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A Robotic Gamified Framework for Upper-Limb Rehabilitation
by
Casanova, Anahis
, Romero, Cristina
, Ubeda, Andres
, Porcel, Koralie
, Jara, Carlos A.
, Sempere, Natalia
in
Biosensors
/ Brain
/ Clinical medicine
/ Collaboration
/ collaborative robots
/ Electromyography
/ Gamification
/ Injuries
/ International economic relations
/ Kinematics
/ Motor ability
/ neurorehabilitation
/ Patients
/ physical assessment
/ Rehabilitation
/ Robotics
/ Robotics industry
/ Robots
/ Sensors
/ Therapy
/ Velocity
2025
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A Robotic Gamified Framework for Upper-Limb Rehabilitation
by
Casanova, Anahis
, Romero, Cristina
, Ubeda, Andres
, Porcel, Koralie
, Jara, Carlos A.
, Sempere, Natalia
in
Biosensors
/ Brain
/ Clinical medicine
/ Collaboration
/ collaborative robots
/ Electromyography
/ Gamification
/ Injuries
/ International economic relations
/ Kinematics
/ Motor ability
/ neurorehabilitation
/ Patients
/ physical assessment
/ Rehabilitation
/ Robotics
/ Robotics industry
/ Robots
/ Sensors
/ Therapy
/ Velocity
2025
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Do you wish to request the book?
A Robotic Gamified Framework for Upper-Limb Rehabilitation
by
Casanova, Anahis
, Romero, Cristina
, Ubeda, Andres
, Porcel, Koralie
, Jara, Carlos A.
, Sempere, Natalia
in
Biosensors
/ Brain
/ Clinical medicine
/ Collaboration
/ collaborative robots
/ Electromyography
/ Gamification
/ Injuries
/ International economic relations
/ Kinematics
/ Motor ability
/ neurorehabilitation
/ Patients
/ physical assessment
/ Rehabilitation
/ Robotics
/ Robotics industry
/ Robots
/ Sensors
/ Therapy
/ Velocity
2025
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A Robotic Gamified Framework for Upper-Limb Rehabilitation
Journal Article
A Robotic Gamified Framework for Upper-Limb Rehabilitation
2025
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Overview
Robotic devices have become increasingly important in upper-limb rehabilitation, as they assist therapists, improve treatment efficiency, and enable personalised therapy. However, the lack of standardised protocols and integrative tools limits their widespread adoption and effectiveness. To address these challenges, a robotic framework was developed for upper-limb rehabilitation in patients with acquired brain injury (ABI). The framework is designed to be adaptable to various ROS-compatible collaborative robots with admittance control and potentially adaptable to other types of control, and also integrates kinematic and electrophysiological (EMG) metrics to monitor patient performance and progress. It combines data acquisition through EMG and robot motion sensors, gamification elements to enhance engagement, and configurable robot control modes within a unified software platform. A pilot evaluation with eight healthy subjects performing upper limb movements on an ROS-compatible robot from the UR family demonstrated the feasibility of the framework’s components, including robot control, EMG acquisition and synchronization, gamified interaction, and synchronised data collection. User performance through all levels remained below the controller limits of force and velocity thresholds even in the most resistive damping. These results support the potential of the proposed framework as a flexible, extensible, and integrative tool for upper-limb rehabilitation, providing a foundation for future clinical studies and multi-platform implementations.
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