Real-Time Prediction of Leg Joint Kinematics during Steady-State Walking and Task Transitions

Amputations and other impairments heavily impact human locomotion. To compensate for these disabilities, advanced devices such as active orthoses and prostheses are being developed with the aim of restoring locomotor function. To achieve this goal, the control of such technologies has been identified as a key research challenge. A reference kinematic profile for the affected or missing joints is often useful for device control. This paper introduces a novel method for estimating reference kinematics of leg joints from kinematic signals of the contralateral leg. This method leverages fusion of two different estimators - i.e. Complementary Limb Motion Estimation and Adaptive Oscillators - which are combined through the use of a logistic function. The method is then applied to estimate the kinematics of a hip joint, and the performance is compared with that of each individual estimator during walking. The correlation between the estimated signal and the ground truth is assessed on a cycle-by-cycle basis. During transitions, our new method behaves similarly to Complementary Limb Motion Estimation. After a few cycles, the method shifts towards Adaptive Oscillators to achieve a better correlation during quasi-steady-state locomotion.