The use of a brain-machine interface (BMI) in combination with powered exoskeletons can assist patients with lower limb disabilities to walk again. These neurorobotic systems are commonly based on motor imagery, but their performance may suffer from lack of user engagement in the task or from cognitive load due to multi-tasking. The present paper shows a novel algorithm based on the gamma spectral band, using the Stockwell transform and a set of smoothing filters, to assess the quality of and improve the decoding of motor imagery during the use of a BMI-Rex exoskeleton system. The results computed in a pseudo-online scenario reveal a high accuracy with a very low false positive ratio.

In this communication, a translational roadmap for a noninvasive Brain Machine Interface (BMI) system for rehabilitation is presented. This multi-faceted project addresses important engineering, clinical, end user and regulatory challenges. The goal is to improve the feasibility of at-home neurorehabilitation for patients with chronic stroke by providing a low-cost, portable, form fitting, reliable, and easy-to-use system. The proposed BMI system is also designed to enable direct communication between the end-user and clinician, allowing for continuous patient specific rehabilitation optimization.

Powered exoskeletons for gait rehabilitation and mobility assistance are currently available for the adult population and hold great promise for children with mobility limiting conditions. Described here is the development and key features of a modular, lightweight and customizable powered exoskeleton for assist-as-needed overground walking and gait rehabilitation. The pediatric lower-extremity gait system (PLEGS) exoskeleton contains bilaterally active hip, knee and ankle joints and assist-as-needed shared control for young children with lower-limb disabilities such as those present in the Cerebral Palsy, Spina Bifida and Spinal Cord Injured populations. The system is comprised of six joint control modules, one at each hip, knee and ankle joint. The joint control module, features an actuator and motor driver, microcontroller, torque sensor to enable assist-as-needed control, inertial measurement unit and system monitoring sensors. Bench-testing results for the proposed joint control module are also presented and discussed.

Efficient and intuitive interfacing of the interventionalist to the information and tools available from image-guided robotic assisted surgeries is required to achieve the full benefit of these technologies. Ongoing research has been performed into the use of forbidden region guided fixtures (FRVF) for human-in-the-loop control of image-guided procedures via haptic force-feedback devices (FFD). Although commercially available FFD provide sufficient degrees-of-freedom (DoF), collaborating clinicians, as well as the results of our previous work indicate that these systems are not completely intuitive for controlling fixed-point access interventional tool which have a remote center of motion. Within this context, we introduce a new FFD which is designed with the same DoF constraints as a fixed-point access interventional tool. The device is tested in a clinical simulation of a robot assisted trans-apical valve implantation under guidance from real-time magnetic resonance imaging. Pre-acquired real-time images are used in the clinical simulation to dynamically update the FRVF and therefore provide guiding forces to allow the operator to see the safe boundaries of operation via a visualization interface and physically feel them through the FFD. Inertial and gravity compensation and per DoF dynamic response of the physical prototype are validated and the frequency response of the system demonstrates it is adequate for tactile sensing. During clinical simulation the operator was successfully able to maneuver the tool within the safe path to the region of interest with the guidance of visual and force-feedback.