The activities performed in an industrial environment are classified as high risk because they are in continuous contact with machinery and mechanical and/or chemical equipment or because they perform activities that are hazardous to health, repetitive or require high physical effort. In addition to the above, there are environmental conditions in certain industrial sectors, such as high noise levels and the presence of solid and liquid particles in the air.
Due to the high levels of exposure of industrial workers, there is a high risk of occupational diseases. The density of occurrence of occupational diseases can be considered as a public health problem and also as a problem for the industrial productive sector of a country.
One of the ways to mitigate the consequences of occupational diseases is through the collection of risk indexes for each operator and his activity. Based on this information, practices, roles, schedules and even processes are modified. However, occupational safety experts carry out these occupational risk studies based on questionnaires and visual checks, which adds subjectivity and reduces the reliability of the results obtained. On the other hand, there are assessments based on medical instrumentation, however, this is considered invasive for the required purpose, since the work environment is completely modified due to the fact that the operator under analysis must emulate his activity within a laboratory environment.
Based on the above, the present project proposes the development and validation of a technological system based on Internet of Medical Things (IoMT) and Sensors architectures to acquire biomechanical variables and calculate in an objective way the occupational risk indexes. The system will consist of: an smart vest that will capture the variables through sensors; a communication hub that will acquire the variables to send them to the cloud platform; finally, there will be a set of applications for processing, analytics and predictions of indicators applied to the measurement and prognosis of occupational risks.
Due to the high levels of exposure of industrial workers, there is a high risk of occupational diseases. The density of occurrence of occupational diseases can be considered as a public health problem and also as a problem for the industrial productive sector of a country.
One of the ways to mitigate the consequences of occupational diseases is through the collection of risk indexes for each operator and his activity. Based on this information, practices, roles, schedules and even processes are modified. However, occupational safety experts carry out these occupational risk studies based on questionnaires and visual checks, which adds subjectivity and reduces the reliability of the results obtained. On the other hand, there are assessments based on medical instrumentation, however, this is considered invasive for the required purpose, since the work environment is completely modified due to the fact that the operator under analysis must emulate his activity within a laboratory environment.
Based on the above, the present project proposes the development and validation of a technological system based on Internet of Medical Things (IoMT) and Sensors architectures to acquire biomechanical variables and calculate in an objective way the occupational risk indexes. The system will consist of: an smart vest that will capture the variables through sensors; a communication hub that will acquire the variables to send them to the cloud platform; finally, there will be a set of applications for processing, analytics and predictions of indicators applied to the measurement and prognosis of occupational risks.