Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors
Abstract Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-04-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12984-020-00684-4 |
| _version_ | 1818328742649397248 |
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| author | Nicholas Shawen Megan K. O’Brien Sanjeev Venkatesan Luca Lonini Tanya Simuni Jamie L. Hamilton Roozbeh Ghaffari John A. Rogers Arun Jayaraman |
| author_facet | Nicholas Shawen Megan K. O’Brien Sanjeev Venkatesan Luca Lonini Tanya Simuni Jamie L. Hamilton Roozbeh Ghaffari John A. Rogers Arun Jayaraman |
| author_sort | Nicholas Shawen |
| collection | DOAJ |
| description | Abstract Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices. |
| first_indexed | 2024-12-13T12:37:00Z |
| format | Article |
| id | doaj.art-c1ea3fc8773644f395049fb946403f80 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-13T12:37:00Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-c1ea3fc8773644f395049fb946403f802022-12-21T23:45:49ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032020-04-0117111410.1186/s12984-020-00684-4Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensorsNicholas Shawen0Megan K. O’Brien1Sanjeev Venkatesan2Luca Lonini3Tanya Simuni4Jamie L. Hamilton5Roozbeh Ghaffari6John A. Rogers7Arun Jayaraman8Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLabDepartment of Neurology, Northwestern UniversityThe Michael J. Fox Foundation for Parkinson’s ResearchCenter for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern UniversityCenter for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern UniversityMax Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLabAbstract Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.http://link.springer.com/article/10.1186/s12984-020-00684-4Parkinson’s diseaseWearable sensorsSoft wearablesMachine learningSymptom detectionTremor |
| spellingShingle | Nicholas Shawen Megan K. O’Brien Sanjeev Venkatesan Luca Lonini Tanya Simuni Jamie L. Hamilton Roozbeh Ghaffari John A. Rogers Arun Jayaraman Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors Journal of NeuroEngineering and Rehabilitation Parkinson’s disease Wearable sensors Soft wearables Machine learning Symptom detection Tremor |
| title | Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors |
| title_full | Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors |
| title_fullStr | Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors |
| title_full_unstemmed | Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors |
| title_short | Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors |
| title_sort | role of data measurement characteristics in the accurate detection of parkinson s disease symptoms using wearable sensors |
| topic | Parkinson’s disease Wearable sensors Soft wearables Machine learning Symptom detection Tremor |
| url | http://link.springer.com/article/10.1186/s12984-020-00684-4 |
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