A portable remote emergency support system (PRESS) for worker safety in noisy working environments

See, Aaron Raymond; Kuo, Hsu-Chan; Yang, Chun-Hung

doi:10.1109/icce-taiwan49838.2020.9258194

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…An example of a functional safety standard put in place for the industry is the IEC 61508 [22]. The authors in Reference [23], developed a portable android platform that is designed to raise an alert on emergency cases regarding fall detection and a Bluetooth area positioning. It also incorporates a form of voice broadcast with warning messages to alert a fatigued worker in danger.…”

Section: Safety and Security: Establishing The Differencementioning

confidence: 99%

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

et al. 2021

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Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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Section: Safety and Security: Establishing The Differencementioning

confidence: 99%