Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank
Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proo...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2021
|
| Online Access: | https://hdl.handle.net/1721.1/131352 |
| _version_ | 1826207294430380032 |
|---|---|
| author | Williamson, James R. Telfer, Brian Mullany, Riley Friedl, Karl E. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Williamson, James R. Telfer, Brian Mullany, Riley Friedl, Karl E. |
| author_sort | Williamson, James R. |
| collection | MIT |
| description | Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity. |
| first_indexed | 2024-09-23T13:47:05Z |
| format | Article |
| id | mit-1721.1/131352 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:47:05Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1313522023-09-13T17:44:26Z Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank Williamson, James R. Telfer, Brian Mullany, Riley Friedl, Karl E. Lincoln Laboratory Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity. 2021-09-20T14:16:20Z 2021-09-20T14:16:20Z 2021-03-14 2021-03-26T14:12:31Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131352 Sensors 21 (6): 2047 (2021) PUBLISHER_CC http://dx.doi.org/10.3390/s21062047 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Williamson, James R. Telfer, Brian Mullany, Riley Friedl, Karl E. Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title | Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title_full | Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title_fullStr | Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title_full_unstemmed | Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title_short | Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank |
| title_sort | detecting parkinson s disease from wrist worn accelerometry in the u k biobank |
| url | https://hdl.handle.net/1721.1/131352 |
| work_keys_str_mv | AT williamsonjamesr detectingparkinsonsdiseasefromwristwornaccelerometryintheukbiobank AT telferbrian detectingparkinsonsdiseasefromwristwornaccelerometryintheukbiobank AT mullanyriley detectingparkinsonsdiseasefromwristwornaccelerometryintheukbiobank AT friedlkarle detectingparkinsonsdiseasefromwristwornaccelerometryintheukbiobank |