Robohub.org
 

Smart soft robotics for stroke rehabilitation

by
09 November 2017



share this:

The culmination of work by Alistair C. McConnell (lead-researcher) through his PhD and the SOPHIA team, the Soft Orthotic Physiotherapy Hand Interactive Aid (SOPHIA) forms the foundation for our future research into Soft Robotic rehabilitation systems.

Through Alistair’s research, it became apparent that there was a lack of stroke rehabilitation systems for the hand, that could be used in a domestic environment and monitor both physical and neural progress. Alistair conducted a thorough review of the literature to fully explore the state of the art, and apparent lack of this type of rehabilitation system. This review investigated the development of both Exoskeleton and End-Effector based systems to examine how this point was reached and what gaps and issues still occurred.
From this review and discussions with physiotherapists, we developed an idea for a brain machine controlled soft robotic system. The “Soft Orthotic Physiotherapy Hand Interactive Aid” (SOPHIA) needed to provide rehabilitation aid in two forms, passive and active:
• Passive rehabilitation, where the subject performs their exercises, and this is reflected in a 3D representation on a screen, and all the data is stored for analysis.
• Active rehabilitation, where the subject attempts to open their hand and if the full extension is not achieved in a designated time, the system provides the extra force needed.

Through a grant from the Newton Fund we developed the SOPHIA system, which consists of a soft robotic exoskeleton with a set of pneunets actuators providing the force for the fingers of a hand to be fully extended, and an electropneumatic control system containing the required diaphragm pumps, valves and sensors in a compact modular unit.
The inclusion of a Brain Machine Interface (BMI) allowed us to use motor imagery techniques, where the electroencephalogram signal from the subject could be used as a trigger for the extension motion of the hand, augmenting the active rehabilitation.
We designed the system to accept input from two different BMI devices, and compared a wired, high-end BMI with a low-cost, wireless BMI. By applying machine-learning approaches we were able to narrow down the differences in these two input systems, and our approach enabled the inexpensive system to perform at the same-level as the high-end system.

You can find further information on the SOPHIA system and the current state of the art in robotic devices and brain-machine interfaces for hand rehabilitation in our recent journal publications.

SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid: 
https://www.frontiersin.org/articles/10.3389/fmech.2017.00003/full

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke: 
https://www.ncbi.nlm.nih.gov/pubmed/28597018




Adam Stokes is a Lecturer in the Institute for Micro and Nano Systems (IMNS) at The University of Edinburgh.
Adam Stokes is a Lecturer in the Institute for Micro and Nano Systems (IMNS) at The University of Edinburgh.





Related posts :



Estimating manipulation intentions to ease teleoperation

Introducing an intention estimation model that relies on both gaze and motion features.
06 December 2022, by and

Countering Luddite politicians with life (and cost) saving machines

Beyond aerial tricks, drones are now being deployed in novel ways to fill the labor gap of menial jobs that have not returned since the pandemic.
04 December 2022, by

Call for robot holiday videos 2022

That’s right! You better not run, you better not hide, you better watch out for brand new robot holiday videos on Robohub!
02 December 2022, by

The Utah Bionic Leg: A motorized prosthetic for lower-limb amputees

Lenzi’s Utah Bionic Leg uses motors, processors, and advanced artificial intelligence that all work together to give amputees more power to walk, stand-up, sit-down, and ascend and descend stairs and ramps.

Touch sensing: An important tool for mobile robot navigation

Proximal sensing often is a blind spot for most long range sensors such as cameras and lidars for which touch sensors could serve as a complementary modality.
29 November 2022, by

Study: Automation drives income inequality

New data suggest most of the growth in the wage gap since 1980 comes from automation displacing less-educated workers.
27 November 2022, by





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association