Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Upper Extremity Robotic-Assisted Rehabilitation: Results Not Yet Robust.

Stroke·2023
Same author

Experimental Neurotherapeutics: Surfing the Tidal Wave of New Opportunities.

Annals of neurology·2022
Same author

FLUORESCE: A Pilot Randomized Clinical Trial of Fluoxetine for Vision Recovery After Acute Ischemic Stroke.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society·2022
Same author

HRS phosphorylation drives immunosuppressive exosome secretion and restricts CD8<sup>+</sup> T-cell infiltration into tumors.

Nature communications·2022
Same author

Bivalirudin anticoagulation in neonates and infants undergoing cardiac surgery.

Journal of cardiothoracic and vascular anesthesia·2022
Same author

Umbilical Nodule Metastasis from Unknown Primary: Diagnostic and Therapeutic Dilemma.

Surgery journal (New York, N.Y.)·2022

Related Experiment Video

Updated: Oct 23, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.6K

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine

Noah Balestra1, Gaurav Sharma2,3,4, Linda M Riek5

  • 1Department of Neurology, University of Rochester, Rochester, New York, USA.

Digital Biomarkers
|August 20, 2021
PubMed
Summary

Body-worn sensors accurately track rehabilitation exercises for stroke patients. This technology may help quantify exercise dose, improving recovery and clinical trials.

Keywords:
Rehabilitation researchStroke rehabilitationSupervised machine learningTask performance and analysisWearable devices

More Related Videos

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

1.0K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.7K

Related Experiment Videos

Last Updated: Oct 23, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.6K
Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

1.0K
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.7K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Wearable Technology

Background:

  • Post-stroke motor function improves with rehabilitation exercises.
  • Quantifying exercise dose and timing in stroke rehabilitation is challenging.
  • Previous studies lacked methods for multi-day exercise activity quantification.

Purpose of the Study:

  • Assess feasibility of body-worn sensors for tracking inpatient rehabilitation exercises.
  • Identify optimal recording parameters and data analysis for accurate exercise repetition counting.
  • Investigate sensor system utility for measuring post-stroke exercise dose.

Main Methods:

  • Used MC10 BioStampRC® sensors (accelerometer, gyroscope) on upper extremities.
  • Collected data from healthy controls (n=13) and stroke patients (n=13) performing arm exercises.
  • Trained a machine learning algorithm with labeled sensor data to classify exercise types and count repetitions.

Main Results:

  • Achieved 95.6% overall repetition counting accuracy.
  • Attained 95.0% accuracy in stroke patients using combined accelerometer and gyroscope data.
  • Accuracy decreased with fewer sensors or accelerometer data alone.

Conclusions:

  • Body-worn sensor systems are technically feasible for stroke rehabilitation.
  • The system is well-tolerated by individuals with recent stroke.
  • This technology may enable precise measurement of exercise dose in clinical settings and trials.