Static load bearing exercises of individuals with transfemoral amputation fitted with an osseointegrated implant: Reliability of kinetic data

This study aimed at presenting the intra-tester reliability of the static load bearing exercises (LBEs) performed by individuals with transfemoral amputation (TFA) fitted with an osseointegrated implant to stimulate the bone remodelling process. There is a need for a better understanding of the implementation of these exercises particularly the reliability. The intra-tester reliability is discussed with a particular emphasis on inter-load prescribed, inter-axis and inter-component reliabilities as well as the effect of body weight normalisation. Eleven unilateral TFAs fitted with an OPRA implant performed five trials in four loading conditions. The forces and moments on the three axes of the implant were measured directly with an instrumented pylon including a six-channel transducer. Reliability of loading variables was assessed using intraclass correlation coefficients (ICCs) and percentage standard error of measurement values (%SEMs). The ICCs of all variables were above 0.9 and the %SEM values ranged between 0 and 87%. This study showed a high between-participants’ variance highlighting the lack of loading consistency typical of symptomatic population as well as a high reliability between the loading sessions indicating a plausible correct repetition of the LBE by the participants. However, these outcomes must be understood within the framework of the proposed experimental protocol.

Analysis of young driver crash data. Report to the Research Council of Oman, Muscat

Road traffic crashes are an alarming public health issue in Oman, despite ongoing improvements in traffic law enforcement practices and technology. One of the main target groups for road safety in Oman are young drivers aged 17-25 years. This report provides an overview of the characteristics of crashes in Oman involving young drivers (17-25 years) between 1st January 2009 and 31st December 2011. Although, young drivers aged 17-25 years comprise around 17% of all licence holders in Oman, they represented more than one third of all drivers involved in road traffic crashes in the country. A total of 11,101 young drivers (17-25 years) were involved in registered crashes during the study period. From this, 7,727 young drivers (69.6%) were found to be the cause of the crashes...

Role of big data in the early detection of Ebola and other emerging infectious diseases

The lack of adequate disease surveillance systems in Ebola-affected areas has both reduced the ability to respond locally and has increased global risk. There is a need to improve disease surveillance in vulnerable regions, and digital surveillance could present a viable approach.

Developing an epidemic forecasting model for Influenza A in Brisbane, Australia, based on climate and Hong Kong Influenza A surveillance data

Influenza is associated with substantial disease burden [ 1]. Development of a climate-based early warning system for in fluenza epidemics has been recommended given the signi fi - cant association between climate variability and influenza activity [2]. Brisbane is a subtropical city in Australia and offers free in fluenza vaccines to residents aged ≥65 years considering their high risks in developing life-threatening complications, especially for in fluenza A predominant seasons. Hong Kong is an international subtropical city in Eastern Asia and plays a crucial role in global infectious diseases transmission dynamics via the international air transportation network [3, 4]. We hypothesized that Hong Kong in fluenza surveillance data could provide a signal for in fluenza epidemics in Brisbane [ 4]. This study aims to develop an epidemic forecasting model for influenza A in Brisbane elders, by combining climate variability and Hong Kong in fluenza A surveillance data. Weekly numbers of laboratoryconfirmed influenza A positive isolates for people aged ≥65 years from 2004 to 2009 were obtained for Brisbane from Queensland Health, Australia, and for Hong Kong from Queen Mary Hospital (QMH). QMH is the largest public hospital located in Hong Kong Island, and in fluenza surveillance data from this hospital have been demonstrated to be representative for influenza circulation in the entirety of Hong Kong [ 5]. The Brisbane in fluenza A epidemics occurred during July –September, whereas the Hong Kong in fluenza A epidemics occurred during February –March and May –August.

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e.position, speed and heading) in the process of map-matching are normally suitable for high frequency (1 Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A* search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory. The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1 s, 5 s, 30 s, 60 s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30 s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

Links between unlicensed and unregistered driving (Data analysis). Report to Queensland Department of Transport and Main Roads

There is a current lack of understanding regarding the use of unregistered vehicles on public roads and road-related areas, and the links between the driving of unregistered vehicles and a range of dangerous driving behaviours. This report documents the findings of data analysis conducted to investigate the links between unlicensed driving and the driving of unregistered vehicles, and is an important initial undertaking into understanding these behaviours. This report examines de-identified data from two sources: crash data; and offence data. The data was extracted from the Queensland Department of Transport and Main Roads (TMR) databases and covered the period from 2003 to 2008.

Pedagogical relations in the age of big data [Commentary]

Introduction A pedagogical relationship - the relationship produced through teaching and learning - is, according to phenomenologist Max van Maanen, ‘the most profound relationship an adult can have with a child’ (van Maanen 1982). But what does it mean for a teacher to have a ‘profound’ relationship with a student in digital times? What, indeed, is an optimal pedagogical relationship at a time when the exponential proliferation and transformation of information across the globe is making for unprecedented social and cultural change? Does it involve both parties in a Facebook friendship? Being snappy with Snapchat? Tumbling around on Tumblr? There is now ample evidence of a growing trend to displace face-to-face interaction by virtual connections. One effect of these technologically mediated relationships is that a growing number of young people experience relationships as ‘mile-wide, inch-deep’ phenomena. It is timely, in this context, to explore how pedagogical relationships are being transmuted by Big Data, and to ask about the implications this has for current and future generations of professional educators.

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Clinical data warehousing for evidence based decision making

Large volumes of heterogeneous health data silos pose a big challenge when exploring for information to allow for evidence based decision making and ensuring quality outcomes. In this paper, we present a proof of concept for adopting data warehousing technology to aggregate and analyse disparate health data in order to understand the impact various lifestyle factors on obesity. We present a practical model for data warehousing with detailed explanation which can be adopted similarly for studying various other health issues.

Data driven and practice-based evidence : design and development of efficient and effective clinical decision support system

Decision-making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from randomized controlled trials (RCT), such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals’ confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records (EHR) capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

Exploring Environmental Stewardship Through Data-Driven Practices

What potential do artists working with environmental data in public space have for producing new forms of engagement with local environmental conditions? Operating on the edge of heavy bureaucracy, these types of data-driven artistic experiments probe the politics of environmental metrics and explore methods of engaging audiences with issues of environmental health. This discussion considers a small collection of cases studies representative of this growing field of practice. These are works by Natalie Jeremijenko and The Living, Tega Brain and Keith Deverell. The case studies considered are examples of strategic design, works that soften, reveal and potentially shift existing regulations and bureaucratic norms. In doing so they open up new possibilities and questions as to what the smart city is and how it might be realised.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

The Global Protection of Personal Health Data

The workshop is an activity of the IMIA Working Group ‘Security in Health Information Systems’ (SiHIS). It is focused to the growing global problem: how to protect personal health data in today’s global eHealth and digital health environment. It will review available trust building mechanisms, security measures and privacy policies. Technology alone does not solve this complex problem and current protection policies and legislation are considered woefully inadequate. Among other trust building tools, certification and accreditation mechanisms are dis-cussed in detail and the workshop will determine their acceptance and quality. The need for further research and international collective action are discussed. This workshop provides an opportunity to address a critical growing problem and make pragmatic proposals for sustainable and effective solutions for global eHealth and digital health.