Search research articles

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

Smart Monitoring for Cancer Treatment: Feasibility Study of an IoT-Based Assessment System.

Sensors (Basel, Switzerland)·2026

Same author

Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.

International journal of neural systems·2025

Same author

A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists' demonstrations.

Journal of neuroengineering and rehabilitation·2025

Same author

Rehabilitation Technologies by Integrating Exoskeletons, Aquatic Therapy, and Quantum Computing for Enhanced Patient Outcomes.

Sensors (Basel, Switzerland)·2024

Same author

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions.

Computers in biology and medicine·2024

Same author

Influence of Robotic Therapy on Severe Stroke Patients.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2023

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 4, 2026

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

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

Published on: September 6, 2024

Intelligent System for Upper Limb Motor Assessment Using Inertial Sensors and Machine Learning for Telerehabilitation

David Martinez-Pascual, Yolanda Vales, Racul Martin-Batanero

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|March 2, 2026

Summary

This study introduces an intelligent system using Inertial Measurement Units (IMUs) to assess upper limb motor function. The technology can recognize daily activities and classify motor disabilities for remote rehabilitation.

More Related Videos

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

Related Experiment Videos

Last Updated: Mar 4, 2026

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

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

Published on: September 6, 2024

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

Area of Science:

Neurology
Biomedical Engineering
Rehabilitation Technology

Background:

Neurological injuries frequently cause upper limb motor dysfunction, hindering Activities of Daily Living (ADLs).
Remote patient monitoring and telerehabilitation offer potential solutions for managing these disabilities.
Current remote assessment methods for upper limb function require technological advancement.

Purpose of the Study:

To develop an intelligent evaluation system for remote assessment of upper limb motor function.
To utilize machine learning and Inertial Measurement Units (IMUs) for activity recognition and impairment grading.
To enable remote monitoring and rehabilitation for patients with neurological injuries.

Main Methods:

An intelligent system was designed using three IMUs to capture upper limb joint trajectories.
A machine learning model processed trajectories to recognize twelve ADL-based activities.
Dynamic Time Warping (DTW) calculated similarity indexes between patient and non-disabled user trajectories to estimate impairment levels (mild/moderate).

Main Results:

The system successfully recognized twelve distinct upper limb activities.
Similarity indexes effectively estimated the degree of motor impairment in neurological injury survivors.
Feasibility was demonstrated in 31 neurological injury survivors and 9 controls.

Conclusions:

The developed intelligent system is feasible for recognizing upper limb activities and assessing motor function remotely.
This technology can significantly enhance the remote monitoring and rehabilitation of stroke patients in home environments.
The system aids in classifying motor disabilities, paving the way for personalized telerehabilitation strategies.