TY - GEN
T1 - Automated detection of near-miss fall incidents in iron workers using inertial measurement units
AU - Yang, Kanghyeok
AU - Aria, Sepi
AU - Ahn, Changbum R.
AU - Stentz, Terry L.
PY - 2014
Y1 - 2014
N2 - Accidental falls (slips, trips, and falls from height) are the leading cause of death and injury on a construction site. Assessing the risk of such falls, therefore, becomes a fundamental step toward reducing these accidents. However, the quantitative assessment of a fall risk for construction workers is still very challenging because of sparse data related to fall accidents. Recently, there has been a growing interest in the identification of near-miss fall accidents to utilize them as supplementary data for fall-risk assessments. Current documentation for near-miss fall accidents is based on workers' self-reporting, a fact that adds variability to the data. In response, this research introduces a method that can detect near-miss fall incidents based on inertial measurement units (IMUs). A preliminary laboratory experiment collects data on ironworkers' typical movements, postures, and near-miss fall accidents. Workers' postures and movements are recognized through supervised classification algorithms; near-miss fall incidents during the classified posture/movement are quantifiably detected based on the time-series anomaly detection approach. Such research helps to identify the possibility of fall accidents more precisely according to worker's activity data. Additionally, documenting near-miss fall data provides quantitative data for ironworkers' fall-risk assessment, a significant step forward in the field.
AB - Accidental falls (slips, trips, and falls from height) are the leading cause of death and injury on a construction site. Assessing the risk of such falls, therefore, becomes a fundamental step toward reducing these accidents. However, the quantitative assessment of a fall risk for construction workers is still very challenging because of sparse data related to fall accidents. Recently, there has been a growing interest in the identification of near-miss fall accidents to utilize them as supplementary data for fall-risk assessments. Current documentation for near-miss fall accidents is based on workers' self-reporting, a fact that adds variability to the data. In response, this research introduces a method that can detect near-miss fall incidents based on inertial measurement units (IMUs). A preliminary laboratory experiment collects data on ironworkers' typical movements, postures, and near-miss fall accidents. Workers' postures and movements are recognized through supervised classification algorithms; near-miss fall incidents during the classified posture/movement are quantifiably detected based on the time-series anomaly detection approach. Such research helps to identify the possibility of fall accidents more precisely according to worker's activity data. Additionally, documenting near-miss fall data provides quantitative data for ironworkers' fall-risk assessment, a significant step forward in the field.
KW - Construction Safety
KW - Fall Accidents
KW - Inertial Measurement Unit
KW - Wearable Wireless Sensor Network
UR - http://www.scopus.com/inward/record.url?scp=84904632942&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904632942&partnerID=8YFLogxK
U2 - 10.1061/9780784413517.0096
DO - 10.1061/9780784413517.0096
M3 - Conference contribution
AN - SCOPUS:84904632942
SN - 9780784413517
T3 - Construction Research Congress 2014: Construction in a Global Network - Proceedings of the 2014 Construction Research Congress
SP - 935
EP - 944
BT - Construction Research Congress 2014
PB - American Society of Civil Engineers (ASCE)
T2 - 2014 Construction Research Congress: Construction in a Global Network, CRC 2014
Y2 - 19 May 2014 through 21 May 2014
ER -