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Accelerometer-Based Activity Recognition in Construction.

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Title: Accelerometer-Based Activity Recognition in Construction.
Authors: Joshua, Liju1, Varghese, Koshy2
Source: Journal of Computing in Civil Engineering. Sep2011, Vol. 25 Issue 5, p370-379. 10p. 1 Illustration.
Subjects: Automation, Work sampling, Labor productivity, Accelerometers, Construction industry
Abstract: Recognizing the activities of workers helps to measure and control safety, productivity, and quality in construction sites. Automated activity recognition can enhance the efficiency of the measurement system. The present study investigates accelerometer-based activity classification for automating the work-sampling process. A methodology is developed for evaluating classifiers for recognizing activities based on the features generated from accelerometer data segments. An experimental study is carried out in instructed and uninstructed modes for classifying masonry activities by using accelerometers attached to the waist of the mason. Three types of classifiers were evaluated, and multilayer perceptron, a neural network classifier, gave the best results. A 50% overlap for data segments enhanced classifier performance. The study showed that the utilization of best features instead of all features did not affect the classification accuracy significantly but reduced the run time considerably. An accuracy of 80% was obtained with accelerometers attached at both sides of the waist in an uninstructed environment. The results from preliminary studies have shown the potential of the proposed method for automating the activity recognition in construction sites. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Accelerometer-Based Activity Recognition in Construction.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computing+in+Civil+Engineering%22">Journal of Computing in Civil Engineering</searchLink>. Sep2011, Vol. 25 Issue 5, p370-379. 10p. 1 Illustration.
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  Data: <searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Work+sampling%22">Work sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Labor+productivity%22">Labor productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Accelerometers%22">Accelerometers</searchLink><br /><searchLink fieldCode="DE" term="%22Construction+industry%22">Construction industry</searchLink>
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  Data: Recognizing the activities of workers helps to measure and control safety, productivity, and quality in construction sites. Automated activity recognition can enhance the efficiency of the measurement system. The present study investigates accelerometer-based activity classification for automating the work-sampling process. A methodology is developed for evaluating classifiers for recognizing activities based on the features generated from accelerometer data segments. An experimental study is carried out in instructed and uninstructed modes for classifying masonry activities by using accelerometers attached to the waist of the mason. Three types of classifiers were evaluated, and multilayer perceptron, a neural network classifier, gave the best results. A 50% overlap for data segments enhanced classifier performance. The study showed that the utilization of best features instead of all features did not affect the classification accuracy significantly but reduced the run time considerably. An accuracy of 80% was obtained with accelerometers attached at both sides of the waist in an uninstructed environment. The results from preliminary studies have shown the potential of the proposed method for automating the activity recognition in construction sites. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1061/(ASCE)CP.1943-5487.0000097
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      – Code: eng
        Text: English
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      – SubjectFull: Automation
        Type: general
      – SubjectFull: Work sampling
        Type: general
      – SubjectFull: Labor productivity
        Type: general
      – SubjectFull: Accelerometers
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      – SubjectFull: Construction industry
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              Text: Sep2011
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