This research investigates methodologies to reduce and implement negative inertia in robots for upper extremity diagnostics and rehabilitation. The robot’s responsiveness is enhanced by integrating accelerometers and Kalman filters into the control scheme, ensuring smoother physical human-robot interactions. A force gain that mimics negative inertia significantly improves system dynamics within the admittance control framework. Introducing dead zones for force and acceleration stabilizes responses at lower rendered inertia, crucial for handling spastic conditions. However, this research identifies a lack of standardized evaluation methods for negative inertia and highlights hardware constraints, such as bandwidth limitations, that restrict performance. Future research should focus on establishing evaluation standards and optimizing hardware to refine control precision. This work demonstrates the potential of advanced control strategies to optimize robotic rehabilitation, paving the way for more effective diagnostic and therapeutic interventions.