Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.

Development and formative evaluation of a motorcycle rider training intervention to address risk taking

The need to address on-road motorcycle safety in Australia is important due to the disproportionately high percentage of riders and pillions killed and injured each year. One approach to preventing motorcycle-related injury is through training and education. However, motorcycle rider training lacks empirical support as an effective road safety countermeasure to reduce crash involvement. Previous reviews have highlighted that risk-taking is a contributing factor in many motorcycle crashes, rather than merely a lack of vehicle-control skills (Haworth & Mulvihill, 2005; Jonah, Dawson & Bragg, 1982; Watson et al, 1996). Hence, though the basic vehicle-handling skills and knowledge of road rules that are taught in most traditional motorcycle licence training programs may be seen as an essential condition of safe riding, they do not appear to be sufficient in terms of crash reduction. With this in mind there is considerable scope for the improvement of program focus and content for rider training and education. This program of research examined an existing traditional pre-licence motorcycle rider training program and formatively evaluated the addition of a new classroom-based module to address risky riding; the Three Steps to Safer Riding program. The pilot program was delivered in the real world context of the Q-Ride motorcycle licensing system in the state of Queensland, Australia. Three studies were conducted as part of the program of research: Study 1, a qualitative investigation of delivery practices and student learning needs in an existing rider training course; Study 2, an investigation of the extent to which an existing motorcycle rider training course addressed risky riding attitudes and motives; and Study 3, a formative evaluation of the new program. A literature review as well as the investigation of learning needs for motorcyclists in Study 1 aimed to inform the initial planning and development of the Three Steps to Safer Riding program. Findings from Study 1 suggested that the training delivery protocols used by the industry partner training organisation were consistent with a learner-centred approach and largely met the learning needs of trainee riders. However, it also found that information from the course needs to be reinforced by on-road experiences for some riders once licensed and that personal meaning for training information was not fully gained until some riding experience had been obtained. While this research informed the planning and development of the new program, a project team of academics and industry experts were responsible for the formulation of the final program. Study 2 and Study 3 were conducted for the purpose of formative evaluation and program refinement. Study 2 served primarily as a trial to test research protocols and data collection methods with the industry partner organisation and, importantly, also served to gather comparison data for the pilot program which was implemented with the same rider training organisation. Findings from Study 2 suggested that the existing training program of the partner organisation generally had a positive (albeit small) effect on safety in terms of influencing attitudes to risk taking, the propensity for thrill seeking, and intentions to engage in future risky riding. However, maintenance of these effects over time and the effects on riding behaviour remain unclear due to a low response rate upon follow-up 24 months after licensing. Study 3 was a formative evaluation of the new pilot program to establish program effects and possible areas for improvement. Study 3a examined the short term effects of the intervention pilot on psychosocial factors underpinning risky riding compared to the effects of the standard traditional training program (examined in Study 2). It showed that the course which included the Three Steps to Safer Riding program elicited significantly greater positive attitude change towards road safety than the existing standard licensing course. This effect was found immediately following training, and mean scores for attitudes towards safety were also maintained at the 12 month follow-up. The pilot program also had an immediate effect on other key variables such as risky riding intentions and the propensity for thrill seeking, although not significantly greater than the traditional standard training. A low response rate at the 12 month follow-up unfortunately prevented any firm conclusions being drawn regarding the impact of the pilot program on self-reported risky riding once licensed. Study 3a further showed that the use of intermediate outcomes such as self-reported attitudes and intentions for evaluation purposes provides insights into the mechanisms underpinning risky riding that can be changed by education and training. A multifaceted process evaluation conducted in Study 3b confirmed that the intervention pilot was largely delivered as designed, with course participants also rating most aspects of training delivery highly. The complete program of research contributed to the overall body of knowledge relating to motorcycle rider training, with some potential implications for policy in the area of motorcycle rider licensing. A key finding of the research was that psychosocial influences on risky riding can be shaped by structured education that focuses on awareness raising at a personal level and provides strategies to manage future riding situations. However, the formative evaluation was mainly designed to identify areas of improvement for the Three Steps to Safer Riding program and found several areas of potential refinement to improve future efficacy of the program. This included aspects of program content, program delivery, resource development, and measurement tools. The planned future follow-up of program participants' official crash and traffic offence records over time may lend further support for the application of the program within licensing systems. The findings reported in this thesis offer an initial indication that the Three Steps to Safer Riding is a useful resource to accompany skills-based training programs.

