Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
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,...
| Main Authors: | , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/54815 |
| _version_ | 1826212542479859712 |
|---|---|
| author | Patel, Shyamal Lorincz, Konrad Hughes, Richard Huggins, Nancy Growdon, John H. Standaert, David Akay, Metin Dy, Jennifer G. Welsh, Matt Bonato, Paolo |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Patel, Shyamal Lorincz, Konrad Hughes, Richard Huggins, Nancy Growdon, John H. Standaert, David Akay, Metin Dy, Jennifer G. Welsh, Matt Bonato, Paolo |
| author_sort | Patel, Shyamal |
| collection | MIT |
| description | 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 taken while 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 and motor 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. |
| first_indexed | 2024-09-23T15:25:19Z |
| format | Article |
| id | mit-1721.1/54815 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:25:19Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/548152022-10-02T02:38:58Z Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors Patel, Shyamal Lorincz, Konrad Hughes, Richard Huggins, Nancy Growdon, John H. Standaert, David Akay, Metin Dy, Jennifer G. Welsh, Matt Bonato, Paolo Harvard University--MIT Division of Health Sciences and Technology Bonato, Paolo Bonato, Paolo wearable sensors support vector machines (SVMs) Parkinson's disease Body sensor networks 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 taken while 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 and motor 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. Microsoft Corporation Sun Microsystems Siemens Aktiengesellschaft Intel Corporation Michael J. Fox Foundation for Parkinson's Research National Science Foundation (Grant CNS-0546338) National Institute of Neurological Disorders and Stroke (U.S.) (Grant R21NS045401-02) 2010-05-19T21:01:43Z 2010-05-19T21:01:43Z 2009-11 2009-07 Article http://purl.org/eprint/type/JournalArticle 1089-7771 INSPEC Accession Number: 10957700 http://hdl.handle.net/1721.1/54815 Patel, S. et al. “Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors.” Information Technology in Biomedicine, IEEE Transactions on 13.6 (2009): 864-873. © 2009 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/titb.2009.2033471 IEEE Transactions on Information Technology in Biomedicine Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | wearable sensors support vector machines (SVMs) Parkinson's disease Body sensor networks Patel, Shyamal Lorincz, Konrad Hughes, Richard Huggins, Nancy Growdon, John H. Standaert, David Akay, Metin Dy, Jennifer G. Welsh, Matt Bonato, Paolo Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title | Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title_full | Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title_fullStr | Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title_full_unstemmed | Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title_short | Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors |
| title_sort | monitoring motor fluctuations in patients with parkinson s disease using wearable sensors |
| topic | wearable sensors support vector machines (SVMs) Parkinson's disease Body sensor networks |
| url | http://hdl.handle.net/1721.1/54815 |
| work_keys_str_mv | AT patelshyamal monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT lorinczkonrad monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT hughesrichard monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT hugginsnancy monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT growdonjohnh monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT standaertdavid monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT akaymetin monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT dyjenniferg monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT welshmatt monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors AT bonatopaolo monitoringmotorfluctuationsinpatientswithparkinsonsdiseaseusingwearablesensors |