Evidence map›Paper›PMID 30573447›Full record

ArticleJMIR rehabilitation and assistive technologies2018

Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study.

Giulia Barbareschi, Catherine Holloway, Nadia Bianchi-Berthouze, Sharon Sonenblum, Stephen Sprigle

Abstract read
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Article in JMIR rehabilitation and assistive technologies, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Giulia BarbareschiUniversity College London Interaction Centre, London, United Kingdom.ORCID http://orcid.org/0000-0001-9036-3566
Catherine HollowayUniversity College London Interaction Centre, London, United Kingdom.ORCID http://orcid.org/0000-0001-7843-232X
Nadia Bianchi-BerthouzeUniversity College London Interaction Centre, London, United Kingdom.ORCID http://orcid.org/0000-0001-8921-0044
Sharon SonenblumRehabilitation Engineering and Applied Research Laboratory, Georgia Institute of Technology, Atlanta, GA, United States.ORCID http://orcid.org/0000-0001-9747-5683
Stephen SprigleRehabilitation Engineering and Applied Research Laboratory, Georgia Institute of Technology, Atlanta, GA, United States.ORCID http://orcid.org/0000-0003-0462-0138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTransfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place.

objectiveThe objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings.

methodsWe collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data.

resultsResults show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context (multinomial logistic regression accuracy 87.8%). However, we achieve these results using only a single sensor and collecting data in a more ecological manner.

conclusionsThe use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation.

Indexed as

accelerometeractivity monitoringmachine learningmovement evaluationwheelchair transfers

Identifiers

PMID30573447
PMCPMC6320409

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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.