Fact & Feeling introduces a new control intelligence in healthcare facilities
Considering the fact that healthcare facilities consist of different areas with specific requirements, the control logic must respond flexibly to changing room contexts and usage scenarios. Our IoT solution makes this possible – automatically, priority-driven, and health-oriented. The core principles of our control logic:
Conflict-sensitive and priority-based decision-making
Measures to improve indoor environmental quality often do not act in isolation but influence multiple environmental factors at once. For automated control to operate in a health-conscious way, it must assess which factors should be prioritized within the specific usage context. This decision-making logic is fully adaptable: facilities can define their own priorities for CO₂, temperature, humidity, noise, light, and more – tailored to each room type, time of day, or operational phase. The system reliably applies these rules – automatically, transparently, and flexibly. Our control logic identifies potential conflicts between the involved factors and automatically selects the action with the least negative impact – based on the individually defined health priorities.
Context-aware control
The requirements for indoor climate and environmental conditions vary significantly depending on the usage situation. Our control logic therefore automatically takes into account relevant contexts such as time of day, room type, usage scenario, or seasonal conditions. Whether it’s patient sleep, a therapy unit, or office work – the system dynamically adjusts its behavior. This prevents, for example, cold outdoor air or disturbing noise from triggering automated actions during nighttime rest.
Transparent decision-making
Even with automated control, transparency remains a key factor. In situations where multiple options appear equally effective or an action is not clearly beneficial, the system provides active guidance and suggests actions. Users can be notified and involved in the decision-making process – for example, to decide whether ventilation should occur despite a critical CO₂ level if the outdoor temperature is low. This ensures that control remains understandable and user-centered at all times.
Outcome
An intelligent control system for indoor quality that is individually configurable, respects health criteria, and dynamically adapts to various usage scenarios – from care and therapy to administration.
