Literature DB >> 17551857

Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy.

Hussein A Abdullah1, Cole Tarry, Rahul Datta, Gauri S Mittal, Mohamed Abderrahim.   

Abstract

This article describes the design, validation, and application of a dynamic biomechanical model that assesses and monitors trajectory, position, orientation, force, and torque generated by upper-limb (UL) movement during robot-assisted therapy. The model consists of two links that represent the upper arm and forearm, with 5 degrees of freedom (DOF) for the shoulder and elbow joints. The model is a useful tool for enhancing the functionality of poststroke robot-assisted UL therapy. The individualized inertial segment parameters were based on anthropometric measurements. The model performed inverse dynamic analysis of UL movements to calculate reaction forces and moments acting about the 3-DOF shoulder and 2-DOF elbow joints. Real-time fused biofeedback of a 6-DOF force sensor and three-dimensional (3-D) pose sensors supported the model validation and application. The force sensor was mounted between the robot manipulator and the subject's wrist, while the 3-D pose sensors were fixed at specific positions on the subject's UL segments. The model input and output parameters were stored in the subject's database, which is part of the rehabilitation information system. We assigned 20 nondisabled subjects three different therapy exercises to test and validate the biomechanical model. We found that when the biomechanical model is taught an exercise, it can accurately predict a subject's actual UL joint angles and torques and confirm that the exercise is isolating the desired movement.

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Year:  2007        PMID: 17551857     DOI: 10.1682/jrrd.2006.03.0025

Source DB:  PubMed          Journal:  J Rehabil Res Dev        ISSN: 0748-7711


  5 in total

1.  Results of clinicians using a therapeutic robotic system in an inpatient stroke rehabilitation unit.

Authors:  Hussein A Abdullah; Cole Tarry; Cynthia Lambert; Susan Barreca; Brian O Allen
Journal:  J Neuroeng Rehabil       Date:  2011-08-26       Impact factor: 4.262

Review 2.  Robotic neurorehabilitation: a computational motor learning perspective.

Authors:  Vincent S Huang; John W Krakauer
Journal:  J Neuroeng Rehabil       Date:  2009-02-25       Impact factor: 4.262

Review 3.  Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review.

Authors:  Nurdiana Nordin; Sheng Quan Xie; Burkhard Wünsche
Journal:  J Neuroeng Rehabil       Date:  2014-09-12       Impact factor: 4.262

4.  A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs.

Authors:  Angel Gil-Agudo; Ana de Los Reyes-Guzmán; Iris Dimbwadyo-Terrer; Benito Peñasco-Martín; Alberto Bernal-Sahún; Patricia López-Monteagudo; Antonio Del Ama-Espinosa; José Luis Pons
Journal:  Neural Regen Res       Date:  2013-07-05       Impact factor: 5.135

5.  Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework.

Authors:  Fares J Abu-Dakka; Angel Valera; Juan A Escalera; Mohamed Abderrahim; Alvaro Page; Vicente Mata
Journal:  Sensors (Basel)       Date:  2020-10-31       Impact factor: 3.576

  5 in total

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