Separation of Doppler radar-based respiratory signatures

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Optimal sensing requirement for slippage prevention in robotic grasping

This paper presents a new theoretical development and modelling related to the requirement of the minimum number of sensors necessary for slippage prevention in robotic grasping. A fundamental experimental investigation has been conducted to support the newly developed postulate. A series of basic experiments proved that it is possible to evaluate the contributions of various sensors to slippage prevention and control in robotic grasping. The use of three discrete physical sensors, one for each of the three sensing functions (normal, tangential and slippage), has been proven to be the most reliable combination for slippage prevention in robotic grasping. It was also proven that the best performance from a two-sensor combination can be achieved when normal grasp force and tangential force are both monitored in the grasping process.

Location based power control for energy critical sensors in a disconnected network

This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.

Ozbot and haptics : remote surveillance to physical presence

This paper reports on robotic and haptic technologies and capabilities developed for the law enforcement and defence community within Australia by the Centre for Intelligent Systems Research (CISR). The OzBot series of small and medium surveillance robots have been designed in Australia and evaluated by law enforcement and defence personnel to determine suitability and ruggedness in a variety of environments. Using custom developed digital electronics and featuring expandable data busses including RS485, I2C, RS232, video and Ethernet, the robots can be directly connected to many off the shelf payloads such as gas sensors, x-ray sources and camera systems including thermal and night vision. Differentiating the OzBot platform from its peers is its ability to be integrated directly with haptic technology or the 'haptic bubble' developed by CISR. Haptic interfaces allow an operator to physically 'feel' remote environments through position-force control and experience realistic force feedback. By adding the capability to remotely grasp an object, feel its weight, texture and other physical properties in real-time from the remote ground control unit, an operator's situational awareness is greatly improved through Haptic augmentation in an environment where remote-system feedback is often limited.

Electrospun granular hollow SnO2 nanofibers hydrogen gas sensors operating at low temperatures

In this paper, we present H2 gas sensors based on hollow and filled, well-aligned electrospun SnO2 nanofibers, operating at a low temperature of 150 C. SnO2 nanofibers with diameters ranging from 80 to 400 nm have been successfully synthesized in which the diameter of the nanofibers can be controlled by adjusting the concentration of polyacrylonitrile in the solution for electrospinning. The presence of this polymer results in the formation of granular walls for the nanofibers. We discussed the correlation between nanofibers morphology, structure, oxygen vacancy contents and the gas sensing performances. X-ray photoelectron spectroscopy analysis revealed that the granular hollow SnO2 nanofibers, which show the highest responses, contain a significant number of oxygen vacancies, which are favorable for gas sensor operating at low temperatures. © 2014 American Chemical Society.

Graphene and graphitic derivative filled polymer composites as potential sensors

Graphite and numerous graphitic-derived micro- and nano-particles have gained importance in current materials science research. These two-dimensional sheets of sp(2)-hybridized carbon atoms remarkably influence the properties of polymers. Graphene mono-layers, graphene oxides, graphite oxides, exfoliated graphite, and other related materials are derived from a parental graphite structure. In this review, we focus primarily on the role of these fillers in regulating the electrical and sensing properties of polymer composites. It has been demonstrated that the addition of an optimized mixture of graphene and or its derivatives to various polymers produces a record-high enhancement of the electrical conductivity and achieved semiconducting characteristics at small filler loading, making it suitable for sensor manufacture. Promising sensing characteristics are observed in graphite-derived composite films compared with those of micro-sized composites and the properties are explained mainly based on the filler volume fraction, nature and rate of dispersion and the filler polymer interactions at the interface. In short, this critical review aims to provide a thorough understanding of the recent advances in the area of graphitic-based polymer composites in advanced electronics. Future perspectives in this rapidly developing field are also discussed.

A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.

An adaptive orientation misalignment calibration method for shoulder movements using inertial sensors : a feasibility study

Qualitative assessment of the progress in physical rehabilitation largely depends on accurate measurement of the range of movements and other kinematic parameters. In clinical practice, wearable inertial sensors have proved to be a potential candidate for such measurements, over the traditional marker based optical systems due to cost and space considerations. The accuracy of wearable sensors have a significant dependence on the initial orientation calibration and the assumption that the sensor will not slip or move with respect to the attached limb. This article introduces a novel calibration algorithm to correct initial orientation misalignment, as well as to track and correct subsequent alignment errors progressively throughout the experiment. The theoretical assertions are validated through controlled experiments with simulated accelerometer and gyroscope measurements.

Building a better mousetrap (Exergame) to increase youth physical activity

Although exergames have been demonstrated to induce moderate levels of physical activity (PA) if played as designed, there is conflicting evidence on use of exergaming leading to increased habitual PA. Exergames have increased PA in some home and school studies, but not others. Exergames have been used in community centers to good effect, but this has not generally been validated with research. PA from exergames may be enhanced by innovative use of sensors, "fun"-enhancing procedures, tailored messaging, message framing, story or narrative, goal setting, feedback, and values-based messaging. Research is needed on PA-enhancing procedures used within exergames for youth to provide a firmer foundation for the design and use of exergames in the future.

High-sensitivity acoustic sensors from nanofibre webs

Considerable interest has been devoted to converting mechanical energy into electricity using polymer nanofibres. In particular, piezoelectric nanofibres produced by electrospinning have shown remarkable mechanical energy-to-electricity conversion ability. However, there is little data for the acoustic-to-electric conversion of electrospun nanofibres. Here we show that electrospun piezoelectric nanofibre webs have a strong acoustic-to-electric conversion ability. Using poly(vinylidene fluoride) as a model polymer and a sensor device that transfers sound directly to the nanofibre layer, we show that the sensor devices can detect low-frequency sound with a sensitivity as high as 266 mV Pa(-1). They can precisely distinguish sound waves in low to middle frequency region. These features make them especially suitable for noise detection. Our nanofibre device has more than five times higher sensitivity than a commercial piezoelectric poly(vinylidene fluoride) film device. Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Human bio-kinematic parameter estimation using inertial sensors.

This thesis focuses on accurately capturing bio-kinematic parameters for physical tele-rehabilitation using measurements from inertial sensors. Contributions are: accurately capturing human kinematics despite intrinsic uncertainties omnipresent with human movements, improving the tracking accuracy by correcting the sensor misalignment and assessing rehabilitation exercises for evaluating the progress of people with disabilities.