908 resultados para fatigue


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Monotony has been identified as a contributing factor to road crashes. Drivers’ ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks, such as driving on Australian rural roads, many of which are monotonous by nature. Highway design in particular attempts to reduce the driver’s task to a merely lane-keeping one. Such a task provides little stimulation and is monotonous, thus affecting the driver’s attention which is no longer directed towards the road. Inattention contributes to crashes, especially for professional drivers. Monotony has been studied mainly from the endogenous perspective (for instance through sleep deprivation) without taking into account the influence of the task itself (repetitiveness) or the surrounding environment. The aim and novelty of this thesis is to develop a methodology (mathematical framework) able to predict driver lapses of vigilance under monotonous environments in real time, using endogenous and exogenous data collected from the driver, the vehicle and the environment. Existing approaches have tended to neglect the specificity of task monotony, leaving the question of the existence of a “monotonous state” unanswered. Furthermore the issue of detecting vigilance decrement before it occurs (predictions) has not been investigated in the literature, let alone in real time. A multidisciplinary approach is necessary to explain how vigilance evolves in monotonous conditions. Such an approach needs to draw on psychology, physiology, road safety, computer science and mathematics. The systemic approach proposed in this study is unique with its predictive dimension and allows us to define, in real time, the impacts of monotony on the driver’s ability to drive. Such methodology is based on mathematical models integrating data available in vehicles to the vigilance state of the driver during a monotonous driving task in various environments. The model integrates different data measuring driver’s endogenous and exogenous factors (related to the driver, the vehicle and the surrounding environment). Electroencephalography (EEG) is used to measure driver vigilance since it has been shown to be the most reliable and real time methodology to assess vigilance level. There are a variety of mathematical models suitable to provide a framework for predictions however, to find the most accurate model, a collection of mathematical models were trained in this thesis and the most reliable was found. The methodology developed in this research is first applied to a theoretically sound measure of sustained attention called Sustained Attention Response to Task (SART) as adapted by Michael (2010), Michael and Meuter (2006, 2007). This experiment induced impairments due to monotony during a vigilance task. Analyses performed in this thesis confirm and extend findings from Michael (2010) that monotony leads to an important vigilance impairment independent of fatigue. This thesis is also the first to show that monotony changes the dynamics of vigilance evolution and tends to create a “monotonous state” characterised by reduced vigilance. Personality traits such as being a low sensation seeker can mitigate this vigilance decrement. It is also evident that lapses in vigilance can be predicted accurately with Bayesian modelling and Neural Networks. This framework was then applied to the driving task by designing a simulated monotonous driving task. The design of such task requires multidisciplinary knowledge and involved psychologist Rebecca Michael. Monotony was varied through both the road design and the road environment variables. This experiment demonstrated that road monotony can lead to driving impairment. Particularly monotonous road scenery was shown to have the most impact compared to monotonous road design. Next, this study identified a variety of surrogate measures that are correlated with vigilance levels obtained from the EEG. Such vigilance states can be predicted with these surrogate measures. This means that vigilance decrement can be detected in a car without the use of an EEG device. Amongst the different mathematical models tested in this thesis, only Neural Networks predicted the vigilance levels accurately. The results of both these experiments provide valuable information about the methodology to predict vigilance decrement. Such an issue is quite complex and requires modelling that can adapt to highly inter-individual differences. Only Neural Networks proved accurate in both studies, suggesting that these models are the most likely to be accurate when used on real roads or for further research on vigilance modelling. This research provides a better understanding of the driving task under monotonous conditions. Results demonstrate that mathematical modelling can be used to determine the driver’s vigilance state when driving using surrogate measures identified during this study. This research has opened up avenues for future research and could result in the development of an in-vehicle device predicting driver vigilance decrement. Such a device could contribute to a reduction in crashes and therefore improve road safety.

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Structural Health Monitoring (SHM) is defined as the use of on-structure sensing system to monitor the performance of the structure and evaluate its health state. Recent bridge failures, such as the collapses of the 1-35W Highway Bridge in USA, the collapse of the Can Tho Bridge in Vietnam and the Xijiang River Bridge in the Mainland China, all of which happened in the year 2007, have alerted the importance of structural health monitoring. This book presents a background of SHM technologies together with its latest development and successful applications. It is a book launched to celebrate the establishment of the Australian Network of Structural Health Monitoring (ANSHM). The network comprising leading SHM experts in Australia promotes and advances SHM research, application, education and development in Australia.

