<![CDATA[Operational industry upstream oil and natural gas , especially in the environment off offshore , has risk high health and safety . One of the problems that need to be addressed get attention is possibility occurrence illness-related fatalities consequence change condition physiological and fatigue that is not detected in a way early . System monitoring health conventional in nature periodic , including pre-shift medical check-up , have limitations in give information condition worker in a way continuous during activity operational . This engineering practice aims designing system monitoring fitness worker offshore in a way real-time based Internet of Medical Things (IoMT) with utilise device wearable in the form of integrated smartwatch with online dashboard . The method used covering survey field , Focus Group Discussion (FGD) with doctor company , identification problems , studies literature , design systems , development and integration , testing , and Analysis and evaluation of fatality and illness data 2021–2025 used For identify profile risk health workers . Design results produce integrated architecture wearable devices , networks communication , server, dashboard , and early warning system . Physiological parameters main designed For monitored covering heart rate (HR), heart rate variability (HRV), saturation oxygen (SpO₂), and respiratory rate (RR), while blood pressure (BP) is used as a supporting parameter . In 2025 there will be 13 workers with category high based on Jakarta Cardiovascular Score , 17 workers recorded as smokers , 8 workers experience hypertension , and 2 workers experiencing diabetes mellitus . The results strengthen need integration profile risk health with monitoring physiological in a way continuous . The system is designed potential speed up identification abnormal and supportive conditions taking decision power medical and HSE in implementation of the fit to work program . However , validation quantitative in the form of sensor accuracy , latency , packet loss , sensitivity , specificity , and false alarm rate Still need done at the stage implementation field next .]]>
