Imperial College London
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DrPaulBentley

Faculty of Medicine, Department of Brain Sciences

Senior Clinical Research Fellow
 
 
 
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Contact

 

p.bentley

 
 
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Location

 

10L21Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Formstone:2021:10.1109/TNSRE.2021.3089613,
author = {Formstone, L and Huo, W and Wilson, S and McGregor, A and Bentley, P and Vaidyanathan, R},
doi = {10.1109/TNSRE.2021.3089613},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {1158--1167},
title = {Quantification of motor function post-stroke using wearable inertial and ,echanomyographic Sensors},
url = {http://dx.doi.org/10.1109/TNSRE.2021.3089613},
volume = {29},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Subjective clinical rating scales represent the goldstandard diagnosis of motor function following stroke, however in practice they suffer from well-recognised limitations including variance between assessors, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have significantly impacted clinical practice. We address translational challenges in this arena through: (1) implementation of a novel sensor suite fusing inertial measurement and mechanomyography (MMG) to quantify hand and wrist motor function; and (2) introduction of a new range of signal features extracted from the suite to supplement predicted clinical scores. The wearable sensors, signal features, and sensor fusion algorithms have been combined to produce classified ratings from the Fugl-Meyer clinical assessment rating scale. Furthermore, we have designed the system to augment clinical rating with several sensor-derived supplementary features encompassing critical aspects of motor dysfunction (e.g. joint angle, muscle activity, etc.). Performance is validated through a large-scale study on a poststroke cohort of 64 patients. Fugl-Meyer Assessment tasks were classified with 75% accuracy for gross motor tasks and 62% for hand/wrist motor tasks. Of greater import, supplementary features demonstrated concurrent validity with Fugl-Meyer ratings, evidencing their utility as new measures of motor function suited to automated assessment. Finally, the supplementary features also provide continuous measures of sub-components of motor function, offering the potential to complement low accuracy but well-validated clinical rating scales when high-quality motor outcome measures are required. We believe this work provides a basis for widespread clinical adoption of inertial-MMG sensor use for post-stroke clinical motor assessment.Index Terms—Stroke, Fugl-Meyer assessment, automated upper-limb assessment, wearables, machine learning, mechanomyogra
AU  - Formstone,L
AU  - Huo,W
AU  - Wilson,S
AU  - McGregor,A
AU  - Bentley,P
AU  - Vaidyanathan,R
DO  - 10.1109/TNSRE.2021.3089613
EP  - 1167
PY  - 2021///
SN  - 1534-4320
SP  - 1158
TI  - Quantification of motor function post-stroke using wearable inertial and ,echanomyographic Sensors
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR  - http://dx.doi.org/10.1109/TNSRE.2021.3089613
UR  - https://ieeexplore.ieee.org/document/9455409
UR  - http://hdl.handle.net/10044/1/89462
VL  - 29
ER  -
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