Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Open accessAbstract
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.
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Patel et al. use SVMs to identify patients with PD from accelerometer data.
“Patel et al. 11 use SVMs to identify patients with PD from accelerometer data.”
From Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures (2016) · cites this paper as [11]
The Abstract and Methods section C confirm that Patel et al. used SVMs with accelerometer features. They used them to estimate the severity of tremor, bradykinesia, and dyskinesia in patients with Parkinson’s disease, not to identify which people had the disease. (LLM, conf=0.88)
Experimental — accuracy not yet measuredQuantitative objective digital tremor biomarkers can support neurosurgeons and neurologists and generate more precise and continuous data.
“We [7] and others [8][9] have previously shown that quantitative objective digital tremor biomarkers can support neurosurgeons and neurologists and generate more precise and continuous data.”
From An online movement and tremor identification algorithm for evaluation during deep brain stimulation (2022) · cites this paper as [9]
The Methods and Discussion and Conclusions (pages unavailable) describe objective accelerometer-based estimates of tremor severity and their potential use in tracking changes relevant to clinical management. The study does not show that the biomarkers support neurosurgeons specifically or that they produce continuous data; its reported recordings lasted 30 seconds, with testing at 30-minute intervals. It also does not establish that the estimates are more precise than clinicians’ assessments. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe feasibility of using data recorded with accelerometers to estimate the severity of symptoms and motor complications in patients with Parkinson's disease has been shown recently.
“The feasibility of using data recorded with accelerometers to estimate the severity of symptoms and motor complications in patients with Parkinson’s disease has been shown recently [27].”
From Neurological Tremor: Sensors, Signal Processing and Emerging Applications (2010) · cites this paper as [27]
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Background
- Authors
- Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., & Bonato, P.
- Affiliations
- Harvard University (US)
- University of Alabama at Birmingham (US)
- Arizona State University (US)
- Northeastern University (US)
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2009-11
- Volume
- 13
- Issue
- 6
- Pages
- 864-873
- ISSN
- 1089-7771 (ISSN-L)
- PMC
- PMC5432434
- OpenAlex
- W2162592883
- Cited by
- 623
- References
- 25
- Citations by year
- 2026: 21 · 2025: 31 · 2024: 18 · 2023: 18 · 2022: 37 · 2021: 37 · 2020: 43 · 2019: 43 · 2018: 43 · 2017: 51 · 2016: 43 · 2015: 42 · 2014: 47 · 2013: 45 · 2012: 39
- FWCI
- 38.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (0546338)
- Intel Corporation
- National Institute of Neurological Disorders and Stroke (R21NS045410, R21NS045401-02)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, motor fluctuations, wearable sensors, accelerometer, tremor severity, bradykinesia, dyskinesia, motor complications, support vector machine, wireless body area networks, feature selection, computational cost
- MeSH terms
- Acceleration, Aged, Algorithms, Artificial Intelligence, Clothing, Humans, Middle Aged, Parkinson Disease, Severity of Illness Index, Signal Processing, Computer-Assisted, Telemetry, Video Recording, Monitoring, Ambulatory, Dyskinesias
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