches ∼76%; 70% of the CNCs have fiber diameters approximately 250 nm. The CNC coil diameters and lengths are 450–550nm and 0.5–2mm, respectively. The CNC nucleation and growth mechanism are also discussed.

Colloidal semiconductor nanocrystals : controlled synthesis and surface chemistry in organic media

Colloidal semiconductor nanocrystals (CS-NCs) possess compelling benefits of low-cost, large-scale solution processing, and tunable optoelectronic properties through controlled synthesis and surface chemistry engineering. These merits make them promising candidates for a variety of applications. This review focuses on the general strategies and recent developments of the controlled synthesis of CS-NCs in terms of crystalline structure, particle size, dominant exposed facet, and their surface passivation. Highlighted are the organic-media based synthesis of metal chalcogenide (including cadmium, lead, and copper chalcogenide) and metal oxide (including titanium oxide and zinc oxide) nanocrystals. Current challenges and thus future opportunities are also pointed out in this review.

Solution processable poly(2,5-dialkyl-2,5-dihydro-3,6-di-2-thienyl- pyrrolo[3,4-c]pyrrole-1,4-dione) for ambipolar organic thin film transistors

Solution processable diketopyrrolopyrrole (DPP)-bithiophene polymers (PDBT) with long branched alkyl side chains on the DPP unit are synthesized. These polymers have favourable highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels for the injection and transport of both holes and electrons. Organic thin film transistors (OTFTs) using these polymers as semiconductors and gold as source/drain electrodes show typical ambipolar characteristics with very well balanced high hole and electron mobilities (μ h = 0.024 cm 2 V -1 s -1 and μ e = 0.056 cm 2 V -1 s -1). These simple and high-performing polymers are promising materials for ambipolar organic thin film transistors for low-cost CMOS-like logic circuits.