Smart Estimation of Vertical Inertial Dynamics for Inertia Compensation in Medical and Space Training Systems

Gait rehabilitation systems often neglect the compensation of vertical inertial forces generated by body motion, leading to deviations in unloading force and disrupting natural gait patterns. This study presents a smart and cost-effective method for estimating vertical inertial dynamics to enable realtime inertia compensation in both medical rehabilitation and space training systems. Utilizing readily available sensors, such as those in smartphones, we implement a Gait Phase Estimation (GPE) algorithm to infer vertical displacement, velocity, and acceleration. These estimates allow robotic gait trainers and simulated gravity systems to dynamically counteract inertial effects without the complexity of traditional multi-sensor platforms. The proposed technique preserves the user's natural movement while reducing hardware and calibration overhead. Validation experiments using human motion data demonstrate the approach's accuracy and feasibility. Furthermore, we explore its potential application in NASA's Active Response Gravity Offload System (ARGOS), enabling realistic microgravity simulation. This dual-use solution offers a promising pathway for enhancing both clinical gait therapy and astronaut training environments.