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Structural health monitoring has been accepted as a justified effort for long-span bridges, which are critical to a region's economic vitality. As the most heavily instrumented bridge project in the world, WASHMS - Wind And Structural Health Monitoring System has been developed and installed on the cable-supported bridges in Hong Kong (Wong and Ni 2009a). This chapter aims to share some of the experience gained through the operations and studies on the application of WASHMS. It is concluded that Structural Health Monitoring should be composed of two main components: Structural Performance Monitoring (SPM) and Structural Safety Evaluation (SSE). As an example to illustrate how the WASHMS could be used for structural performance monitoring, the layout of the sensory system installed on the Tsing Ma Bridge is briefly described. To demonstrate the two broad approaches of structural safety evaluation - Structural Health Assessment and Damage Detection, three examples in the application of SHM information are presented. These three examples can be considered as pioneer works for the research and development of the structural diagnosis and prognosis tools required by the structural health monitoring for monitoring and evaluation applications.

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The paper explores the way in which the life of concrete sleepers can be dramatically affected by two important factors, namely impact forces and fatigue cycles. Drawing on the very limited experimental and field data currently available about these two factors, the paper describes detailed simulations of sleepers in a heavy haul track in Queensland Australia over a period of 100 years. The simulation uses real wheel/rail impact force records from that track, together with data on static bending tests of similar sleepers and preliminary information on their impact vs static strength. The simulations suggest that despite successful performance over many decades, large unplanned replacement costs could be imminent, especially considering the increasingly demanding operational conditions sleepers have sustained over their life. The paper also discusses the key factors track owners need to consider in attempting to plan for these developments.

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Introduction: Cancer is increasingly being viewed as a chronic illness requiring long-term management, and there is a growing need for evidence-based rehabilitation interventions for cancer survivors. Previous reviews have evaluated the benefits of exercise interventions for patients undergoing cancer treatment and long-term survivors, but none have investigated the role of exercise during cancer rehabilitation, the period immediately following cancer treatment completion. This systematic review summarises the literature on the health effects of exercise during cancer rehabilitation and evaluates the methodological rigour of studies in this area to date.----------- Methods: Relevant studies were identified through a systematic search of PubMed and Embase to April 2009. Data on study design, recruitment strategy, participants, exercise intervention, adherence rates, and outcomes were extracted. Methodological rigour was assessed using a structured rating system.---------- Results: Ten studies were included. Breast cancer patients were the predominate patient group represented. Most interventions were aerobic or resistance-training exercise programmes, and exercise type, frequency, duration and intensity varied across studies. Improvements in physical functioning, strength, physical activity levels, quality of life, fatigue, immune function, haemoglobin concentrations, potential markers of recurrence, and body composition were reported. However, all studies were limited by incomplete reporting and methodological limitations.---------- Conclusions: Although the methodological limitations of studies in this new field must be acknowledged, initial evidence indicates that exercise is feasible and may provide physiological and psychological benefits for cancer survivors during the rehabilitation period. Future studies with rigorous study designs are now required to advance the field.

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Little is known about cancer survivors’ experiences with and preferences for exercise programmes offered during rehabilitation (immediately after cancer treatment). This study documented colorectal cancer survivors’ experiences in an exercise rehabilitation programme and their preferences for programme content and delivery. At the completion of 12-weeks of supervised exercise, 10 participants took part in one-on-one semi-structured interviews. Data from these interviews were coded, and themes were identified using qualitative software. Key findings were that most participants experienced improvements in treatment symptoms, including reduced fatigue and increased energy and confidence to do activities of daily living. They also reported that interactions with the exercise trainer and a flexible programme delivery were important aspects of the intervention. Most participants reported that they preferred having a choice of exercise, starting to exercise within a month after completing treatment, having supervision and maintaining a one-on-one format. Frustrations included scheduling conflicts and a lack of a transition out of the programme. The findings indicate that colorectal cancers experience benefits from exercise offered immediately after treatment and prefer individual attention from exercise staff. They further indicate directions for the implementation of future exercise programmes with this population.

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Background: Exercise is known to improve mental and physical functioning and to improve quality of life. The obstacles faced by individuals with chronic kidney disease on maintenance haemodialysis include increased levels of fatigue, decreased motivation, and the inability to schedule exercise around daily activities and dialysis schedules. Aim: This pilot study was undertaken to determine the feasibility and potential efficacy of an individually-tailored exercise program for in-centre haemodialysis patients. Method: A 16 week program was designed and evaluated in relation to changes in physical capacity, the extent of exercise undertaken, and quality of life indicators. Results and Conclusion: The resultant recommendations regarding the level of motivational support, the time and physical requirements in implementing an exercise program will provide useful information for others embarking on similar studies.

