Research from the University of Southern California was able to develop a new robotic system that enables them to gather accurate data regarding how people who are recovering from stroke spontaneously use their arms, shedding light on their post-stroke mobility.
Robotic System For Examining Post-Stroke Mobility
The method covers the use of a robotic arm for monitoring 3D spatial data and machine learning techniques for assessing the information. It then generated a metric for arm nonuse, which could aid clinicians in assessing the rehabilitation progress of a patient. A socially assistive robot (SAR) also grants encouragement and instructions all throughout.
Nathan Dennler, a computer science doctoral student and the lead author of the study that documents the novel innovation, explains that they are ultimately trying to see how a patient's physical therapy performance can be translated to real-life settings. Maja Matarić, a co-author of the study and a Chan Soon-Shiong Chair and Distinguished Professor of Computer Science, Neuroscience, and Pediatrics, explains that the work consolidates quantitative data on user performance that was gathered through a robotic arm. It does so while encouraging the user to grant a representative performance with the help of the SAR. Professor Matarić explains that the novel mix can become a more motivating and accurate way of assessing stroke patients.
As part of the study, 14 participants were recruited. These 14 participants were mainly right-hand dominant prior to experiencing stroke. They then positioned their hands over the home position of the device.
A SAR then described the mechanics of the system and offered positive feedback. This took place as the robotic arm moved a certain button to various location targets. This was done in front of the participants, and there were a total of 100 locations. The reaching trial then started with the light-up of the button. The participant was then cued by the SAR to move.
During the first phase, the participants were tasked to naturally reach the button using whichever hand they typically used. During the second phase, they had to use the arm affected by the stroke, which mirrors how they may perform during physiotherapy and other clinical environments.
The team then used machine learning for analyzing three measurements to come up with an arm nonuse metric. These three measurements were time to reach, probability, and successful reach. Significant differences across the phases would posit the affected arm's nonuse.
Among survivors of chronic stroke, it was observed that hand choice and time for reaching the targets had a high variability. The approach was also found to be reliable during repeated sessions, while participants also rated the system as easy to use.
The researchers also crucially discovered arm use differences across participants. Such data could be maximized by healthcare practitioners for accurately monitoring the recovery of a stroke patient.
The participants also feel that the system could become better if a touch of personalization could be added. The researchers hope to delve further into this in future study.
Amelia Cain, a clinical physical therapy assistant professor, explains that this kind of technology could offer objective and rich data regarding the arm use of a stroke survivor to their rehabilitation therapist. The therapist can then use the data for making clinical decisions and tailoring the interventions better to help the patient with the specific areas of weakness and strength.
ALSO READ : How to Recognize Signs of a Stroke
Stroke
Stroke mainly takes place when the blood supply to a certain brain region is blocked or when a brain blood vessel ends up bursting. In both cases, areas of the brain get damaged or even die. The condition could result in long-term brain damage or disability or even death.
This condition is considered a medical emergency that warrants immediate attention and treatment. Early intervention can reduce damage in the brain and other possible complications.
Symptoms of stroke include speech troubles, difficulties in understanding what others say, numbness or paralysis of the leg, arm, or face, vision problems in both or either eye, a headache, and walking troubles.
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