Design, Development and Testing of a Unified Variable Impedance Controller for Transfemoral Knee-Ankle Robotic Prostheses

Lower limb amputation is a global health challenge, affecting millions of individuals and necessitating the development of advanced technologies to improve mobility and quality of life for amputees. Active lower limb prostheses have the potential to restore a more natural and efficient gait, restoring different locomotion activities and improving the quality of life of amputees. However, the development of active lower limb prostheses encounters challenges in the development of sophisticated control systems. This thesis presents the development of a unified variable impedance controller for walking at different inclines and velocities. A data-driven optimization approach was employed to model the physiological impedance of the knee and ankle joints as a function of the stride phase. The resulting controller does not necessitate either for subjects’ specific tuning or gait segmentation, modulating continuously the impedance to reproduce the kinematic and kinetic behavior of the human joints. The control algorithm was tested on an ankle prosthesis and a knee prosthesis on three different able-bodied subjects and benchmarked against a finite state machine controller. The presented control strategy outperformed the finite state machine control in achieving physiologically accurate kinematic and kinetics profiles for different locomotion activities

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients