کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6479119 1428279 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identifying poses of safe and productive masons using machine learning
ترجمه فارسی عنوان
شناسایی حالت های مصالح ساختمانی ایمن و سازنده با استفاده از یادگیری ماشین
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
چکیده انگلیسی


- Masons' kinematics were classified accurately based on level of expertise.
- Twenty-one masons with varying levels of expertise took part in the study.
- Linear SVM provided the best performance in terms of accuracy and computational cost.
- Pose set classification can be used to train novice workers.

This paper presents a framework to classify work poses among groups of masons during the building of a standard wall of concrete masonry units. The experience of the group composed of masonry instructors and master masons averaged five times that of the other groups, their productivity was highest, and the loads on their joints were the lowest. Thus, they were deemed experts in this paper. Inertial measurement units (IMU) and video cameras were used to collect kinematic data of the masons, from which pose clusters were identified. A Support Vector Machine (SVM) algorithm was used to classify masons' poses into expert and inexpert classes based on the relative frequency of poses in the motions used to lay each of 945 masonry units. Two classification scenarios were tested. While both scenarios achieved similar levels of accuracy, 91.23% and 92.04% respectively, the processing time for binary classification was only 13 s compared to 523 s for inter-group multiclass SVM. Like characteristic vibration frequencies in machine diagnostics and system identification, the characteristic poses identified provide insight into differing methods between expert and less experienced masons. For example, results show that experts utilize fewer and more ergonomicaly safe poses, while being more productive, which indicates lower energy expenditure (less wasted motions). The classification method and the poses identified contribute knowledge to help develop affordable mason training systems that utilize IMU and video feedback to improve health and productivity of apprentice masons.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automation in Construction - Volume 84, December 2017, Pages 345-355
نویسندگان
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