Abstract
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.
Highlights
The manufacturing industry is constantly challenged by issues, such as accidents on the factory floor
Since the convolutional neural network (CNN) was compared with machine learning (ML) algorithms, such as K-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and naive Bayes (NB), this section presents a brief description of the ML candidates
We proposed a CNN model for the emergency detection in the smart factory shop floor, as well as developed a simple testbed for the purpose of providing dataset for future research direction in this area
Summary
The manufacturing industry is constantly challenged by issues, such as accidents on the factory floor. Dataset availability is critical to research works especially in the development of neural networks for efficient detection and classification of various scenarios in the smart factory. Today, disruptive technologies, such as the Internet of Things (IoT) and machine learning (ML), have transformed everyday life and positively impacted various sectors of the human endeavor. The use of biomedical sensors can be used in keeping track of the worker’s physical activities, especially critical and extreme environmental conditions, such as hot rooms and places with high toxicity In such places, there is the need for emergency evacuation of workers, to reduce mortality and fatality rate. The authors in Reference [19] proposed industry 4.0-based Bluetooth beacons attached to the worker’s safety helmet to track workers entering danger zones and estimate the positions of workers in the event of an accident
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