Computer Tool Can Track Stroke Rehabilitation to Boost Recovery | NYU Langone News
Source: https://nyulangone.org/news/computer-tool-can-track-stroke-rehabilitation-boost-recovery
Archived: 2026-04-23 17:10
Computer Tool Can Track Stroke Rehabilitation to Boost Recovery | NYU Langone News
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A
sensor-equipped computer program can accurately identify and count arm movements in people participating in
stroke rehabilitation
, a new study shows. Now that it can do so, the next step, say the study authors, is to use the tool to define the intensity of movements that bring about the greatest recovery in patients’ ability to move independently and take care of themselves after a
stroke
.
The urgency of the work proceeds from the fact that arm mobility, as well as mobility in other limbs, is seriously reduced in more than half of stroke survivors. Each year, nearly 800,000 Americans experience a stroke, according to estimates from the U.S. Centers for Disease Control and Prevention.
Led by researchers at NYU Grossman School of Medicine, the study showed that the tool, developed at New York University and called PrimSeq, was 77 percent effective in identifying and counting the number of arm motions prescribed during rehabilitation exercises for patients who had a stroke. Sensors strapped to the arms and back were used to track movements in three dimensions. The developers say they plan further testing on more patients who had a stroke to refine their computer model, cut down on the number of sensors needed, and then develop a smaller prototype device that could be worn on the arm and upper body.
“Our study demonstrates that a digital tool, which is being designed to serve the same function as a smartwatch, is highly accurate in tracking the intensity of patients’ movements during stroke rehabilitation therapy,” says co-senior investigator
Heidi Schambra, MD
, an associate professor in the Departments of
Neurology
and
Rehabilitation Medicine
at NYU Langone.
“Such an aid is desperately needed because counts made from video recordings or other wearable sensors do not offer standardized measures of precisely how much rehabilitation exercise each patient is receiving,” says Dr. Schambra. “Any improvements in exercise ‘dose’ received must be based on accurate, automated measures of the type and number of arm movements involved in a given exercise.”
Previous research in animals suggests that intense exercise of the upper body can promote
recovery after stroke
. However, research in humans shows that people who had a stroke receive on average one-tenth of the exercise training proven effective in animals. This, researchers say, is mostly because there was no easy way—until the development of PrimSeq—to accurately track their arm movements.
Published in the journal
PLOS Digital Health
online June 16
, the new study recorded the upper body movements of 41 adults who had a stroke while they performed routine rehabilitation exercises for regaining use in the arms and hands. Exercises and arm movements involved patients feeding themselves with a fork and grooming themselves with a comb.
More than 51,616 upper body movements were recorded from 9 sensors, with the digital recordings of each arm movement then matched to functional categories, such as whether the movement involved reaching for an object or holding it still.
Artificial intelligence (machine learning) software was then programmed to detect patterns within the data and tie these patterns to specific movements. The resulting PrimSeq tool was then tested on a separate group of eight patients who had a stroke who wore the sensors while performing various exercises.
PrimSeq was then used to see if it could accurately identify 12,545 of their recorded movements according to their function. The program was successful in accurately assessing the majority of movements in patients, all of whom had mild to moderate arm impairments from stroke.
“PrimSeq has state-of-the-art performance in terms of identifying and counting functional movements in stroke patients, and we are gathering more data to continue increasing its accuracy,” says co-senior investigator Carlos Fernandez-Granda, PhD, an associate professor of mathematics and data science at New York University.
“As our research seeks to find optimal levels of training intensity needed for recovery, I would assert that our tool is very promising for clinical use, since the alternative is not having accurate counts at all,” says Dr. Schambra. “If further experiments prove successful, we will of course be testing the system in clinical trials.”
The authors intend to make PrimSeq freely available to stroke rehabilitation experts worldwide and have already
posted their data used to construct the program
online.
Funding for the study was provided by National Institutes of Health grants R01LM013316, K02NS104207, and NCATS UL1TR001445. Additional funding support was provided by National Science Foundation grant NRT-HDR1922658 and American Heart Association–Amazon Web Service grant 19AMTG35210398.
Besides Dr. Fernandez-Granda and Dr. Schambra, other NYU Langone and NYU researchers involved in this study are co-lead investigators Avinash Parnandi and Aakash Kaku, Anita Venkatesan, Natasha Pandit, Audre Wirtanen, Haresh Rajamohan, and Kannan Venkataramanan. Another study co-investigator is Dawn Nilsen at Columbia University in New York.
Media Inquiries
David March
Phone: 212-404-3528
david.march@nyulangone.org
Our Experts
Heidi Schambra, MD
Care & Services
Neurology
Conditions
Stroke
Departments & Institutes
Department of Neurology
Rusk Rehabilitation Education & Research
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or call 844-698-7243.
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We use cookies and similar tools to give you the best website experience. By using our site, you accept our
Websites Privacy Policy
.
If you need help accessing our website, call 855-698-9991
A
sensor-equipped computer program can accurately identify and count arm movements in people participating in
stroke rehabilitation
, a new study shows. Now that it can do so, the next step, say the study authors, is to use the tool to define the intensity of movements that bring about the greatest recovery in patients’ ability to move independently and take care of themselves after a
stroke
.
