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Concordia doctoral candidate wins a Prix Relève étoile from the Fonds de recherche du Québec

Soroosh Shahtalebi received the Louis-Berlinguet award for his work on assistive devices for patients with pathological hand tremors
August 3, 2020
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By School of Graduate Studies

Soroosh Shahtalebi: “Our research is about trying to enhance the reliability and performance of assistive devices.” Soroosh Shahtalebi: “Our research is about trying to enhance the reliability and performance of assistive devices.”

The Fonds de recherche du Québec recently announced its latest recipients of the Prix Relève étoile, the distinguished research awards that identify promising graduate students and other early-career researchers within Quebec and recognizes them for their innovative work.

Concordia’s Soroosh Shahtalebi has been awarded the Louis-Berlinguet prize from the Fonds de recherche du Québec – Nature et technologies (FRQNT). He is being recognized for his article “PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor Using Recurrent Neural Network Models,” which appeared earlier this year in the open-access journal Scientific Reports, published under Nature Research.

“This research publication marks the end of one study, but it also represents an exciting trajectory for Mr. Shahtalebi, a potential advancement that could improve assistive devices,” explains Michael Verwey, advisor for fellowship development at Concordia’s School of Graduate Studies.

“And, more generally, it provides a critical foundation for future research at the Gina Cody School of Engineering and Computer Science.”

The article is also a prime example of successful collaborations being led by Concordia researchers, as it brings together contributors from New York University, Western University and the London Health Sciences Centre.

Shahtalebi is currently a doctoral candidate at the Concordia Institute for Information Systems Engineering under the supervision of associate professor Arash Mohammadi. Together, their work at the Intelligent Signal and Information Processing Laboratory is applying machine learning solutions to problems in medicine and health care.

Rehabilitation technologies for neurological disorders

The world population of people over the age of 60 is expected to increase to nearly 2.1 billion by the year 2050, up from 962 million in 2017. Without discovery and innovation, the parallel rise in age-related disorders could result in a worldwide health crisis. To help address this, Shahtalebi characterized and trained neural network models to better diagnose, predict and estimate debilitating hand tremors in age-related neurological disorders.

“I’m investigating novel signal processing and machine learning solutions to enhance the performance and reliability of rehabilitation technologies,” he explains. “We’re looking to develop advanced computer-aided frameworks to assist physicians in the diagnosis and treatment of neurological disorders.”

Pathological hand tremors (PHT) are a common symptom of these disorders and can severely affect patient quality of life. Such tremors can make it nearly impossible for patients to feed and care for themselves, even if other symptoms are still relatively minor.

“Our research, PHTNet, is about trying to enhance the reliability and performance of assistive devices and expand the population of patients that could benefit from rehabilitation technologies,” Shahtalebi says. “Such advancements could help minimize caregiver burden and prolong the period of self-sufficiency for those suffering from these diseases.”

Enabling patients to take care of themselves for longer could help to both improve their quality of life and reduce the tremendous financial and emotional costs so often linked with these age-related diseases.

“While this award recognizes Mr. Shahtalebi, the Prix Relève étoile also reinforces that Concordia contributes essential expertise, innovation and infrastructure to Quebec’s rich research ecosystem. This impact is increasingly being appreciated and recognized,” Verwey notes.

The university also acknowledged Shahtalebi’s important research contributions in May, when the same article was selected for the Stand-Out Graduate Research Award.

 

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