The objective of this project to implement laser-induced graphene sensing in a robotic glove exoskeleton, aiming to optimize the high sensitivity properties of LIG/PDMS composites for a robotic application and further improve upon the usability and sensitivity of exisisting glove designs and serve as a test bed for cutting edge nanomaterials. Laser-induced graphene (LIG) can be synthesized by a one-step process through CO2­ laser treatment of commercial polyimide (PI) film in an ambient atmosphere, selectively converting PI to conductive graphene film.

To investigate the 'human' side of human-robot interactions, the MAHI Lab is looking to collaborators beyond engineering disciplines to improve the work we do. With Dr. Marcia Brennan in the Department of Religion, we are making connections to the deeply personal nature of injury, impairment, and rehabilitation to better understand the participants in our studies. Working as a literary artist, Dr.

Robotic exoskeletons can be effective tools for providing repetitive and high dose rehabilitation therapy. However, currently there is a lack of techniques to design therapy systematically using the myriad of subject-specific experimental data that is available from these devices. We envision an objective and systematic approach that combines experimental data with computational simulations for designing robot-assisted rehabilitation therapies.

Electromyographic (EMG) control interfaces have the potential to increase the effectiveness and accessibility of rehabilitation robotics to a larger population of impaired individuals, including those with no residual motion in their upper limb. Building on our previous work to characterize the surface EMG patterns of able-bodied and incomplete spinal cord injury (iSCI) subjects, we have developed a real-time controller for the MAHI EXO-II upper limb exoskeleton.

There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during training. These findings point to a need to incorporate a more complete understanding of human motor learning principles when designing haptic interactions with the trainee.

Robotic rehabilitation exoskeletons are particularly valuable in therapy because they leverage robotic devices' unique potential for accurate and repeatable movements, and quantitative measurement in position and force domains. In addition to coordinated movement capabilities and functional workspace requirements such as range of motion (ROM) and torque required for ADL, a rehabilitation robot must possess quantitative measurement capabilities for evaluation, which requires high quality position sensing, good backdrivability, and backlash-free operation.

Providing minimal assistance to neurologically impaired individuals only becomes possible when the subject's functional capability is known.  In this research we introduce a minimal assist-as-needed (mAAN) controller which utilizes sensorless force estimation to determine subject inputs as a function of time, before providing a corresponding assistance with adjustable ultimate bounds on position error.

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.

Robotic rehabilitation for stroke patients has been an active field of research since the 1990s. There has been many studies focusing on mechanical design of robotic devices, design of software and interfaces for the patients and therapists, identifying quantitative and objective measures for motor improvement, and developing different operating modes/scenarios for the devices. However, a unified set of robotic (based on data captured by the robotic device) motor function improvement measures still does not exist.