Investigation of reducing fatigue and musculoskeletal disorder with passive actuators

Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice. ©2010 IEEE

Upper body pose estimation utilizing kinematic constraints from physical human-robot interaction

In physical Human-Robot Interaction (pHRI), knowing the pose of the operator is beneficial and may allow the robot to better accommodate the human operator. Due to a large redundancy in the human body, determining the pose of the human operator is difficult to achieve in unstructured environments especially in human-robot collaborative operations where the robot often occludes the human from vision-based sensors. This work presents an upper body pose estimation method based on exploiting known positions of the human operator's hands while performing a task with the robot. Upper body pose is estimated using upper limb kinematic models alongside sensor information and model approximations to produce solutions that are biomechanically feasible. The pose estimation method was compared to upper body poses obtained using a motion capture system. It was shown to be able to perform robustly with varying amounts of available information. This approach is well suited in applications where robots are controlled using well-defined interfaces such as handlebars, operating in unstructured environments

Angled sensor configuration capable of measuring tri-axial forces for pHRI

© 2016 IEEE. This paper presents a new configuration for single axis tactile sensor arrays molded in rubber to enable tri-axial force measurement. The configuration requires the sensing axis of each sensor in the array to be rotated out of alignment with respect to external forces. This angled sensor array measures shear forces along axes in a way that is different to a planar sensor array. Three sensors using the angled configuration (22.5°, 45° and 67.5°) and a fourth sensor using the planar configuration (0°) have been fabricated for experimental comparison. Artificial neural networks were trained to interpret the external force applied along each axis (X, Y and Z) from raw pressure sensor values. The results show that the angled sensor configuration is capable of measuring tri-axial external forces with a root mean squared error of 1.79N, less error in comparison to the equivalent sensor utilizing the planar configuration (4.52N). The sensors are then implemented to control a robotic arm. Preliminary findings show angled sensor arrays to be a viable alternative to planar sensor arrays for shear force measurement; this has wide applications in physical Human Robot Interaction (pHRI)

Infrastructure robotics: Research challenges and opportunities

Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney

The Freezeout Hypersurface at LHC from particle spectra: Flavor and Centrality Dependence

We extract the freezeout hypersurface in Pb-Pb collisions at $\sqrt{s_{\rm NN}}=$ 2760 GeV at the CERN Large Hadron Collider by analysing the data on transverse momentum spectra within a unified model for chemical and kinetic freezeout. The study has been done within two different schemes of freezeout, single freezeout where all the hadrons freezeout together versus double freezeout where those hadrons with non-zero strangeness content have different freezeout parameters compared to the non-strange ones. We demonstrate that the data is better described within the latter scenario. We obtain a strange freezeout hypersurface which is smaller in volume and hotter compared to the non-strange freezeout hypersurface for all centralities with a reduction in $\chi^2/N_{df}$ around $40\%$. We observe from the extracted parameters that the ratio of the transverse size to the freezeout proper time is invariant under expansion from the strange to the non-strange freezeout surfaces across all centralities. Moreover, except for the most peripheral bins, the ratio of the non-strange and strange freezeout proper times is close to $1.3$.Comment: Final version accepted for publicatio

Sex differences in sepsis hospitalisations and outcomes in older women and men: A prospective cohort study

Purpose: To examine the association of sex with hospitalisation due to sepsis and related outcomes. Methods: Prospective cohort study of 264,678 adults, average age 62.7 years at recruitment (2006–2009) in Australia. Participants were followed for sepsis hospitalisation identified using the International Classification of Diseases coding. Outcomes included sex differences in the risk of an incident sepsis hospitalisation, mortality, length of ICU and hospital stay and readmissions during the following year. Results: Over 2,070,343 years of follow-up there were 12,912 sepsis hospitalisations, 59.6% in men. Age-standardised risk of hospitalisation was higher in men versus women (10.37 vs 6.77 per 1,000 person years; age-adjusted HR 1.58; 95% CI 1.53–1.59) and did not attenuate after adjusting for sociodemographics, health behaviours and co-morbidities. Relative risks were similar for sepsis-related ICU admissions (adjusted HR 1.72; 95% CI 1.57–1.88). Death at one year was more common in men than women (39.3% vs 33.7% p<0.001). After adjusting for age, men had a longer hospital (12.0 vs 11.2 days; p<0.001) and ICU (6.5 vs 5.8 days; p<0.001) stays and were more likely to be readmitted to hospital for sepsis (22.3 vs 19.4%; p<0.001) or any reason (73.0% vs 70.7%; p<0.001) at one year. Conclusion: In older adults, compared to women, men are at an increased risk of sepsis hospitalisation, sepsis-related ICU admission, death and readmission to hospital within one year after a sepsis hospitalisation. Understanding these sex differences and their mechanisms may offer opportunities for better prevention and management and improved patient outcomes

Working Memory Modulation of Frontoparietal Network Connectivity in First-Episode Schizophrenia

Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging, the current study investigated whether WM-dependent modulation of effective connectivity in this network is affected in a group of first-episode schizophrenia (FES) patients compared with similarly performing healthy participants during a verbal n-back task. Dynamic causal modeling (DCM) of the coupling between regions (left inferior frontal gyrus (IFG), left inferior parietal lobe (IPL), and primary visual area) identified in a psychophysiological interaction (PPI) analysis was performed to characterize effective connectivity during the n-back task. The PPI analysis revealed that the connectivity between the left IFG and left IPL was modulated by WM and that this modulation was reduced in FES patients. The subsequent DCM analysis confirmed this modulation by WM and found evidence that FES patients had reduced forward connectivity from IPL to IFG. These findings provide evidence for impaired WM modulation of frontoparietal effective connectivity in the early phase of schizophrenia, even with intact WM performance, suggesting a failure of context-sensitive coupling in the schizophrenic brain

A 4D Light-Field Dataset and CNN Architectures for Material Recognition

We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field images. Our main goal is to investigate whether the additional information in a light-field (such as multiple sub-aperture views and view-dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7% boost compared with 2D image classification (70% to 77%). These results constitute important baselines that can spur further research in the use of CNNs for light-field applications. Upon publication, our dataset also enables other novel applications of light-fields, including object detection, image segmentation and view interpolation.Comment: European Conference on Computer Vision (ECCV) 201

Rethinking Indian monsoon rainfall prediction in the context of recent global warming

Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability

Magnetic field mediated low-temperature resistivity upturn in electron-doped La1-xHfxMnO3 manganite oxides

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