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Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

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Wellness is now seen as central to redefining the National Health agenda. There is growing evidence that contact with nature and physical activity in nature has considerable positive effects on human health. At the most basic level humanity is reliant on the natural world for resources such as air and water. However, a growing body of research is finding that beyond this fundamental relationship exposure to the non-human natural world can also positively enhance perceptions of physiological, emotional, psychological and spiritual health in ways that cannot be satisfied by alternate means. Theoretical explanations for this have posited that non-human nature might 1) restore mental fatigue, 2) trigger deep reflections, 3) provide an opportunity for nurturing and 4) rekindle innate connections. In this paper the authors show how human wellness is strongly connected to their relationship with the natural world. This paper points to how non-human nature could be better utilised for enhancing human health and wellness.

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Information behaviour (IB) is an area within Library and Information Science that studies the totality of human behaviour in relation to information, both active and passive, along with the explicit and the tacit mental states related to information. This study reports on a recently completed dissertation research that integrates the different models of information behaviours using a diary study where 34 participants maintained a daily journal for two weeks through a web log or paper diary. This resulted in thick descriptions of IB, which were manually analysed using the Grounded Theory method of inquiry, and then cross-referenced through both text-analysis and statistical analysis programs. Among the many key findings of this study, one is the focus this paper: how participants express their feelings of the information seeking process and their mental and affective states related specifically to the sense-making component which co-occurs with almost every other aspect of information behaviour. The paper title – Down the Rabbit Hole and Through the Looking Glass – refers to an observation that some of the participants made in their journals when they searched for, or avoided information, and wrote that they felt like they have fallen into a rabbit hole where nothing made sense, and reported both positive feelings of surprise and amazement, and negative feelings of confusion, puzzlement, apprehensiveness, frustration, stress, ambiguity, and fatigue. The study situates this sense-making aspects of IB within an overarching model of information behaviour that includes IB concepts like monitoring information, encountering information, information seeking and searching, flow, multitasking, information grounds, information horizons, and more, and proposes an integrated model of information behaviour illuminating how these different concepts are interleaved and inter-connected with each other, along with it's implications for information services.

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Goals of work: The aim of this secondary data analysis was to investigate symptom clusters over time for symptom management of a patient group after commencing adjuvant chemotherapy. Materials and methods: A prospective longitudinal study of 219 cancer outpatients conducted within 1 month of commencing chemotherapy (T1), 6 months (T2), and 12 months (T3) later. Patients' distress levels were assessed for 42 physical symptoms on a clinician-modified Rotterdam Symptom Checklist. Symptom clusters were identified in exploratory factor analyses at each time. Symptom inclusion in clusters was determined from structure coefficients. Symptoms could be associated with multiple clusters. Stability over time was determined from symptom cluster composition and the proportion of symptoms in the initial symptom clusters replicated at later times. Main results Fatigue and daytime sleepiness were the most prevalent distressing symptoms over time. The median number of concurrent distressing symptoms approximated 7, over time. Five consistent clusters were identified at T1, 2, and T3. An additional two clusters were identified at 12 months, possibly due to less variation in distress levels. Weakness and fatigue were each associated with two, four, and five symptom clusters at T1, T2, and T3, respectively, potentially suggesting different causal mechanisms. Conclusion: Stability is a necessary attribute of symptom clusters, but definitional clarification is required. We propose that a core set of concurrent symptoms identifies each symptom cluster, signifying a common cause. Additional related symptoms may be included over time. Further longitudinal investigation is required to identify symptom clusters and the underlying causes.

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Today, polarisation of the fashion textile industry has already begun as smart, intelligent and conscientious fashion emerges as a backlash to the experience of choice fatigue, poor quality, dumb design and greenwash. But the process, development and manufacture of fashion textiles is complex. And the demand, both customer and industry driven, for new integrated product policies,2 designed to minimise environmental impacts by looking at all phases of a product's life cycle, is problematic due to complexity and a lack of networking tools. This article explores these issues through the construct of the department store of the future.

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Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

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To assist road safety professionals in developing effective strategies to combat the risk associated with driving while fatigued, a survey was administered to 1000 Australian drivers. Participants reported their past behaviours in regards to driving while sleepy and their perceptions of risk associated with driving fatigued as compared to speeding and driving under the influence of alcohol. Although participants appeared to be aware of the substantial risk associated with driving while sleepy, many drivers reported that they frequently drive when sleepy. Age and gender comparisons, revealed that risk taking behaviour in regards to driving while sleepy is occurring across all age groups and in both male and female drivers. Overall young to middle age drivers and male drivers reported the highest frequency of driving while sleepy and reported the lowest perceived personal risk in regards to driving while sleepy.