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Accelerometer-based activity recognition in construction
Published in
2011
Volume: 25
   
Issue: 5
Pages: 370 - 379
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. © 2011 American Society of Civil Engineers.
About the journal
JournalJournal of Computing in Civil Engineering
ISSN08873801
Open AccessNo
Concepts (23)
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    ACCELEROMETER DATA
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    Activity recognition
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    Analysis
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    Classification accuracy
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    CLASSIFIER PERFORMANCE
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    CONSTRUCTION MANAGEMENT
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    Construction sites
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    Data collection
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    DATA SEGMENT
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    Experimental studies
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    Measurement system
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    Multi layer perceptron
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    NEURAL NETWORK CLASSIFIER
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    Runtimes
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    Acceleration
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    Automation
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    Civil engineering
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    Neural networks
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    Occupational risks
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    Personnel
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    Productivity
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    Project management
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    Accelerometers