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dc.contributor.authorFang, Yi-Choen_US
dc.contributor.authorDzeng, Ren-Jyeen_US
dc.date.accessioned2019-04-03T06:47:27Z-
dc.date.available2019-04-03T06:47:27Z-
dc.date.issued2014-01-01en_US
dc.identifier.issn1877-7058en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.proeng.2014.10.539en_US
dc.identifier.urihttp://hdl.handle.net/11536/136440-
dc.description.abstractThe construction industry accounts for nearly half of all industry-related fatalities in Taiwan. Identified as the leading cause of such fatalities for several decades, falls also contribute to almost half of work-related fatalities. Given the strenuous nature of construction work, workers are prone to loss of awareness and balance, increasing the safety risk and number of fall accidents. Previous studies have indicated that loss of awareness may be the major cause of occupational injuries or fatalities, and identified the strong correlation between falls and loss of balance. Thus, real-time monitoring of the mental and balance conditions of workers may help identify fall portents, and thus prevent falls from happening. This paper describes a framework for developing a personal safety monitoring system based on a smartphone, which receives external signals wirelessly from motion sensors and brain wave sensors attached to a vest and inside a helmet, and transmit these signals to a monitoring server for further analysis. This paper also presents an experiment with preliminary findings regarding the detection of fall portents, using the internal motion sensors of a smartphone. In the experiment, participants performed a tiling task on a scaffold under four physiological statuses. We identified the fall portents based on the self-awareness of the participants, hazardous actions performed by the participants, and outsider observations by experiment administrators. An accelerometer-based threshold algorithm was tested, and its performance was evaluated against the identified fall portents. The results indicated that the work-related motions had a limited impact on the detection algorithm. The accuracy for the sleepiness, fatigue, normal, and inebriation statuses were 92.3%, 90.4%, 77.3%, and 68.8%, respectively. The algorithm exhibited an overall accuracy of 86%, thus, we conclude that using a smartphone to detect fall portents in a working scenario is feasible, and deserves further investigation. (C) 2014 Published by Elsevier Ltd.en_US
dc.language.isoen_USen_US
dc.subjectaccelerometeren_US
dc.subjectconstruction safetyen_US
dc.subjectfall portent detectionen_US
dc.subjectsmartphoneen_US
dc.subjectthreshold algorithmen_US
dc.titleA smartphone-based detection of fall portents for construction workersen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1016/j.proeng.2014.10.539en_US
dc.identifier.journalCREATIVE CONSTRUCTION CONFERENCE 2014en_US
dc.citation.volume85en_US
dc.citation.spage147en_US
dc.citation.epage156en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000371314000018en_US
dc.citation.woscount2en_US
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