OBJECTIVE: The aim of this study was to explore the longitudinal relationship between sitting time on a working day and vitality, work performance, presenteeism, and sickness absence.METHODS: At the start and end of a five-month intervention program at the workplace, as well as 10 months after the intervention, sitting time and work-related outcomes were measured using a standardized self-administered questionnaire and company records. Generalized linear mixed models were used to estimate the longitudinal relationship between sitting time and work-related outcomes, and possible interaction effects over time.RESULTS: A significant and sustainable decrease in sitting time on a working day was observed. Sitting less was significantly related to higher vitality scores, but this effect was marginal (b = -0.0006, P = 0.000).CONCLUSIONS: Our finding of significant though marginal associations between sitting time and important work-related outcomes justifies further research.
Recent textile innovations have significantly transformed both the material structures of fibers and fabrics as well as their sphere of use and applications.At the same time, new recycling concepts and methods to re--use textile waste are rapidly being developed and many new ways to make use of recycled and reclaimed fibers have already been found. In this paper, we describe how the development of a new textile, making use of recycled fibers, sparked the development of Textile Reflexes, a robotic textile that can change shape. This paper elaborates on the development of the new textile material, the multidisciplinary approach we take to advance it towards a robotic textile and our first endeavours to implement it in a health & wellbeing context. Textile Reflexes was applied in a vest that supports posture correction and training that was evaluated in a user study. In this way, the paper demonstrates a material and product design study that bridges disciplines and that links to both environmental and social change.doi: 10.21606/dma.2017.610This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. https://creativecommons.org/licenses/by-nc-sa/4.0/
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The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%)
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