Literature DB >> 34078400

Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton.

Florian Grimm1, Jelena Kraugmann2, Georgios Naros2, Alireza Gharabaghi3.   

Abstract

BACKGROUND: The clinical evaluation of the upper limb of severely impaired stroke patient is challenging. Sensor-based assessments may allow for an objective evaluation of this patient population. This study investigated the validity of a device-assisted approach in comparison to the clinical outcome that it is supposed to reflect.
METHODS: In nineteen severely impaired chronic stroke patients, we applied a gravity-compensating, multi-joint arm exoskeleton (Armeo Spring) and compared this sensor-based assessment with the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. Specifically, we assessed separately and subsequently the range of motion in joint space for four single joints (i.e., wrist, elbow and shoulder flexion/extension (FE), and shoulder internal/external rotation (IER)), and the closing and opening of the hand with a pressure sensor placed in the handle.
RESULTS: Within the kinematic parameters, a strong correlation was observed between wrist and elbow FE (r > 0.7, p < 0.003; Bonferroni corrected). The UE-FMA was significantly predicted by a multiple regression model (F (5, 13) = 12.22, p < 0.0005, adj. R2 = 0.83). Both shoulder IER and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.
CONCLUSIONS: By applying an exoskeleton-based self-contained evaluation of single-joint movements, a clinically valid assessment of the upper limb range of motion in severely impaired stroke patients is feasible. Shoulder IER contributed most relevantly to the prediction of the clinical status. These findings need to be confirmed in a large, independent patient cohort.

Entities:  

Keywords:  Exoskeleton; Hand-arm model; Human–machine interface; Movement analysis; Neurorehabilitation; Rehabilitation robotics; Sensorimotor interaction; Stroke; Virtual reality

Year:  2021        PMID: 34078400     DOI: 10.1186/s12984-021-00875-7

Source DB:  PubMed          Journal:  J Neuroeng Rehabil        ISSN: 1743-0003            Impact factor:   4.262


  41 in total

1.  Predictors of change in quality of life after distributed constraint-induced therapy in patients with chronic stroke.

Authors:  Yan-hua Huang; Ching-yi Wu; Yu-wei Hsieh; Keh-chung Lin
Journal:  Neurorehabil Neural Repair       Date:  2010-05-03       Impact factor: 3.919

Review 2.  Stroke. Neurologic and functional recovery the Copenhagen Stroke Study.

Authors:  H S Jørgensen; H Nakayama; H O Raaschou; T S Olsen
Journal:  Phys Med Rehabil Clin N Am       Date:  1999-11       Impact factor: 1.784

Review 3.  Kinematic measures for upper limb robot-assisted therapy following stroke and correlations with clinical outcome measures: A review.

Authors:  Vi Do Tran; Paolo Dario; Stefano Mazzoleni
Journal:  Med Eng Phys       Date:  2018-02-01       Impact factor: 2.242

4.  Virtual reality-based post-stroke hand rehabilitation.

Authors:  R Boian; A Sharma; C Han; A Merians; G Burdea; S Adamovich; M Recce; M Tremaine; H Poizner
Journal:  Stud Health Technol Inform       Date:  2002

5.  Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Jeroen van der Grond; Arie J H Prevo
Journal:  Stroke       Date:  2003-08-07       Impact factor: 7.914

6.  Reliability of movement workspace measurements in a passive arm orthosis used in spinal cord injury rehabilitation.

Authors:  Claudia Rudhe; Urs Albisser; Michelle L Starkey; Armin Curt; Marc Bolliger
Journal:  J Neuroeng Rehabil       Date:  2012-06-09       Impact factor: 4.262

7.  Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation.

Authors:  Florian Grimm; Georgios Naros; Alireza Gharabaghi
Journal:  Front Neurosci       Date:  2016-11-15       Impact factor: 4.677

8.  Weight compensation characteristics of Armeo®Spring exoskeleton: implications for clinical practice and research.

Authors:  Bonnie E Perry; Emily K Evans; Dobrivoje S Stokic
Journal:  J Neuroeng Rehabil       Date:  2017-02-17       Impact factor: 4.262

9.  Closed-Loop Neuroprosthesis for Reach-to-Grasp Assistance: Combining Adaptive Multi-channel Neuromuscular Stimulation with a Multi-joint Arm Exoskeleton.

Authors:  Florian Grimm; Alireza Gharabaghi
Journal:  Front Neurosci       Date:  2016-06-23       Impact factor: 4.677

10.  Compensation or Restoration: Closed-Loop Feedback of Movement Quality for Assisted Reach-to-Grasp Exercises with a Multi-Joint Arm Exoskeleton.

Authors:  Florian Grimm; Georgios Naros; Alireza Gharabaghi
Journal:  Front Neurosci       Date:  2016-06-21       Impact factor: 4.677

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  3 in total

1.  Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study.

Authors:  Ze-Jian Chen; Chang He; Ming-Hui Gu; Jiang Xu; Xiao-Lin Huang
Journal:  J Healthc Eng       Date:  2021-08-19       Impact factor: 2.682

2.  Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers.

Authors:  Michela Goffredo; Sanaz Pournajaf; Stefania Proietti; Annalisa Gison; Federico Posteraro; Marco Franceschini
Journal:  Front Neurol       Date:  2021-12-21       Impact factor: 4.003

3.  A unified scheme for the benchmarking of upper limb functions in neurological disorders.

Authors:  Valeria Longatelli; Diego Torricelli; Jesús Tornero; Alessandra Pedrocchi; Franco Molteni; José L Pons; Marta Gandolla
Journal:  J Neuroeng Rehabil       Date:  2022-09-27       Impact factor: 5.208

  3 in total

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