An uncertain predictive model in detecting the gait phase and the joint trajectory on lower-limb exoskeleton systems. Current deterministic machine learning methods usually provide high accuracy but do not scale to sensor noise and inter-subject variation by providing confidence estimation. To overcome such shortcomings, an uncertainty-sensitive Gaussian Process Regression model is developed to learn nonlinear biomechanical dynamics as well as offer predictive uncertainty to control based on confidence. Multi-subject wearable sensor gait data was subjected to subject-independent evaluation of the framework. The implementation was done in a Python based environment with scientific computing libraries to do probabilistic modeling and analysis of performance. In an experimental study, predictive accuracy of 94.8 % was proven, as well as a consistent regression across individuals. The introduction of variance-based torque adaptation improves the level of safety and real-time responsiveness. The suggested solution is advantageous to the population with mobility limitations since it facilitates intelligent, adaptive, and dependable assistive control in wearable exoskeleton systems.
Wearable Robotic Exoskeletons, Human Gait Modeling, Subject-Independent Generalization, Nonlinear Biomechanical Dynamics, Uncertainty-Aware Predictive Control.
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