Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors

Authors:
Shyamal Patel;Konrad Lorincz;Richard Hughes;Nancy Huggins;John Growdon;David Standaert;Metin Akay;Jennifer Dy;Matt Welsh;Paolo Bonato
Affiliations:
Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA;Harvard School of Engineering and Applied Sciences, Cambridge, MA;Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA;Harvard Medical School, Boston, MA;Harvard Medical School, Boston, MA;University of Alabama at Birmingham, Birmingham, AL;School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ;Department of Electrical and Computer Engineering, Northeastern University, Boston, MA;Harvard School of Engineering and Applied Sciences, Cambridge, MA;Department of Physical Medicine and Rehabilitation, HarvardMedical School, Spaulding RehabilitationHospital, Boston, MA and Health Sciences and Technology, Cambridge, MA
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Year:
2009

Citing 2
Cited 12

The nature of statistical learning theory

The nature of statistical learning theory
Analysis of the Severity of Dyskinesia in Patients with Parkinson's Disease via Wearable Sensors

BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks

An overview of body sensor networks in enabling pervasive healthcare and assistive environments

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
A logic approach to virtual sensor networks

Proceedings of the Fourteenth International Database Engineering & Applications Symposium
SVM based context awareness using body area sensor network for pervasive healthcare monitoring

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
A novel gait recognition analysis system based on body sensor networks for patients with Parkinson's disease

International Journal of Communication Networks and Distributed Systems
A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients

Computer Methods and Programs in Biomedicine
Using partial decision trees to predict Parkinson's symptoms: A new approach for diagnosis and therapy in patients suffering from Parkinson's disease

Computers in Biology and Medicine
Throughput efficiency in body sensor networks: A clean-slate approach

Expert Systems with Applications: An International Journal
An automated methodology for levodopa-induced dyskinesia: Assessment based on gyroscope and accelerometer signals

Artificial Intelligence in Medicine
Telemonitoring System of Neurological Signs in a Health Telematique Network

International Journal of E-Health and Medical Communications
Optimization intensive energy harvesting

DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Efficient health data compression on mobile devices

Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare
A Markovian model for harvested power from human motion

BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings takenwhile patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms andmotor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.