Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Wearable Sensors in an Extreme Work Environment: Applying Computational Modelling for Evaluation

    Access Status
    Fulltext not available
    Authors
    Wilson, Micah
    Date
    2020
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Wilson, M.K. 2020. Wearable Sensors in an Extreme Work Environment: Applying Computational Modelling for Evaluation, in Society for Ambulatory Assessment Conference, Jan 15-17 2020. University of Melbourne.
    Source Conference
    Society for Ambulatory Assessment Conference 2020
    Faculty
    Faculty of Business and Law
    School
    Future of Work Institute
    URI
    http://hdl.handle.net/20.500.11937/80225
    Collection
    • Curtin Research Publications
    Abstract

    In safety-critical work environments (e.g., military, space operations), it is imperative that psycho-physical measurements do not disrupt operator's task performance. Consequently, many industries are now interested in the feasibility of implementing wearable technologies to passively assess psychological and physical states relevant to human performance (e.g., stress, fatigue, workload) on a continuous basis. However, studies demonstrating reliable associations between sensors and domain-relevant psycho-physical states are typically conducted in tightly controlled settings over relatively short timescales. This talk will outline the results of a field study that involved evaluating the utility of wearable sensors on board an operational Royal Australian Navy (RAN) vessel. RAN crew (n = 63) were equipped with electrocardiogram (ECG) and actigraphy (ACT) sensors, and completed psychometric testing four times per-day and a continuous activity diary. Data collection occurred over a 14 day mission, with acceptable compliance rates (> 80%). The data were used to develop a series of open-source bio-mathematical models capable of predicting subjective fatigue under different sleep schedules using full Bayesian inference. Additionally, time-domain ECG features were linked with daily diary observations, and an Artificial Neural-Network machine learning classifier was trained to detect sleep episodes. The classifier achieved a mean accuracy = 86.9%. Several challenges are discussed.

    Related items

    Showing items related by title, author, creator and subject.

    • Non-parametric belief propagation for mobile mapping sensor fusion
      Hollick, Joshua; Helmholz, Petra; Belton, David (2016)
      © 2016 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Many different forms of sensor fusion have been proposed each with its own niche. We propose a method of fusing multiple different ...
    • Comparing finger and forehead sensors to measure oxygen saturation in people with chronic obstructive pulmonary disease
      Wilson, Stephanie; Cecins, Nola; Jenkins, Sue; Melang, Michelle; Singh, B.; Hill, Kylie (2013)
      Background and objective: Oxyhaemoglobin saturation of arterial blood is commonly measured using a finger sensor attached to a pulse oximeter (SpO2). We sought to compare SpO2 measured using finger and forehead sensors ...
    • Surface-atmosphere interactions in the thermal infrared (8 - 14um)
      McAtee, Brendon Kynnie (2003)
      Remote sensing of land surface temperature (LST) is a complex task. From a satellite-based perspective the radiative properties of the land surface and the atmosphere are inextricably linked. Knowledge of both is required ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.