Therabotics

 

Jesse Weisberg is a Washington University alumnus with a Masters of Engineering in Robotics and is the project leader of Therabotics. Therabotics’ vision is to improve the control of robotic prosthetics and orthotics, starting with the prosthetic hand. He and his teammates, Pin-Wei Chen and Sri Harsha Kondapalli, created a series of functional prototypes that present how computer vision can be used to make a prosthetic hand that is both easier to use and has more functionality than ever before. Therabotics combines eye-tracking technology with a sensor-enriched artificial intelligence system, so that the robotic device becomes aware of the user’s intended grasp. Jesse won the Sling Health summer fellowship in which he received a $5,000 stipend to work at CET (an innovation center that houses many Sling Health alumni) to further develop the technology that his team created during the academic year.

 


Here is a Q & A with Jesse about his experience:

Q. Why did you apply to the Sling Health Fellowship program?

A. I applied to the Sling Health Fellowship program because I was working on an idea that I was very passionate and excited about, and wanted to further my work.  Bringing this idea to fruition, even at the prototype stage, was going to require full-time dedication for a number of months.  I was excited to fully immerse myself in this project, and with the resources from Sling Health, I felt it was very possible. 

 



Jesse Weisberg (left), MS in Robotics and Pin-Wei Chen (right), PhD in Rehabilitation and Participation Science

Q. What problem were you trying to solve?

A. We found that robotic hand orthoses/prostheses are simply too difficult to use, and prevents most patients who need such a device to use one. Over 3 million people suffer from upper limb paralysis and need these robotic assistive devices. However, roughly 65% of this population does not use these devices. Rehabilitation researchers and a number of clinical rehabilitation specialists agree this is because existing solutions are too difficult to use.  From an engineering standpoint, we discovered the root of the issue stemmed from limitations in myoelectric control.  We set out to create an alternative control paradigm that fused useful sensory information additional to myoelectric control to make a more robust and intuitive robotic prosthetic hand.  

 


Sri Harsha Kondapalli, PhD in Electrical & Systems Engineering

 

 

 

Q. What support did you receive and benefit from the most?

A. Design reviews were very helpful in taking the expert opinions from professionals in the St. Louis area to constructively mold our idea into a viable product.  I also received constant support from those on the exec board of Sling Health, who were more than willing to connect me with anyone who could potentially help. There were a couple of times where these connections helped us in a big way in moving our idea from concept to creation.




Q. Can you describe what you learned and accomplished during the time of the fellowship?

 
A. I learned an incredible amount of entrepreneurship, technical innovation, the art of prototyping, interdisciplinary engineering teamwork, team leadership, resourcefulness, and how to pitch an idea.  Throughout countless design reviews, connecting with a wide array medical researchers and engineers in the St. Louis area, and making a relentless individual and team effort, we were able to develop a series of functional prototypes.  We learned a ton about rapid prototyping of embedded mobile robotic systems from the ground up, but also about breaking down very complex systems into a sequence of minimum viable products.  By the end of this fellowship, I had developed a method for moving a complex engineering project forward, taking all the necessary details into consideration.

Presentation on 2017 Demo Day

 

Q. What is the future of Therabotics? 

A. We are continuing to iterate through prototypes to make the system more practical, functional, and reliable. Technically, we are currently working on moving from using real-time object detection to a reinforcement learning architecture.  Such an architecture will allow grasps and movements of the prosthetic hand to be learned which each use, so that the functionality of the prosthetic adapts to the user’s movement over time.  Through this effort, our hope is to build a robotic prosthetic hand that can achieve truly fine control.

 


You can follow Jesse and Therabotics at this website: https://www.jesseweisberg.com/therabotics/

Demonstration of the Object-Specific Grasps with Eye Tracking by Jesse Weisberg

 

 

 

 

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