eprintid: 10060066
rev_number: 33
eprint_status: archive
userid: 608
dir: disk0/10/06/00/66
datestamp: 2018-10-30 13:49:23
lastmod: 2021-10-01 23:48:28
status_changed: 2019-02-04 13:19:30
type: article
metadata_visibility: show
creators_name: Barbareschi, G
creators_name: Holloway, CSM
creators_name: Berthouze, N
creators_name: Sonenblum, S
creators_name: Sprigle, S
title: Use of a Low Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D05
divisions: F70
divisions: B04
divisions: C05
divisions: F48
keywords: Wheelchair transfers, movement evaluation, machine learning, activity monitoring.
note: Articles are distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published by JMIR Publications, is properly cited. The complete bibliographic information (authors, title, journal, volume/issue, articleID), a link to the original publication (URL), as well as this copyright and license information ("Licensed under Creative Commons Attribution cc-by 4.0") must be included.
abstract: BACKGROUND: Transfers are an important skill for many wheelchair users. 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.
OBJECTIVE: The objective of this paper is to develop a strategy to enable transfer quality evaluation and improve the predictive accuracy of transfer detection using a single wearable low cost accelerometer.
METHODS: We collected data from nine wheelchair users wearing tri-axial 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.
RESULTS: Results show that the best predictive accuracy for Automatic Transfer Quality Evaluation was obtained with Support Vector Machine (SVM) classifiers when determining use of head-hip relationship (75.93%) and smoothness of landing
(79.62%), when the start and end of the transfer are known. Automatic Transfer
Detection reaches an accuracy of 87.8% using Multinomial Logistic Regression (MLR) classifiers, which is in line with the state of the art in this context. However,
we achieve these results using only a single sensor and collecting data in a more ecological manner.
CONCLUSIONS: The use of a single chest-placed accelerometer shows a predictive accuracy of over 75% for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous technology for personalized skill development interventions targeting wheelchair
users. However, monitoring transfers still requires the use of external inputs or extra sensors to identify start and end of the transfer, which are needed to perform an
accurate evaluation.
date: 2018-12-20
date_type: published
official_url: https://doi.org/10.2196/11748
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1597918
doi: 10.2196/11748
lyricists_name: Barbareschi, Giulia
lyricists_name: Berthouze, Nadia
lyricists_name: Holloway, Catherine
lyricists_id: GBARB38
lyricists_id: NBERT19
lyricists_id: CSHOL54
actors_name: Holloway, Catherine
actors_id: CSHOL54
actors_role: owner
full_text_status: public
publication: JMIR Rehabilitation and Assistive Technologies
volume: 5
number: 2
article_number: e11748
issn: 2369-2529
citation:        Barbareschi, G;    Holloway, CSM;    Berthouze, N;    Sonenblum, S;    Sprigle, S;      (2018)    Use of a Low Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities.                   JMIR Rehabilitation and Assistive Technologies , 5  (2)    , Article e11748.  10.2196/11748 <https://doi.org/10.2196/11748>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10060066/7/Barbareschi_Use%20of%20a%20low-cost%2C%20chest-mounted%20accelerometer%20to%20evaluate%20transfer%20skills%20of%20wheelchair%20users%20during.pdf