Framework for Personalized Real-Time Control of Hidden Temperature Variables in Therapeutic Knee Cooling

The paper formalizes, implements and evaluates a framework for personalized real-time control of inner knee temperature during cryotherapy after knee surgery. Studies have shown that the cryotherapy should be controlled depending on the individual patient's feedback on the cooling, which raises the need for smart personalized therapy. The framework is based on the feedback control loop that uses predicted instead of measured inner temperatures because measurements are not feasible or would introduce invasiveness into the system. It uses machine learning to construct a predictive model for estimation of the controlled inner temperature variable based on other variables whose measurement is more feasible - temperatures on the body surface. The machine learning method uses data generated from computer simulation of the therapeutic treatment for different input simulation parameters. A fuzzy proportional-derivative controller is designed to provide adequate near real-time control of the inner knee temperature by controlling the cooling temperature. The framework is evaluated for robustness and controllability. The results show that controlled cooling is essential for small-sized (and large-sized) knees that are significantly more (less) sensitive to the cooling compared to average-sized knees. Moreover, the framework recognizes dynamic physiological changes and potential changes in the system settings, such as extreme changes in the blood flow or changed target inner knee temperature, and consequently adapts the cooling temperature to reach the target value.