Publications by authors named "Mohammad Shushtari"

Article Synopsis
  • The study presents a new method for evaluating human-robot interaction in lower limb exoskeletons, focusing on muscle activity and interaction torque through a visualization called the interaction portrait (IP).* -
  • It compares two advanced control systems for exoskeletons—hybrid torque controller (HTC) and adaptive model-based torque controller (AMTC)—highlighting their effectiveness in reducing energy expenditure compared to a traditional time-based controller (TBC) during walking.* -
  • The analysis reveals that HTC fosters a strategy where users rely more on the exoskeleton, while AMTC encourages user engagement, demonstrating that IP can uncover distinct co-adaptation strategies beneficial for optimizing user experience and rehabilitation.*
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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.

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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.

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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.

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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.

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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.

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