A simulation-based control interface layer for a high-fidelity anthropomorphic training simulator

Deakin University’s futuristic Universal Motion simulator will overcome the limitations of current motion simulator platforms by employing an anthropomorphic robot arm to provide the motion fidelity necessary to exploit the potential of modern simulation environments. Full motion simulators frequently utilize Stewart platforms to mimic the movement of vehicles during simulation. However, due to the limited motion range and dexterity of such systems, and their inability to convey realistic accelerations, they are unable to represent accurate motion characteristics. The Universal Motion Simulation aims to close the gap between the limitations of the current motion technology and real world, by introducing a flexible, modular, high-fidelity motion system that can be used for a variety of immersive training applications. The modular nature of the design allows interchangeable and configurable simulation pods to be attached to the end effectors.

Gymnasium-based unsupervised exercise maintains benefits in oxygen uptake kinetics obtained following supervised training in type 2 diabetes

Supervised exercise (SE) in patients with type 2 diabetes improves oxygen uptake kinetics at the onset of exercise. Maintenance of these improvements, however, has not been examined when supervision is removed. We explored if potential improvements in oxygen uptake kinetics following a 12-week SE that combined aerobic and resistance training were maintained after a subsequent 12-week unsupervised exercise (UE). The involvement of cardiac output (CO) in these improvements was also tested. Nineteen volunteers with type 2 diabetes were recruited. Oxygen uptake kinetics and CO (inert gas rebreathing) responses to constant-load cycling at 50% ventilatory threshold (V_T), 80% V_T, and mid-point between V_T and peak workload (50% Δ) were examined at baseline (on 2 occasions) and following each 12-week training period. Participants decided to exercise at a local gymnasium during the UE. Thirteen subjects completed all the interventions. The time constant of phase 2 of oxygen uptake was significantly faster (p < 0.05) post-SE and post-UE compared with baseline at 50% V_T (17.3 ± 10.7 s and 17.5 ± 5.9 s vs. 29.9 ± 10.7 s), 80% V_T (18.9 ± 4.7 and 20.9 ± 8.4 vs. 34.3 ± 12.7s), and 50% Δ (20.4 ± 8.2 s and 20.2 ± 6.0 s vs. 27.6 ± 3.7 s). SE also induced faster heart rate kinetics at all 3 intensities and a larger increase in CO at 30 s in relation to 240 s at 80% V_T; and these responses were maintained post-UE. Unsupervised exercise maintained benefits in oxygen uptake kinetics obtained during a supervised exercise in subjects with diabetes, and these benefits were associated with a faster dynamic response of heart rate after training.

Training improves interobserver reliability for the diagnosis of scaphoid fracture displacement

Background The diagnosis of displacement in scaphoid fractures is notorious for poor interobserver reliability.

Questions/purposes We tested whether training can improve interobserver reliability and sensitivity, specificity, and accuracy for the diagnosis of scaphoid fracture displacement on radiographs and CT scans.

Methods Sixty-four orthopaedic surgeons rated a set of radiographs and CT scans of 10 displaced and 10 nondisplaced scaphoid fractures for the presence of displacement, using a web-based rating application. Before rating, observers were randomized to a training group (34 observers) and a nontraining group (30 observers). The training group received an online training module before the rating session, and the nontraining group did not. Interobserver reliability for training and nontraining was assessed by Siegel’s multirater kappa and the Z-test was used to test for significance.

Results There was a small, but significant difference in the interobserver reliability for displacement ratings in favor of the training group compared with the nontraining group. Ratings of radiographs and CT scans combined resulted in moderate agreement for both groups. The average sensitivity, specificity, and accuracy of diagnosing displacement of scaphoid fractures were, respectively, 83%, 85%, and 84% for the nontraining group and 87%, 86%, and 87% for the training group. Assuming a 5% prevalence of fracture displacement, the positive predictive value was 0.23 in the nontraining group and 0.25 in the training group. The negative predictive value was 0.99 in both groups.

Conclusions Our results suggest training can improve interobserver reliability and sensitivity, specificity and accuracy for the diagnosis of scaphoid fracture displacement, but the improvements are slight. These findings are encouraging for future research regarding interobserver variation and how to reduce it further.

Optometry training simulation with augmented reality and haptics

Optometry is an essential health care profession that has existed for many centuries and is still evolving. However, the training approaches for optometrists are not yet on par with the latest technological evolution. The traditional supervisor-student training mode could not provide good immersion and repeatability, while most existing vision-based computer-assisted simulations provide even worse immersion on screens. In this paper, we propose an effective system for optometry training simulation with two major components: augmented reality and haptics. These components are integrated with the actual slit lamp and are able to greatly enhance the immersion for typical optometry training tasks such as foreign body removal. Medical doctors are also involved in suggesting configurations and validating visual and haptic rendering results. Preliminary user studies show very positive feedbacks from optometry students.

