Literature DB >> 26357394

Objective Assessment of Upper-Limb Mobility for Poststroke Rehabilitation.

Zhe Zhang, Qiang Fang, Xudong Gu.   

Abstract

The assessment of the limb mobility of stroke patients is an essential part of poststroke rehabilitation. Conventionally, the assessment is manually performed by clinicians using chart-based ordinal scales, which can be subjective and inefficient. By introducing quantitative evaluation measures, the sensitivity and efficiency of the assessment process can be significantly improved. In this paper, a novel single-index-based assessment approach for quantitative upper-limb mobility evaluation has been proposed for poststroke rehabilitation. Instead of the traditional human-observation-based measures, the proposed assessment system utilizes the kinematic information automatically collected during a regular rehabilitation training exercise using a wearable inertial measurement unit. By calculating a single index, the system can efficiently generate objective and consistent quantitative results that can reflect the stroke patient's upper-limb mobility. In order to verify and validate the proposed assessment system, experiments have been conducted using 145 motion samples collected from 21 stroke patients (12 males, nine females, mean age 58.7±19.3) and eight healthy participants. The results have suggested that the proposed assessment index can not only differentiate the levels of limb function impairment clearly (p < 0.001, two-tailed Welch's t-test), but also strongly correlate with the Brunnstrom stages of recovery (r = 0.86, p < 0.001). The assessment index is also proven to have great potential in automatic Brunnstrom stage classification application with an 82.1% classification accuracy, while using a K-nearest-neighbor classifier.

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Mesh:

Year:  2015        PMID: 26357394     DOI: 10.1109/TBME.2015.2477095

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  9 in total

1.  Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke.

Authors:  Xinyu Song; Shirdi Shankara van de Ven; Shugeng Chen; Peiqi Kang; Qinghua Gao; Jie Jia; Peter B Shull
Journal:  Front Physiol       Date:  2022-06-03       Impact factor: 4.755

Review 2.  Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Authors:  Eni Halilaj; Apoorva Rajagopal; Madalina Fiterau; Jennifer L Hicks; Trevor J Hastie; Scott L Delp
Journal:  J Biomech       Date:  2018-09-13       Impact factor: 2.712

Review 3.  A review of computational approaches for evaluation of rehabilitation exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian; David Paul; Russell Baker
Journal:  Comput Biol Med       Date:  2020-03-04       Impact factor: 4.589

4.  A Deep Learning Framework for Assessing Physical Rehabilitation Exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-01-13       Impact factor: 3.802

5.  Assessment of physical rehabilitation movements through dimensionality reduction and statistical modeling.

Authors:  Christian Williams; Aleksandar Vakanski; Stephen Lee; David Paul
Journal:  Med Eng Phys       Date:  2019-10-25       Impact factor: 2.242

6.  Systematic review of novel technology-based interventions for ischemic stroke.

Authors:  Steven Mulackal Thomas; Ellie Delanni; Brandon Christophe; Edward Sander Connolly
Journal:  Neurol Sci       Date:  2021-02-18       Impact factor: 3.830

7.  A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.

Authors:  Lei Yu; Daxi Xiong; Liquan Guo; Jiping Wang
Journal:  Sensors (Basel)       Date:  2016-02-05       Impact factor: 3.576

Review 8.  Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment.

Authors:  Pablo Maceira-Elvira; Traian Popa; Anne-Christine Schmid; Friedhelm C Hummel
Journal:  J Neuroeng Rehabil       Date:  2019-11-19       Impact factor: 4.262

9.  Occupational Therapy Assessment for Upper Limb Rehabilitation: A Multisensor-Based Approach.

Authors:  Seedahmed S Mahmoud; Zheng Cao; Jianming Fu; Xudong Gu; Qiang Fang
Journal:  Front Digit Health       Date:  2021-12-17
  9 in total

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