An Instrumented Measurement Scheme for the Assessment of Upper Limb Function in Individuals with Friedreich Ataxia.

Clicks: 481
ID: 83200
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #6 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 127 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Continuous and objective assessment is essential for accurate monitoring of the progression of neurodegenerative conditions such as Friedreich ataxia. However, current clinical assessments predominantly rely on the ability of the affected individual to complete specific clinical tests which may not capture the intricate kinematic details associated with ataxia Moreover, such testing often consists of a level of subjectivity of the assessing clinician. In this paper, we propose an objective measuring instrument, in the form of a spoon, equipped with the Internet-of-Things (IoT) based system and relevant machine learning techniques to quantitatively assess impairment levels while engaged in routine daily activity. In a clinical study involving individuals diagnosed with Friedreich ataxia, movement patterns during a simulated eating task were captured and kinematic biomarkers were extracted that were consistent with the frequently-used clinical rating scales. Multivariate analysis of these biomarkers allows us to accurately classify individuals with Friedreich ataxia and control subjects to an accuracy of 91%. Furthermore, the kinematic information captured from the spoon can be used to introduce an alternative assessment scheme with a greater sensitivity to ataxic movements and with less inter-rater discrepancy.
Reference Key
nguyen2019anconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nguyen, Khoa D;Corben, Louise A;Pathirana, Pubudu N;Horne, Malcolm K;Delatycki, Martin B;Szmulewicz, David J;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8857107
URL
Keywords
random forest iot wsn amof intrusion detection systems linear regression

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.