During military duty, the soldiers are often exposed to extreme conditions and emergencies (such as fire, sailors in the water, combat, warship cruiser fighting, lost radio communication on the ship, etc.). The aim is always to handle the situation without the loss not only of equipment but especially of health or lives. Such situations are usually connected with stress, confusion, and time pressure. Thus, handling the situation can be difficult and requires decisions based on accurate and complete information. Our project’s focus is monitoring vital functions and overall stress by wearables and environmental sensors in extreme conditions. Thanks to modern technologies such as various wire(less) sensors, wearables, and artificial intelligence, we can help to better handle such situations. Environmental sensors (thermometers, barometers, humidity sensors, smoke detectors, etc.) give information about the environment and its changes. Wearables can measure the biological signals and motion of each soldier. All measured data are real-time processed, where their availability or quality, which may be degraded due to additive noise, is continuously monitored. The system adapts to reduced signal quality or the loss of modality and suggests the best alternative solution. Stress monitoring can be useful in military emergencies as well as in duty in general. Stress level can determine a soldier's overall fitness/readiness to engage in combat or other operations. Utilizing prepared (physically and mentally) soldiers increases overall mission success and minimizes loss. The novelty of the project lies mainly in the automatic selection of the most relevant biological/environmental modalities depending on their availability/quality and, thus, obtaining the maximum accurate information available at a given moment in each situation. Particularly, from each signal, specific features will be extracted and statistically evaluated to select the most robust and relevant ones for decision-making. Next, the impact of the quality and availability of the signals on the decision-making reliability will be assessed. Finally, the decision tree providing the best possible solution for a given situation will be developed. The developed signal processing framework will help to decide on aid/rescue priorities and/or similar issues. Only the decisions made using accurate and relevant information can be potentially reliable.