With RFE, however, patients get a mental boost. They are asked to think about moving. At the same time, a practitioner flexes the wrist. The goal is to send a long latency response from the stretch that arrives in the brain at the exact time the thought happens, creating a neural signal. The result is a strong, combined response that zips back to the forearm muscles and moves the wrist.

It all happens in a span of approximately 40 to 60 milliseconds.

“Timing is everything. When the window is that small, it’s not easy for two people to match each other,” said Georgia Institute of Technology master’s graduate Lauren Lacey.

That’s why Lacey and a team of fellow Georgia Tech researchers created a mechanical device that takes people out of the process, replacing them with accurate computers. Their functional MRI-compatible hemiparesis rehab device creates a long latency stretch reflex at the exact time as a brain signal.

“It’s kind of like trying to fill a bucket with water,” explained Minoru Shinohara, an associate professor in the School of Applied Physiology and director of the Human Neuromuscular Physiology Lab. “Stroke individuals can only mentally fill it halfway. The machine pours in the rest to make it full.”

So far, the research team has worked only with healthy individuals in their study. Study participants lie on a bed with the arm extended beneath a pneumatic actuator tendon hammer. In order to simulate the weak signal created by hemiparesis individuals to move their wrist, a transcranial magnetic stimulator (TMS) is placed on the heads of these healthy individuals at a 45-degree angle. Milliseconds after the hammer taps the wrist’s tendon, the TMS creates a weak signal in the motor cortex. The responses overlap, produce and send a strong signal back to the arm, and the wrist moves.

The team has successfully varied the timing of the TMS signal and speed of the hammer to strike faster or slower depending on how much of a boost is needed to complement the brain signal. Now that the researchers have proven the viability of the TMS-actuator system, they will next work with stroke individuals.

“The device is designed to adapt to people whether they are hyper, normo or hyporeflexive,” said Lacey, who graduated in spring with a master’s degree from the George Woodruff School of Mechanical Engineering.

Also, because the machine is MRI-compatible, it will allow the team to study what is happening in the brain during rehab, opening the door for robotics.

“Once we fully understand what is happening mentally and physiologically, we should be able to create a robot that can reproduce successful rehabilitative exercises such as RFE,” said Jun Ueda, an associate professor in the School of Mechanical Engineering. “It appears that the timing is the critical piece of this exercise. Robots are great at timing, so the results are very promising for robotics.”

The Georgia Tech team was assisted by researchers at Japan’s Kagoshima University, Kazumi Kawahira, Megumi Shimodozono and Yong Yu, who originally performed clinical studies of conventional RFE. The device was presented at the Design of Medical Devices Conference in Minneapolis, Minnesota this spring.

This research was partially supported by the National Science Foundation (NSF) under sub-award EEC 0540834. Any conclusions expressed are those of the principal investigator and may not necessarily represent the official views of the NSF.

The researchers have paired a small humanoid robot with an Android tablet. Kids teach it how to play Angry Birds, dragging their finger on the tablet to whiz the bird across the screen. In the meantime, the robot watches what happens and records “snapshots” in its memory. The machine notices where fingers start and stop, and how the objects on the screen move according to each other, while constantly keeping an eye on the score to check for signs of success.

When it’s the robot’s turn, it mimics the child’s movements and plays the game. If the bird is a dud and doesn’t cause any damage, the robot shakes its head in disappointment. If the building topples and points increase, the eyes light up and the machine celebrates with a happy sound and dance.

“The robot is able to learn by watching because it knows how interaction with a tablet app is supposed to work,” said Georgia Tech’s Ayanna Howard, Motorola Foundation Professor in the School of Electrical and Computer Engineering who is leading the project. “It recognizes that a person touched here and ended there, then deciphers the information that is important and relevant to its progress.”

The robot analyzes the new information and provides appropriate social responses while changing its play strategy.

“One way to get robots more quickly into society is to design them to be flexible for end users,” said Hae Won Park, Howard’s postdoctoral fellow working closely on the project. “If a robot is only trained to perform a specific set of tasks and not able to learn and adapt to its owner or surroundings, its usefulness can become extremely limited.”

That flexibility is one reason Howard and Park see their robot-smart tablet system as a future rehabilitation tool for children with cognitive and motor-skill disabilities. A clinician could program the robot to cater to a child’s needs, such as turn taking or hand-eye coordination tasks, and then send the machine home.

Another benefit for rehab: parents don’t always have time or enough patience for repetitive rehabilitation sessions. But a robot never gets tired or bored.  

“Imagine that a child’s rehab requires a hundred arm movements to improve precise hand-coordination movements,” said Howard. “He or she must touch and swipe the tablet repeatedly, something that can be boring and monotonous after a while. But if a robotic friend needs help with the game, the child is more likely to take the time to teach it, even if it requires repeating the same instructions over and over again. The person’s desire to help their ‘friend’ can turn a five-minute, bland exercise into a 30-minute session they enjoy.”

In a new study, Howard and Park asked grade-school children to play Angry Birds with an adult watching nearby. Afterwards, the kids were asked to teach a robot how to play the game. The children spent an average of nine minutes with the game as the adult watched. They played nearly three times as long (26.5 minutes) with the robot. They also interacted considerably more with the robot than the person. Only 7 percent of their session with the adult included eye contact, gestures and talking. It was nearly 40 percent with the robot.

The next steps for the Georgia Tech team will include more games for the robot, including Candy Crush and ZyroSky. They will also recruit more children diagnosed with Autism Spectrum Disorder (ASD) and children with motor impairments to interact with the system. Their most recent study included two kids with ASD. Their interaction times with the adult were significantly less than those in the typically developing group. They were about the same with the robot. The findings were presented in June at the Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) 2014 Annual Conference in Denver.  

This research was partially supported by the National Science Foundation (NSF) under grant 1208287. Any conclusions expressed are those of the principal investigator and may not necessarily represent the official views of the NSF.