Safety at work The objective of the project Safety at Work is to increase safety at the workplace by applying and combining state of the art artefacts from personal protective equipment and ambient intelligence technology. In this state of the art document we focus on the developments with respect to how (persuasive) technology can help to influence behaviour in a natural, automatic way in order to make industrial environments safer. We focus on personal safety, safe environments and safe behaviour. Direct ways to influence safety The most obvious way to influence behaviour is to use direct, physical measures. In particular, this is known from product design. The safe use of a product is related to the characteristics of the product (e.g., sharp edges), the condition of people operating the product (e.g., stressed or tired), the man-machine interface (e.g., intuitive or complex) and the environmental conditions while operating the product (e.g., noisy or crowded). Design guidelines exist to help designers to make safe products. A risk matrix can be made with two axis: product hazards versus personal characteristics. For each combination one might imagine what can go wrong, and what potential solutions are. Except for ‘design for safety’ in the sense of no sharp edges or a redundant architecture, there is a development called ‘safety by design’ as well. Safety by design is a concept that encourages construction or product designers to ‘design out’ health and safety risks during design development. On this topic, we may learn from the area of public safety. Crime Prevention Through Environmental Design (or Designing Out Crime) is a multi-disciplinary approach to deterring criminal behaviour through environmental design. Designing Out Crime uses measures like taking steps to increase (the perception) that people can be seen, limiting the opportunity for crime by taking steps to clearly differentiate between public space and private space, and promoting social control through improved proprietary concern. Senses Neuroscience has shown that we have very little insight into our motivations and, consequently, are poor at predicting our own behaviour. It seems emotions are an important predictor of our behaviour. Input from our senses are important for our emotional state, and therefore influence our behaviour in an ‘ambient’ (invisible) way. The first sense we focus on is sight. Sight encompasses the perception of light intensity (illuminance) and colours (spectral distribution). Several researchers have studied the effects of light and colour in working environments. Results show, e.g., that elderly people can be helped with higher light levels, that cool colours like blue and green have a relaxing effect, while long-wavelength colours such as orange and red are stimulating and give more arousal, and that concentration and motivation of pupils at school can be influenced with light and colour settings. Identically, sound (hearing) has physiological effects (unexpected sounds cause extra cortisol -the fight or flight hormone- and the opposite for soothing sounds), psychological effects (sounds effect our emotions), cognitive effects (sounds effect our concentration) and behavioural effects (the natural behaviour of people is to avoid unpleasant sounds, and embrace pleasurable sounds). Smell affects 75% of daily emotions and plays an important role in memory, itis also important as a warning for danger (gas, burning smell). Research has shown that smell can influence work performance. Haptic feedback is a relative new area of research, and most studies focus on haptic feedback on handheld and automotive devices. Finally, employers have a duty to take every reasonable precaution to protect workers from heat stress disorders. Influence mechanisms: Cialdini To influence behaviour, we may learn from marketing psychology. Robert Cialdini states that if we have to think about every decision
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Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
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