Ergonomic DHM systems - limitations and trends – a systematic literature review focused on the ‘future of ergonomics’

Digital Human Models (DHM) have been used for over 25 years. They have evolved from simple drawing templates, which are nowadays still used in architecture, to complex and Computer Aided Engineering (CAE) integrated design and analysis tools for various ergonomic tasks. DHM are most frequently used for applications in product design and production planning, with many successful implementations documented. DHM from other domains, as for example computer user interfaces, artificial intelligence, training and education, or the entertainment industry show that there is also an ongoing development towards a comprehensive understanding and holistic modeling of human behavior. While the development of DHM for the game sector has seen significant progress in recent years, advances of DHM in the area of ergonomics have been comparatively modest. As a consequence, we need to question if current DHM systems are fit for the design of future mobile work systems. So far it appears that DHM in Ergonomics are rather limited to some traditional applications. According to Dul et al. (2012), future characteristics of Human Factors and Ergonomics (HFE) can be assigned to six main trends: (1) global change of work systems, (2) cultural diversity, (3) ageing, (4) information and communication technology (ICT), (5) enhanced competiveness and the need for innovation, and; (6) sustainability and corporate social responsibility. Based on a literature review, we systematically investigate the capabilities of current ergonomic DHM systems versus the ‘Future of Ergonomics’ requirements. It is found that DHMs already provide broad functionality in support of trends (1) and (2), and more limited options in regards to trend (3). Today’s DHM provide access to a broad range of national and international databases for correct differentiation and characterization of anthropometry for global populations. Some DHM explicitly address social and cultural modeling of groups of people. In comparison, the trends of growing importance of ICT (4), the need for innovation (5) and sustainability (6) are addressed primarily from a hardware-oriented and engineering perspective and not reflected in DHM. This reflects a persistent separation between hardware design (engineering) and software design (information technology) in the view of DHM – a disconnection which needs to be urgently overcome in the era of software defined user interfaces and mobile devices. The design of a mobile ICT-device is discussed to exemplify the need for a comprehensive future DHM solution. Designing such mobile devices requires an approach that includes organizational aspects as well as technical and cognitive ergonomics. Multiple interrelationships between the different aspects result in a challenging setting for future DHM. In conclusion, the ‘Future of Ergonomics’ pose particular challenges for DHM in regards to the design of mobile work systems, and moreover mobile information access.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Overview of complex systems in sport

The complex systems approach offers an opportunity to replace the extant pre-dominant mechanistic view on sport-related phenomena. The emphasis on the environment-system relationship, the applications of complexity principles, and the use of nonlinear dynamics mathematical tools propose a deep change in sport science. Coordination dynamics, ecological dynamics, and network approaches have been successfully applied to the study of different sport-related behaviors, from movement patterns that emerge at different scales constrained by specific sport contexts to game dynamics. Sport benefit from the use of such approaches in the understanding of technical, tactical, or physical conditioning aspects which change their meaning and dilute their frontiers. The creation of new learning and training strategies for teams and individual athletes is a main practical consequence. Some challenges for the future are investigating the influence of key control parameters in the nonlinear behavior of athlete-environment systems and the possible relatedness of the dynamics and constraints acting at different spatio-temporal scales in team sports. Modelling sport-related phenomena can make useful contributions to a better understanding of complex systems and vice-versa.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Doctoral research training : encouraging timely completion – case study reflections of one university on how it supports a vibrant research training culture

