M Sun
Methods to characterize the real-world use of rollators using inertial sensors – a feasibility study
Sun, M; Kenney, LPJ; Thies, SBA; Costamagna, L
Authors
Prof Laurence Kenney L.P.J.Kenney@salford.ac.uk
Professor
Dr Sibylle Thies S.Thies@salford.ac.uk
Associate Professor/Reader
L Costamagna
Abstract
Rollators are widely used by people with mobility problems, but previous studies have been limited to self-report approaches when evaluating their real-world effectiveness. To support studies based on more robust datasets, a method to estimate mobility parameters, such as gait speed and distance traveled, in the real world is needed. Body-worn sensors offer one approach to the problem, but rollator-mounted sensors have some practical advantages providing direct insight into patterns of walking device used, an under-researched area. We present a novel method to estimate speed and distance traveled from a single rollator-mounted IMU. The method was developed using data collected from ten rollator users performing a series of walking tasks including obstacle negotiation. The IMU data is first pre-processed to account for noise, orientation offset, and rotation-induced accelerations. The method then uses a two-stage approach. First, activity classification is used to separate the rollator data into one of three classes (movement, turning, or other). Subsequently, the speed of movement and distance traveled is estimated, using a separate estimation model for each of the three classes. The results showed high classification accuracy (precision, recall, and F1 statistics all >0.9). Speed estimation showed mean absolute errors below 0.2 m/s. Estimates for distance traveled showed errors which ranged from 5% (straight line walking) to over 70%. The results showed some promise but further work with a larger data set is needed to confirm the performance of our approach.
Citation
Sun, M., Kenney, L., Thies, S., & Costamagna, L. (2019). Methods to characterize the real-world use of rollators using inertial sensors – a feasibility study. IEEE Access, 7, https://doi.org/10.1109/ACCESS.2019.2919286
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 24, 2019 |
Publication Date | May 27, 2019 |
Deposit Date | Jun 21, 2019 |
Publicly Available Date | Nov 25, 2019 |
Journal | IEEE Access |
Publisher | Institute of Electrical and Electronics Engineers |
Volume | 7 |
DOI | https://doi.org/10.1109/ACCESS.2019.2919286 |
Publisher URL | http://dx.doi.org/10.1109/ACCESS.2019.2919286 |
Related Public URLs | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 |
Additional Information | Funders : Engineering and Physical Sciences Research Council (EPSRC) Projects : Adaptive Assistive Rehabilitative Technologies-Beyond the Clinic Grant Number: EP/M025543/1 |
Files
Sun et al IEEE Access 2019.pdf
(10.3 Mb)
PDF
You might also like
Why does my prosthetic hand not always do what it is told?
(2022)
Journal Article
Co-creation and user perspectives for upper limb prosthetics
(2021)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search