Browsing by Author "Moore, Albert"

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    A data-driven approach to understand factors contributing to exoskeleton use-intention in construction
    Kim, Sunwook; Moore, Albert; Ojelade, Aanuoluwapo; Gutierrez, Nancy; Harris-Adamson, Carisa; Barr, Alan; Srinivasan, Divya; Nussbaum, Maury A. (SAGE, 2023-10-25)
    Work-related musculoskeletal disorders (WMSDs) remain an important heath concern for construction workers. Occupational exoskeletons (EXOs) are a new ergonomic intervention to control WMSD risk, yet their adoption has been low in construction. We explored contributing factors to EXO use-intention, by building a decision tree to predict the intention to try an exoskeleton using responses to an online survey. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors, and for a better prediction performance. Performance was assessed using four common metrics. The importance of variables in the final tree was calculated to understand which variable had a greater influence. The final tree had moderate prediction performance. Important variables identified were associated with opinions on EXO use, demographics, job demands, and perceived potential risks. The key influential variables were EXOs becoming standard equipment and fatigue reduction with EXO use. Practical implications of the findings are discussed.
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    A preliminary decision tree modeling of factors that determine readiness to use exoskeletons in construction
    Moore, Albert; Kim, Sunwook; Srinivasan, Divya; Nussbaum, Maury A.; Ojelade, Aanuoluwapo; Harris-Adamson, Carisa; Gutierrez Contreras, Nancy; Barr, Alan; Rempel, David (SAGE, 2021-09)
  • Understanding contributing factors to exoskeleton use-intention in construction: A decision tree approach using results from an online survey
    Kim, Sunwook; Ojelade, Aanuoluwapo; Moore, Albert; Gutierrez, Nancy; Harris-Adamson, Carisa; Barr, Alan; Srinivasan, Divya; Rempel, David M.; Nussbaum, Maury A. (Informa, 2023-12-12)
    Work-related musculoskeletal disorders (WMSDs) are a major health concern in the construction industry. Occupational exoskeletons (EXOs) are a promising ergonomic intervention to help reduce WMSD risk. Their adoption, however, has been low in construction. To understand the contributing factors to EXO use-intention and assist in future decision-making, we built decision trees to predict responses to each of three EXO use-intention questions (Try, Voluntary Use, and Behavioral Intention), using online survey responses. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors and improve prediction performance. The importance of variables in each final tree was calculated to understand which variables had a greater influence. The final trees had moderate prediction performance. The root node of each tree included EXOs becoming standard equipment, fatigue reduction, or performance increase. Important variables were found to be quite specific to different decision trees. Practical implications of the findings are discussed.