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Robotic Training and Kinematic Analysis of Arm and Hand after Incomplete Spinal Cord Injury: A Case Study.

About us

The MAHI Lab focuses on the design, manufacture, and evaluation of mechatronic or robotic systems to model, rehabilitate, enhance or augment the human sensorimotor control system. To this end, we employ analytical and experimental approaches from the field of dynamic systems and controls, with inspiration from human motor control and neuroscience. We are also engaged in the development of novel approaches for teaching dynamic systems and controls. See our recent videos.

Application of Levant’s Differentiator for Velocity Estimation and Increased Z-Width in Haptic Interfaces

A Lyapunov approach for SOSM based velocity estimation and its application to improve bilateral teleoperation performance

In many mechatronic applications, velocity estimation is required for implementation of closed loop control. Proportional-Integral control based differentiation has been proposed to estimate velocity in bilateral teleoperation. We propose a Second Order Sliding Mode (SOSM) based velocity estimation scheme for this application, since the SOSM approach is robust to small disturbances near the origin. Simulation results demonstrate the superior performance of the SOSM based velocity estimation over the PI-control approach for bilateral teleoperation in viscous environments.

On the Application of Levant's Differentiator for Velocity Estimation and Increased Z-Width in Haptic Interfaces

In this study, we demonstrate application of Levant's differentiator for velocity estimation from optical encoder readings. Levant's differentiator is a sliding mode control theory-based real-time differentiation algorithm. The application of the technique allows stable implementation of higher stiffness virtual walls as compared to using the common finite difference method (FDM) cascaded with low-pass filters for velocity estimation.

Vision-Based Force Sensing for Nanomanipulation

Efficacy of Shared-Control Guidance Paradigms for Robot-Mediated Training

Implementation and Analysis of Shared-Control Guidance Paradigms for Improved Robot-Mediated Training

Robot Assisted Rehabilitation - Spinal Cord Injury (SCI)

The objective of this research effort is to develop a rehabilitation robot and associated controllers to be used in both therapy and evaluation of subjects with incomplete spinal-cord injuries. We are working in collaboration with Dr. Gerard Francisco and Dr. Nuray Yozbatiran of TIRR-Memorial Hermann and UTHealth.

Robotic assisted rehabilitation-Stroke

Robotic systems provide numerous opportunities to improve the effectiveness of rehabilitation protocols and to lower therapy expenses for stroke patients. Because treatment intensity has a significant effect on motor recovery after stroke, the use of robotics has potential to automate labor-intensive therapy procedures. Additional potential advantages of robotics include bringing therapy to new venues including the home, new sensing capabilities for monitoring progress, and increased therapy efficiency with the possibility of group therapy.

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Mechatronics and Haptic Interfaces Lab at Rice University

Mechanical Engineering Department, MS 656, 713-348-2300
Bioscience Research Collaborative 980, Houston, TX 77030