Real-time haptic modeling and simulation for prosthetic insertion

In this work a surgical simulator is produced which enables a training otologist to conduct a virtual, real-time prosthetic insertion. The simulator provides the Ear, Nose and Throat surgeon with real-time visual and haptic responses during virtual cochlear implantation into a 3D model of the human Scala Tympani (ST). The parametric model is derived from measured data as published in the literature and accounts for human morphological variance, such as differences in cochlear shape, enabling patient-specific pre- operative assessment. Haptic modeling techniques use real physical data and insertion force measurements, to develop a force model which mimics the physical behavior of an implant as it collides with the ST walls during an insertion. Output force profiles are acquired from the insertion studies conducted in the work, to validate the haptic model. The simulator provides the user with real-time, quantitative insertion force information and associated electrode position as user inserts the virtual implant into the ST model. The information provided by this study may also be of use to implant manufacturers for design enhancements as well as for training specialists in optimal force administration, using the simulator. The paper reports on the methods for anatomical modeling and haptic algorithm development, with focus on simulator design, development, optimization and validation. The techniques may be transferrable to other medical applications that involve prosthetic device insertions where user vision is obstructed

Fall risk assessment in older people using inertial sensors

Abstract of paper that was presented at The 12th National Conference of Emerging Researchers in Ageing

Ambient intelligence through Wireless Ad-hoc Control Networks

The potential of Wireless Ad-hoc Control Networks (WACNets) in realising ambient intelligence in a home environment is explored in this paper. The main objective is to achieve energy and resource efficiency while maintaining optimal comfort. WACNets is a novel concept developed by the Centre for Intelligent Mechatronics Research at the University of Wollongong for the purpose of providing a framework for highly distributed, intelligent wireless control networks. A WACNet consists of intelligent nodes developed on the IEEE 1451 Smart Sensor and ZigBee standards. The WACNet platform is fully selforganizing and employs a mesh of star-topology clusters. The development of an intelligent behaviour- learning control algorithm running on WACNet is described in this paper. The learning algorithm is part of an application which aims at reducing the consumption of resources (such as electricity and water). The main objective of this research project is to embed on a WACNet an algorithm capable of learning the factors influencing the use of resources. The WACNet acts as a platform for a highly distributed implementation of agent-based human behaviour learning algorithm, using fuzzy logic. The concept of WACNet is introduced and the test-bed developed for its study is explained. The suitability of WACNets in creating ambient intelligence in a home environment is addressed. A computer simulation developed to demonstrate the concept of fuzzy learning is presented, along with the results of the first test-bed experiments. Finally, some conclusions are drawn

Application of MML to motor skills acquisition

Study on modeling human psychomotor behaviour based on tracked motion data is reported. The motion data is acquired through various integrated inertial sensors, and represented as Euler angles and accelerations. The Minimum Message Length (MML) algorithm is used to identify frames of intrinsic segmentations and to acquire a classification basis for unsupervised machine learning. The classification model can ultimately be deployed in recognizing certain skilled behaviors. The prior results are analyzed as FSMs\u27 (Finite State Machines) to extract the potential rules underlying behaviors. The progress made so far and plan for further work is reported

Human behaviour recognition with segmented inertial data

The development and recent advancements of integrated inertial sensors has afforded substantive new possibilities for the acquisition and study of complex human motor skills and ultimately their imitation within robotic systems. This paper describes continuing work on kinetic models that are derived through unsupervised learning from a continuous stream of signals, including Euler angles and accelerations in three spatial dimensions, acquired from motions of a human arm. An intrinsic classification algorithm, MML (Minimum Message Length encoding) is used to segment the complex data, formulating a Gaussian Mixture Model of the dynamic modes it represents. Subsequent representation and analysis as FSM (Finite State Machines) has found distinguishing and consistent sequences of modes that persist across both, a variety of tasks as well as multiple candidates. An exemplary “standard” sequence for each behaviour can be abstracted from a corpus of suitable data and in turn utilised together with alignment techniques to identify behaviours of new sequences, as well as detail the homologous extent between each. The progress in contrast to previous work and future objectives are discussed

Nonlinear bilateral teleoperation using extended active observer for force estimation and disturbance suppression

A novel nonlinear teleoperation algorithm for simultaneous inertial parameters and force estimation at the master and slave sides of the teleoperation system is proposed. The scheme, called Extended Active Observer (EAOB), is an extension of the existing active observer. It provides effective force tracking at the master side with accurate position tracking at the slave side in the presence of inertial parameter variation and measurement noise. The proposed method only requires the measurement of robot position, and as a result significantly reduces the difficulty and cost of implementing bilateral teleoperation systems. The approach is described and its stability is analytically verified. The performance of the method is validated through computer simulation and compared with the Nicosia observer-based controller. According to the results, EAOB outperforms the Nicosia observer method and effectively rejects noise

