Wearable insole pressure sensors for automated detection and classification of slip-trip-loss of balance events in construction workers

Maxwell Fordjour Antwi-Afari, Heng Li, Joon Oh Seo, Sang Hyun Lee, David John Edwards, Arnold Yu Lok Wong

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

A fall on the same level is a leading causes of non-fatal injuries among construction workers. Previous research reveals that such incidents are associated with slip, trip and loss of balance (STL) events often caused by unsafe site conditions (e.g., slippery floors, obstacles on the path and uneven surfaces). Consequently, detecting STL events enable site management to identify these hazards and employ suitable risk mitigation "control" measures. This research examined foot plantar pressure distribution for automated detection and classification of STL events using wearable insole pressure sensors. Three volunteers participated in a laboratory controlled simulated experiment that examined different types of STL events, while the corresponding foot plantar pressure data were collected from wearable insole pressure sensors. Diverse features (e.g., time- And frequency-domains, and spatial-temporal features) were extracted from the foot plantar pressure distribution data, which was used to associate different pressure patterns with each type of STL event. Four machine learning classifiers [i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)] were evaluated to select the best classifier. Cross validation results revealed that at approximately 85% of classification accuracy, the KNN classifier achieved the most accurate result using 0.64s window size, indicating a great potential to use the proposed approach to automate fall risk detection. Overall, this method would allow construction managers to understand how workers react to unsafe conditions associated with STL events, so as to minimize the fundamental causes of STL events and thus to reduce non-fatal fall injuries in construction.

Original languageEnglish
Title of host publicationConstruction Research Congress 2018
Subtitle of host publicationSafety and Disaster Management - Selected Papers from the Construction Research Congress 2018
EditorsChristofer Harper, Yongcheol Lee, Rebecca Harris, Charles Berryman, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages73-83
Number of pages11
ISBN (Electronic)9780784481288
DOIs
Publication statusPublished - 29 Mar 2018
EventConstruction Research Congress 2018: Safety and Disaster Management, CRC 2018 - New Orleans, United States
Duration: 2 Apr 20184 Apr 2018

Publication series

NameConstruction Research Congress 2018: Safety and Disaster Management - Selected Papers from the Construction Research Congress 2018
Volume2018-April

Conference

ConferenceConstruction Research Congress 2018: Safety and Disaster Management, CRC 2018
CountryUnited States
CityNew Orleans
Period2/04/184/04/18

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