Evidence map›Paper›PMID 31861514›Full record

ArticleSensors (Basel, Switzerland)2019

Evaluation of Home-Based Rehabilitation Sensing Systems with Respect to Standardised Clinical Tests.

Ioannis Vourganas, Vladimir Stankovic, Lina Stankovic, Anna Lito Michala

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ioannis VourganasDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.ORCID 0000-0002-3433-3757
Vladimir StankovicDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Lina StankovicDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Anna Lito MichalaSchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.

Funding

EC SENSIBLE 734331
6 · The paper itself

Abstract

With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83 % of the respondents supporting the system's motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant ( ϱ c > 0 . 99 , R 2 > 0 . 94 , I C C > 0 . 96 ) and unbiased correlation to the golden standard.

Indexed as

AdultFemaleHealthy VolunteersHumansMaleSurveys and QuestionnairesTelerehabilitationWalkingWearable Electronic DevicesYoung Adultautomated five time sit to stand testautomated timed up and go testevaluation of sensor systemsnon-intrusive sensingself-evaluationsensing for health

Identifiers

PMID31861514
PMCPMC6982997

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.