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USC demonstrates new robotic system to assess mobility after stroke

A new robotic tool developed by a team of experts in computer science and biokinesiology could help stroke survivors more accurately track their recovery progress.

Lead author Nathan Dennler, a computer science doctoral student, with the robotic arm, which provides precise 3D spatial information, and a socially assistive robot, which gives instruction and motivation throughout the assessment.

Each year more than 15 million people worldwide will have strokes, with three-quarters facing challenges such as impairment, weakness and paralysis in their arms and hands, according to the American Stroke Association.

While the old adage says “Use it or lose it,” for stroke survivors, this can be easier said than done.

Many people recovering from stroke rely on their stronger arm to complete daily tasks, even when the weaker arm has the potential to improve.

This can become a vicious cycle – the weaker arm is used less, so it becomes less functional. Breaking this habit, known as “arm non-use” or “learned non-use,” can improve strength and prevent injury.

But, determining how much a patient is using their weaker arm outside of the clinic is challenging. In a classic case of observer’s paradox, the measurement has to be covert for the patient to behave naturally.

Now, USC researchers have developed a novel robotic system for collecting precise data on how people recovering from stroke use their arms spontaneously. The first-of-its-kind method is outlined in a paper published in the November 15 issue of Science Robotics.

Using a robotic arm to track 3D spatial information, and machine learning techniques to process the data, the method generates an “arm non-use” metric, which could help clinicians accurately assess a patient’s rehabilitation progress.

A socially assistive robot (SAR) provides instructions and encouragement throughout the challenge.

“Ultimately, we are trying to assess how much someone’s performance in physical therapy transfers into real life,” said Nathan Dennler, the paper’s lead author and a computer science doctoral student.

The research involved combined efforts of researchers at USC’s Thomas Lord Department of Computer Science and the Division of Biokinesiology and Physical Therapy.

“This work brings together quantitative user-performance data collected using a robot arm, while also motivating the user to provide a representative performance thanks to a socially assistive robot,” says Maja Matarić, study co-author and Chan Soon-Shiong Chair and Distinguished Professor of Computer Science, Neuroscience, and Pediatrics.

“This novel combination can serve as a more accurate and more motivating process for stroke patient assessment.”

Additional authors are Stefanos Nikolaidis, an assistant professor of computer science; Amelia Cain, an assistant professor of clinical physical therapy, Carolee J. Winstein, a professor emeritus and an adjunct professor in the Neuroscience Graduate Program, and computer science students Erica De Guzmann and Claudia Chiu.

Mirroring everyday use

For this study, the research team recruited 14 participants who were right-hand dominant before the stroke. The participant placed their hands on the device’s home position—a 3D-printed box with touch sensors.

A socially assistive robot (SAR) described the system’s mechanics and provided positive feedback, while the robot arm moved a button to different target locations in front of the participant (100 locations in total). The “reaching trial” begins when the button lights up, and the SAR cues the participant to move.

In the first phase, the participants were directed to reach for the button using whichever hand came naturally, mirroring everyday use. In the second phase, they were instructed to use the stroke-affected arm only, mirroring performance in physiotherapy or other clinical settings.

Using machine learning, the team analyzed three measurements to determine a metric for arm non-use: arm use probability, time to reach, and successful reach. A noticeable difference in performance between the phases would suggest non-use of the affected arm.

“The participants have a time limit to reach the button, so even though they know they’re being tested, they still have to react quickly,” said Dennler. “This way, we’re measuring gut reaction to the light turning on – which hand will you use on the spot?”

Safe and easy to use

In chronic stroke survivors, the researchers observed high variability in hand choice and in the time to reach targets in the workspace.

The method was reliable across repeated sessions, and participants rated it as simple to use, with above-average user experience scores. All participants found the interaction to be safe.

Participants felt that the system could be improved through personalization, which the team hopes to explore in future studies, in addition to incorporating other behavioral data such as facial expressions and different types of tasks.

Crucially, the researchers found differences in arm use between participants, which could help healthcare professionals more accurately track a patient’s stroke recovery.

“For example, one participant whose right side was more affected by their stroke exhibited lower use of their right arm specifically in areas higher on their right side, but maintained a high probability of using their right arm for lower areas on the same side,” says Dennler.

“Another participant exhibited more symmetric use but also compensated with their less-affected side slightly more often for higher-up points that were close to the mid-line.”

As a physiotherapist, Cain said the technology addresses many issues encountered with traditional methods of assessment, which “require the patient not to know they’re being tested and are based on the tester’s observation which can leave more room for error”.

“This type of technology could provide rich, objective information about a stroke survivor’s arm use to their rehabilitation therapist,” says Cain.

“The therapist could then integrate this information into their clinical decision-making process and better tailor their interventions to address the patient’s areas of weakness and build upon areas of strength.”

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