This is a case study of a young university striving to generate and sustain a vibrant Research Training culture. The university’s research training framework is informed by a belief in a project management approach to achieving successful research candidature. This has led to the definition and reporting of key milestones during candidature. In turn, these milestones have generated a range of training programs to support Higher Degree Research (HDR) students to meet these milestones in a timely fashion. Each milestone focuses on a specific set of skills blended with supporting the development of different parts of the doctoral thesis. Data on student progress and completion has provided evidence in highlighting the role that the milestones and training are playing in supporting timely completion. A university-wide reporting cycle generated data on the range of workshops and training provided to Higher Degree Research students and supervisors. The report provided details of thesis topic and format, as well as participation in research training events and participant evaluation of those events. Analysis of the data led to recommendations and comments on the strengths and weaknesses of the current research training program. Discussion considered strategies and drivers for enhancements into the future. In particular, the paper reflects on the significant potential role of centrally curated knowledge systems to support HDR student and supervisor access, and engagement and success. The research training program was developed using blended learning as a model. It covered face-to-face workshops as well as online modules. These were supplemented by web portals that offered a range of services to inform and educate students and supervisors and included opportunities for students to interact with each other. Topics ranged from the research life cycle, writing and publication, ethics, managing research data, managing copyright, and project management to use of software and the University’s Code of Conduct for Research. The challenges discussed included: How to reach off campus students and those studying in external modes? How best to promote events to potential participants? How long and what format is best for face-to-face sessions? What online resources best supplement face-to-face offerings? Is there a place for peer-based learning and what form should this take? These questions are raised by a relatively young university seeking to build and sustain a vibrant research culture. The rapid growth in enrolments in recent years has challenged previous one-to-one models of support. This review of research training is timely in seeking strategies to address changing research training support capacity and student needs. Part of the discussion will focus on supervisory training, noting that good supervision is the one remaining place where one-to-one support is provided. Ensuring that supervisors are appropriately equipped to address student expectations is considered in the context of the research training provisions. The paper concludes with reflection on the challenges faced, and recommended ways forward as the number of research students grows into the future.

Fuzzy logic to evaluate driving maneuvers: An integrated approach to improve training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Electronic medical record systems in Saudi Arabia: Knowledge and preferences of healthcare professionals

Background The use of Electronic Medical Record (EMR) systems is increasing internationally, though developing countries, such as Saudi Arabia, have tended to lag behind in the adoption and implementation of EMR systems due to several barriers. The literature shows that the main barriers to EMR in Saudi Arabia are lack of knowledge or experience using EMR systems and staff resistance to using the implemented EMR system. Methods A quantitative methodology was used to examine health personnel knowledge and acceptance of and preference for EMR systems in seven Saudi public hospitals in Jeddah, Makkah and Taif cities. Results Both English literacy and education levels were significantly correlated with computer literacy and EMR literacy. Participants whose first language was not Arabic were more likely to prefer using an EMR system compared to those whose first language was Arabic. Conclusion This study suggests that as computer literacy levels increase, so too do staff preferences for using EMR systems. Thus, it would be beneficial for hospitals to assess English language proficiency and computer literacy levels of staff prior to implementing an EMR system. It is recommended that hospitals need to offer training and targeted educational programs to the potential users of the EMR system. This would help to increase English language proficiency and computer literacy levels of staff as well as staff acceptance of the system.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.

Reviewing in-vehicle systems to improve fuel efficiency and road safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behavior change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, it is crucial to understand the human factors related theories and practices which will inform the design of an in-vehicle Human Machine Interface (HMI) that could provide real-time driver feedback and consequently improve both fuel efficiency and safety. This paper provides a comprehensive review of the current state of published literature on in-vehicle systems to identify and evaluate the impact of eco-driving and safety feedback systems. This paper also discusses how these factors may conflict with one another and have a negative effect on road safety, while also exploring possible eco-driving practices that could encourage more sustainable, environmentally-conscious and safe driving behavior. The review revealed a lack of comprehensive theoretical research integrating eco-driving and safe driving, and no current available HMI covering both aspects simultaneously. Furthermore, the review identified that some eco-driving in-vehicle systems may enhance fuel efficiency without compromising safety. The review has identified a range of concepts which can be developed to influence driver acceptance of safety and eco-driving systems within the area of HMI. This can promote new research aimed at enhancing our understanding of the relationship between eco-driving and safety from the human factors viewpoint. This provides a foundation for developing innovative, persuasive and acceptable in-vehicle HMI systems to improve fuel efficiency and road safety.

Transitions to K–12 Education Systems: Experiences from Five Case Countries

Currently more than 140 countries offer, or are in transition to, what has become the international norm for pre-tertiary education, namely a kindergarten through grade 12 (K–12) school education system—kindergarten because of the preponderance of research asserting the long-term learning and social benefits of school readiness programs; and 12 years of primary and secondary schooling due to the time needed to acquire the knowledge and skills sets necessary for 21st century university education, postsecondary training, or decent1 work. This desk study2 conveys the experiences of four countries and one province in preparing and implementing a transition to a K–12 school education system: Mongolia, Ontario (Canada), the Philippines, Poland, and Turkey. Looking at K–12 transition in countries and systems that vary as broadly as this set enables common threads to stand out and divergent options to be noted.