Intelligent system for occupational disease prevention in the industry

Abstract

Occupational disease is a global problem that causes various damages. Some of these can be avoided if the worker has a tolerance limit for performing work activities, thus avoiding fatigue. Performing a change of activity after a while, proposing a different physical effort, helps the body to recover, mitigating the risk of injury. A smart system is proposed for monitoring workers in the performance of their tasks, aiming to prevent occupational diseases. An IoT device equipped with an accelerometer sensor can be used to collect data on workers' activities. The data is evaluated by a system that implements activity recognition, with the focus is on limiting the maximum execution time. When identifying the tolerance limit for a task, the system issues an alert for an activity change. In the absence of another activity, a break period will be necessary to avoid the body. The results are promising and present an accuracy of 93.12% for the recognition of the activity. For the activity change, the accuracy reaches 85.36%. Based on the conducted computational experiment, the implementation of IoT in the workplace can improve health and safety at work.