The urgency of the work proceeds from the fact that arm mobility, as well as mobility in other limbs, is seriously reduced in more than half of stroke survivors. Each year, nearly 800,000 Americans experience a stroke, according to estimates from the U.S. Centers for Disease Control and Prevention.
Led by researchers at NYU Grossman School of Medicine, the study showed that the tool, developed at New York University and called PrimSeq, was 77 percent effective in identifying and counting the number of arm motions prescribed during rehabilitation exercises for patients who had a stroke. Sensors strapped to the arms and back were used to track movements in three dimensions. The developers say they plan further testing on more patients who had a stroke to refine their computer model, cut down on the number of sensors needed, and then develop a smaller prototype device that could be worn on the arm and upper body.
“Our study demonstrates that a digital tool, which is being designed to serve the same function as a smartwatch, is highly accurate in tracking the intensity of patients’ movements during stroke rehabilitation therapy,” says co-senior investigator
Heidi Schambra, MD
, an associate professor in the Departments of
Neurology
and
Rehabilitation Medicine
at NYU Langone.
“Such an aid is desperately needed because counts made from video recordings or other wearable sensors do not offer standardized measures of precisely how much rehabilitation exercise each patient is receiving,” says Dr. Schambra. “Any improvements in exercise ‘dose’ received must be based on accurate, automated measures of the type and number of arm movements involved in a given exercise.”
Previous research in animals suggests that intense exercise of the upper body can promote
recovery after stroke
. However, research in humans shows that people who had a stroke receive on average one-tenth of the exercise training proven effective in animals. This, researchers say, is mostly because there was no easy way—until the development of PrimSeq—to accurately track their arm movements.
Published in the journal
PLOS Digital Health
online June 16
, the new study recorded the upper body movements of 41 adults who had a stroke while they performed routine rehabilitation exercises for regaining use in the arms and hands. Exercises and arm movements involved patients feeding themselves with a fork and grooming themselves with a comb.
More than 51,616 upper body movements were recorded from 9 sensors, with the digital recordings of each arm movement then matched to functional categories, such as whether the movement involved reaching for an object or holding it still.
Artificial intelligence (machine learning) software was then programmed to detect patterns within the data and tie these patterns to specific movements. The resulting PrimSeq tool was then tested on a separate group of eight patients who had a stroke who wore the sensors while performing various exercises.
PrimSeq was then used to see if it could accurately identify 12,545 of their recorded movements according to their function. The program was successful in accurately assessing the majority of movements in patients, all of whom had mild to moderate arm impairments from stroke.
“PrimSeq has state-of-the-art performance in terms of identifying and counting functional movements in stroke patients, and we are gathering more data to continue increasing its accuracy,” says co-senior investigator Carlos Fernandez-Granda, PhD, an associate professor of mathematics and data science at New York University.
“As our research seeks to find optimal levels of training intensity needed for recovery, I would assert that our tool is very promising for clinical use, since the alternative is not having accurate counts at all,” says Dr. Schambra. “If further experiments prove successful, we will of course be testing the system in clinical trials.”
The authors intend to make PrimSeq freely available to stroke rehabilitation experts worldwide and have already
posted their data used to construct the program
online.
Funding for the study was provided by National Institutes of Health grants R01LM013316, K02NS104207, and NCATS UL1TR001445. Additional funding support was provided by National Science Foundation grant NRT-HDR1922658 and American Heart Association–Amazon Web Service grant 19AMTG35210398.
Besides Dr. Fernandez-Granda and Dr. Schambra, other NYU Langone and NYU researchers involved in this study are co-lead investigators Avinash Parnandi and Aakash Kaku, Anita Venkatesan, Natasha Pandit, Audre Wirtanen, Haresh Rajamohan, and Kannan Venkataramanan. Another study co-investigator is Dawn Nilsen at Columbia University in New York.
Media Inquiries
David March
Phone: 212-404-3528
david.march@nyulangone.org
Our Experts
Heidi Schambra, MD
Care & Services
Neurology
Conditions
Stroke
Departments & Institutes
Department of Neurology
Rusk Rehabilitation Education & Research
Need help finding a doctor?
Search all doctors
or call 844-698-7243.
Related Articles
Patient Care
Genetic Testing & Heart Health: Revealing Your Risk
Our genetic counselors offer testing that can guide early care and help prevent heart disease in families.
February 23, 2026
Patient Care
,
Press Releases
Inside the Female Heart: What NYU Langone Heart Experts Want Every Woman to Know
Heart disease looks different in women, and care is evolving.
February 2, 2026
We can help you find a doctor.
Call
646-929-7800
or
browse our specialists
.
Patient Care
NYU Grossman School of Medicine
NYU Grossman Long Island School of Medicine
MyChart Patient Login
Patient Forms
Contact Us
Give
Education & Research
NYU Grossman School of Medicine
NYU Grossman Long Island School of Medicine
Current site
Current page