# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
###### 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. CNN; dataset; emergency detection; human-centered; IoT; machine learning; smart factory +
Footnote †: journal:burns [3; 4]. Furthermore, falling objects account for several industrial injuries. For instance, a worker can get trapped in a factory as a result of falling objects, thus sustaining traumatic brain injuries, spinal injury, or broken bones, including death, in cases of larger objects falling from high above, as reported in a recent death in Korea of a worker due to falling objects from a broken crane, and about 6005 fatal accidents in China's construction industry resulting in 7275 deaths from 2010 to 2019 [5]. In Korea, 50% of the deaths resulting from workplace accidents in 2019 were aftermaths of workers falling or workers being struck by falling objects [6]. In 2018, it was estimated that such accidents accounted for about 25 trillion Korean Won (KRW) in damages [6]. This has lead to a revision in the occupational safety and health act in 2020 [7; 8]. Recently, Hyundai motors' factory in Ulsan, South Korea, halted its production after a worker died in an accident allegedly caused by retooling of its factory for the production of the new electric vehicle [9]. These accidents occur despite the growth in technologies and attendants benefits of the industry 4.0, thereby making research in the area of safe factory (smart or traditional) floor a burning issue. However, developing a testbed to encourage research in this direction is challenged by the cost of implementing experimental testbeds which has limited the amount of dataset available in this area. 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. IoT has become ubiquitous as it can be easily applied in different areas, including assisted living, private homes, communication and the industry as a whole. The application of IoT to the industry or production process is referred to as industrial IoT (IIoT) [10].