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Towards a Data-Driven Approach to Injury Prevention in Construction

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

The research discussed in this paper is part of a project directed at increasing productivity in construction through mitigating the risk of Musculoskeletal Disorders (MSD). Postures and activities recognition through motion capturing techniques have shown promising potential for monitoring, assessing, and reducing such risks. Current motion sensing systems require a complex whole-body senor placement to capture and recognize construction activities, which limits the practicality and requires great computational effort. This challenge can be addressed through using a machine learning approach that recognizes specific activities from human motion data. The feasibility of reducing the computational effort through using fewer sensors rather than whole-body sensor placement was assessed through a case study. Five sensors were placed in targeted motion areas. The authors propose a novel automatic model configuration process to improve recognition performance under the selected sensor placement. It is based on designing optimal combination of data segmentation window size, feature sets, and classification algorithms for a specific set of injury-prone construction activities. The proposed approach achieved an average overall recognition accuracy of 0.81 and 0.74 for two sets of activities. The recognition model operation time is also reduced to less than 0.01 s under the proposed approach. In this initial case study, the model configuration process was developed iteratively based on the output from the test case. In subsequent efforts, the authors will develop a generic activity recognition model with predefined rules and criteria. This will further accelerate and automate the model configuration process.

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Notes

  1. 1.

    https://www.r-project.org/about.html.

  2. 2.

    The experiment result is not attached in the paper due to limited spaces, the authors can provide the original result upon request.

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Authors and Affiliations

  1. The Pennsylvania State University, State College, PA, 16802, USA

    Junqi Zhao & Esther Obonyo

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  1. Junqi Zhao
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  2. Esther Obonyo
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Correspondence to Esther Obonyo .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Zhao, J., Obonyo, E. (2018). Towards a Data-Driven Approach to Injury Prevention in Construction. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_20

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