Robust sampled-data control of structures subject to parameter uncertainties and actuator saturation

This paper presents a robust sampled-data controller design approach for vibration attenuation of civil structures considering parameter uncertainties and actuator saturation. The parameter uncertainties belong to polytopic form and are assumed to be the variations of the structural stiffness and damping. Regarding the uncertain sampling problem encountered in real world applications, the sampling period designed for the controller is allowed to be variable within a given bound. In order to obtain reduced peak response quantities, the energy-to-peak performance used to describe the peak values of the control output under all possible energy-bounded disturbances is optimised. The robust sampled-data state feedback controller is obtained in terms of the solvability of certain linear matrix inequalities (LMIs). The applicability of the proposed approach is demonstrated by a numerical example on vibration control of a building structure subject to seismic excitation. It is validated by the simulation results confirming that the designed controllers can effectively attenuate the structural vibration and keep the system stability while there are parameter uncertainties and actuator saturation constraints. (C) 2011 Elsevier Ltd. All rights reserved

Symbolic Modelling of Dynamic Human Motions

Numerous psychological studies have shown that humans develop various stylistic patterns of motion behaviour, or dynamic signatures, which can be in general, or in some cases uniquely, associated with an individual. In a broad sense, such motion features provide a basis for non-verbal communication, or body language, and in more specific circumstances they combine to form a Dynamic Finger Print (DFP) of an individual, such as their gait, or walking pattern. A new modelling and classification approach for spatiotemporal human motions is proposed, and in particular the walking gait. The movements are obtained through a full body inertial motion capture suit, allowing unconstrained freedom of movements in natural environments. This involves a network of 16 miniature inertial sensors distributed around the body via a suit worn by the individual. Each inertial sensor provides (wirelessly) multiple streams of measurements of its spatial orientation, plus energy related: velocity, acceleration, angular velocity and angular acceleration. These are also subsequently transformed and interpreted as features of a dynamic biomechanical model with 23 degrees of freedom (DOF). This scheme provides an unparalleled array of ground-truth information with which to further model dynamic human motions compared to the traditional optically-based motion capture technologies. Using a subset of the available multidimensional features, several successful classification models were developed through a supervised machine learning approach. This chapter describes the approach, methods used together with several successful outcomes demonstrating: plausible DFP models amongst several individuals performing the same tasks, models of common motion tasks performed by several individuals, and finally a model to differentiate abnormal from normal motion behaviour. Future developments are also discussed by extending the range of features to also include the energy related attributes. In doing so, valuable future extensions are also possible in modelling, beyond the objective pose and dynamic motions of a human, to include the intent associated with each motion. This has become a key research area for the perception of motion within video multimedia, for improved Human Computer Interfaces (HCI), as well as its application directions to better animate more realistic behaviours for synthesised avatars

The crisis in ICT education: an academic perspective

A national Discipline-Based Initiative project for ICT, funded by the ALTC, has sought to identify the issues and challenges facing the sector. The crisis in ICT education spans high schools, universities and industry. The demand for skilled ICT graduates is increasing yet enrolments are declining. Several factors contribute to this decline including the perceived quality of teaching and a poor perception of the ICT profession amongst the general public. This paper reports on a consultation process with the academic community. Academic concerns include the capacity of the sector to survive the downturn, and improving relationships with industry which should benefit students, academics and industry. An outcome of the consultation process has been the formation of the Australian Council of Deans of ICT (ACDICT) which will have broad responsibility for addressing the issues affecting ICT higher education

Adaptive stochastic energy flow balancing in smart grid

A smart grid can be considered as an unstructured network of distributed interacting nodes represented by renewable energy sources, storage and loads. The nodes emerge or disappear in a stochastic manner due to the intermittent nature of natural sources such as wind speed and solar irradiation. Prediction and stochastic modelling of electrical energy flow is a critical characteristic in such a network to achieve load balancing and/or peak shaving in order to minimise the fluctuation between off peak and peak demand by power consumers. Before contributing energy to the network, a node acquires information about other nodes in the grid and the state of the grid in order to adjust its power injection to or consumption from the grid. The unpredictable behaviour of nodes in a smart grid is modelled and administered through a scheduling strategy control and learning algorithm using the historical data collected from the system. The stochastic model predicts future power consumption/injection to determine the power required for storage components. In the proposed stochastic model and the deployed learning and adaptation processes, two indicators, based on moving averages of different subsets of the time series are implemented to satisfy two objectives. The first objective is to predict the most efficient state of electrical energy flow between a distribution network and nodes. Whereas the second objective is to minimise the peak demand and off peak consumption of acquiring electrical energy from the main grid by using ant colony search algorithm (ACSA). The performance of the indicators is validated against limited autoregressive integrated moving average (LARIMA) and second order Markov Chain model. It is shown that proposed method outperforms both LARIMA and Markov Chain model