A mathematical model of neuromuscular adaptation to resistance training and its application in a computer simulation of accommodating loads

A large corpus of data obtained by means of empirical study of neuromuscular adaptation is currently of limited use to athletes and their coaches. One of the reasons lies in the unclear direct practical utility of many individual trials. This paper introduces a mathematical model of adaptation to resistance training, which derives its elements from physiological fundamentals on the one side, and empirical findings on the other. The key element of the proposed model is what is here termed the athlete’s capability profile. This is a generalization of length and velocity dependent force production characteristics of individual muscles, to an exercise with arbitrary biomechanics. The capability profile, a two-dimensional function over the capability plane, plays the central role in the proposed model of the training-adaptation feedback loop. Together with a dynamic model of resistance the capability profile is used in the model’s predictive stage when exercise performance is simulated using a numerical approximation of differential equations of motion. Simulation results are used to infer the adaptational stimulus, which manifests itself through a fed back modification of the capability profile. It is shown how empirical evidence of exercise specificity can be formulated mathematically and integrated in this framework. A detailed description of the proposed model is followed by examples of its application—new insights into the effects of accommodating loading for powerlifting are demonstrated. This is followed by a discussion of the limitations of the proposed model and an overview of avenues for future work.

A hierarchical PCA-based anomaly detection model

A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.

O where are you going? O do you imagine? Reproduction and response : a reflexive sociology of scholarship of teaching and learning in practical legal training

My doctoral research studies Australian PLT practitioners’ engagement with scholarship of teaching and learning. I argue that many PLT practitioners are motivated to engage with scholarship of teaching and learning in their work. There are, however, individual and extra-individual impediments.
PLT practitioners are lawyers that teach in institutional practical legal training (“PLT”). Satisfactory completion of mandatory PLT is an eligibility requirement for admission to the Australian legal profession. The PLT requirement is additional to academic legal qualifications. PLT is undertaken at a post-graduate level with, or after, the academic law degree.
My study investigates PLT practitioners’ motivations and capabilities to engage with scholarship of teaching and learning (“SoTL”). I study organisational symbolic support for SoTL in PLT, and organisational allocation of resources to SoTL in PLT.
The study involves individual and extra-individual domains of PLT practitioners’ work. It considers how social structures (e.g. “the juridical”) are inscribed into individuals’ practices (“teaching”) and, conversely, whether practices influence social structures.
My research adopts qualitative methodologies. These involve inter-disciplinary exchanges between law, legal education, practice research, sociology of law, cultural theory, and theory and practice of teaching and learning. My theoretical framework draws on Pierre Bourdieu’s “reflexive sociology”, and Michel de Certeau’s “heterological science”.
I sourced data from documents, and semi-structured interviews with 36 Australian PLT practitioners. Documentary sources include statutory instruments, speeches, reports, practice directions, histories, and scholarly publications.
To analyse the data I adopted Kelle’s characterisation of “theoretical sensitivity”, drawing on “explicit” and “emergent” analysis strategies derived from “grounded theory”. The explicit strategies were based on my theoretical framework. The emergent strategy involved sensitivity to non-explicit concepts and theories that emerged from the data. Computer-aided qualitative data analysis software expedited these methods.
My findings to date question dominant legal structures’ readiness for change, the implications of this for teaching and learning in PLT, and in particular for PLT practitioners’ engagement with SoTL in PLT.
The espoused rationale for mandatory PLT (in statutes) is improvement for the protection of clients, the administration of justice, and to assure quality legal services. The tacit rationale is improved quality of legal education, and experiences, for lawyers-to-be. My thesis argues dominant structures in legal education impede the espoused and tacit objectives, and impede PLT practitioners’ engagement with scholarship of teaching and learning.

Application of GIS-based computer modelling to planning for adaption to climate change in rural areas

A GIS-based computer modelling methodology was developed and applied to identify climate change adaptation issues arising in regional agricultural production systems (including forestry). Agricultural production in Australia is very susceptible to the adverse impacts of climate change due to projected shifts in rainfall and temperature. The methodology integrates land suitability analysis with uncertainty analysis and spatial (regional) optimisation to determine optimal agricultural land use at a regional scale for current and possible future climatic conditions. The approach can be used to recognise regions under threat of productivity decline, identify alternative cropping systems that may be better adapted to likely future conditions, and investigate implementation actions to improve the sub-optimal situations created by climate change. An example of how the methodology may be used is outlined through a case study involving the South West Region of Victoria, Australia. The case study provides information on the tools available to support the formulation of a regional adaptation strategy.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

sEMG-based single-joint active training with iLeg—A horizontal exoskeleton for lower limb rehabilitation

In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the singlejoint voluntary motion intention, based on which, two single-joint active training strategies are proposed with iLeg, a horizontal exoskeleton for lower limb rehabilitation newly developed at our laboratory. In damping active training, the joint angular velocity is proportionally controlled by the voluntary effort derived from sEMG, performing as an ideal damper, while spring active training aims to create a spring-like environment where the joint angular displacement from the constant reference is proportionally controlled by the voluntary effort. Experiments are conducted with iLeg and one healthy male subject to validate the feasibility of the two single-joint active training strategies.