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      Personal Data Collection in the Workplace: Ethical and Technical Challenges

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      Electronic Visualisation and the Arts (EVA 2017) (EVA)

      Electronic Visualisation and the Arts

      11 – 13 July 2017

      Personal Data Collection, Hazardous environments, Rugged IoT, Ethics, Wearables

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Forestry is a dangerous work environment and collecting data on site to identify and warn about hazardous situations is challenging. In this paper, we discuss our attempts at creating continuous data-collection methods that are ethical, sustainable and effective. We explore the difficulties in collecting personal and environmental data from workers and their work domain. We also draw attention to the specific challenges in designing for sensor-based, wearable rugged IoT solutions. We present a case-study, comprising of a number of experiments, which exemplifies the work we have been undertaking in this domain. The case study is based on our approach to developing a robust, trusted Internet of Things (IoT) solution for dangerous work environments (specifically the forestry environment). We focus the results of this case- study on both the technical successes and challenges as well as the personal and ethical challenges that have been elicited.

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          Most cited references 44

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          On the features and challenges of security and privacy in distributed internet of things

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            Work load and work hours in relation to disturbed sleep and fatigue in a large representative sample.

            To study the relation between work and background factors on the one hand and disturbed sleep and fatigue on the other. A representative national sample of 58,115 individuals was selected at regular intervals over a period of 20 years and interviewed on issues related to work and health. The data were subjected to a multiple logistic regression analysis. The number of cases was 18,828 (32.8%) for fatigue and 7347 (12.8%) for disturbed sleep. For disturbed sleep, the significant predictors became: female gender, age above 49 years, present illness, hectic work, physically strenuous work, and shift work. For fatigue, the significant predictors became female gender, age below 49 years, high socioeconomic status, present illness, hectic work, overtime work, and physically strenuous work. Work stress, shift work, and physical workload interfere with sleep and are related to fatigue.
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              An overview of the Internet of Things for people with disabilities

               Mari Domingo (2012)
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                Author and article information

                Contributors
                Conference
                July 2017
                July 2017
                : 1-11
                Affiliations
                Computer Science Department

                University of Waikato, New Zealand
                Article
                10.14236/ewic/HCI2017.57
                © Bowen et al. Published by BCS Learning and Development. Proceedings of British HCI 2017 – Digital Make-Believe, Sunderland, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Electronic Visualisation and the Arts (EVA 2017)
                EVA
                London, UK
                11 – 13 July 2017
                Electronic Workshops in Computing (eWiC)
                Electronic Visualisation and the Arts
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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