Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring
Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9122 |
| _version_ | 1797462162345558016 |
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| author | Elham Rastegari Hesham Ali Vivien Marmelat |
| author_facet | Elham Rastegari Hesham Ali Vivien Marmelat |
| author_sort | Elham Rastegari |
| collection | DOAJ |
| description | Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection. |
| first_indexed | 2024-03-09T17:32:34Z |
| format | Article |
| id | doaj.art-20c1ebe19bec476db7ef6a309f600ae2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20c1ebe19bec476db7ef6a309f600ae22023-11-24T12:08:51ZengMDPI AGSensors1424-82202022-11-012223912210.3390/s22239122Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive MonitoringElham Rastegari0Hesham Ali1Vivien Marmelat2Department of Business Intelligence and Analytics, Business College, Creighton University, Omaha, NE 68178, USADepartment of Biomedical Informatics, College of Information Systems and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USADepartment of Biomechanics, College of Education, Health and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USAParkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.https://www.mdpi.com/1424-8220/22/23/9122Parkinson’s diseasewearable accelerometerearly detectionpassive monitoring |
| spellingShingle | Elham Rastegari Hesham Ali Vivien Marmelat Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring Sensors Parkinson’s disease wearable accelerometer early detection passive monitoring |
| title | Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring |
| title_full | Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring |
| title_fullStr | Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring |
| title_full_unstemmed | Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring |
| title_short | Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring |
| title_sort | detection of parkinson s disease using wrist accelerometer data and passive monitoring |
| topic | Parkinson’s disease wearable accelerometer early detection passive monitoring |
| url | https://www.mdpi.com/1424-8220/22/23/9122 |
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