No title
This thesis explores the feasibility of integrating a fall detection system into microprocessor-controlled prosthetic knees using onboard sensors, with a focus on optimizing machine learning models for real-time operational efficiency within the limited computational capacities of such devices. Initial investigations utilized the public UMAFall dataset to gain insights into fall detection methodol
