Health hazards in the construction industry

Harvey, B. H.

doi:10.1680/hic.48540.0004

Cited by 1 publication

(1 citation statement)

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“…The data collection was performed using several sensor devices-two PineTime smartwatches [40] (1) and two tags, one for measuring the acceleration (2), and one for the location (3). The data were collected using a Raspberry Pi computer (5), worn by the test subject, and a laptop (6).…”

Section: Hardware and Software Setupmentioning

confidence: 99%

Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety

Danilenka,

Sowiński,

Rachwał

et al. 2023

Electronics

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Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety.

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Section: Hardware and Software Setupmentioning

confidence: 99%