IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks.

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.

Accurate Real-Time Joint Torque Estimation for Dynamic Prediction of Human Locomotion.

Inverse dynamics is a common tool for determining human joint torques during walking. The traditional approaches rely on ground reaction force and kinematics measurements prior to analysis. A novel real-time hybrid method is proposed in this work by integrating a neural network and dynamic model that only requires kinematic data.

Accurate Real-time Phase Estimation for Normal and Asymmetric Gait.

An accurate real-time gait phase estimator for normal and asymmetric gait is developed by training and testing a time-delay neural network on gait data collected from six participants during treadmill walking. The trained model can generate smooth and highly accurate predictions of the gait phase with a root mean square error of less than 3.48% and 4.

Ultra-Robust Real-Time Estimation of Gait Phase.

An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.

Balance strategy in hoverboard control.

This study examines how people learn to perform lower limb control in a novel task with a hoverboard requiring to maintain dynamic balance. We designed an experiment to investigate the learning of hoverboard balance and two control strategies: A hip strategy, which mainly uses hip movements to change the angle of the foot, and an ankle strategy relying more on ankle motion to control the orientation of hoverboard plates controlling the motion. Motor learning was indicated by a significant [Formula: see text]% decrease in the trial completion time (p < 0.