Towards a Multimodal Automatic Equipment for Predictive Health Monitoring of Industrial Workers: A Usability Perspective

Ensuring the health and safety of workers in indus-trial environments is of utmost importance. In Brazil, Regula-tory Normatives (RNs) mandate Periodic Medical Examinations (PMEs), with the frequency of these examinations primarily determined by the level of risk to which workers are exposed. However, normally these examinations do not provide a real-time assessment of workers' health status. The aim of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal equipment capable of automatically collecting biomedical data, such as oxygen saturation level, body temperature, and heart rate of users. This equipment aids in monitoring the health of industry workers through Machine Learning (ML) algorithms, and can store biomedical data for ongoing research and tracking the health condition of workers. Preliminary findings evaluating 14 workers and 2 doctors suggest promising results using this equipment in industries. In terms of equipment usability, the workers reported the IPMA to be highly user-friendly through evaluation conducted through the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ), which also provided average scores above 85 and 1.9, respectively. Overall, the IPMA shows promising potential for improving safety and well-being in industrial workplaces, and it may also contribute to reducing worker absenteeism due to some illness.