Imperial College London
Alternate

ProfessorRaviVaidyanathan

Faculty of Engineering, Department of Mechanical Engineering

Professor in Biomechatronics
 
 
 
//

Contact

 

+44 (0)20 7594 7020r.vaidyanathan CV

 
 
//

Location

 

717City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Huo:2020:10.1109/TNSRE.2020.2978197,
author = {Huo, W and Angeles, P and Tai, YF and Pavese, N and Wilson, S and Hu, MT and Vaidyanathan, R},
doi = {10.1109/TNSRE.2020.2978197},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {1397--1406},
title = {A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease},
url = {http://dx.doi.org/10.1109/TNSRE.2020.2978197},
volume = {28},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity outof-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and learning algorithms. The sensor system is composed of a force-sensor, two inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson’s Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closedloop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.
AU  - Huo,W
AU  - Angeles,P
AU  - Tai,YF
AU  - Pavese,N
AU  - Wilson,S
AU  - Hu,MT
AU  - Vaidyanathan,R
DO  - 10.1109/TNSRE.2020.2978197
EP  - 1406
PY  - 2020///
SN  - 1534-4320
SP  - 1397
TI  - A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR  - http://dx.doi.org/10.1109/TNSRE.2020.2978197
UR  - https://ieeexplore.ieee.org/document/9064818
UR  - http://hdl.handle.net/10044/1/77054
VL  - 28
ER  -
Download