Objective Motion Sensor Assessment Highly Correlated with Scores of Global Levodopa-Induced Dyskinesia in Parkinson's Disease
Open accessAbstract
Background: Chronic use of medication for treating Parkinson's disease (PD) can give rise to peak-dose dyskinesia. Adjustments in medication often sacrifice control of motor symptoms, and thus balancing this trade-off poses a significant challenge for disease management. Objective: To determine whether a wrist-worn motion sensor unit could be used to ascertain global dyskinesia severity over a levodopa dose cycle and to develop a severity scoring algorithm highly correlated with clinician ratings. Methods: Fifteen individuals with PD were instrumented with a wrist-worn motion sensor unit, and data were collected with arms in resting and extended positions once every hour for three hours after taking a levodopa dose. Two neurologists blinded to treatment status viewed subject videos and rated global and upper extremity dyskinesia severity based on the modified Abnormal Involuntary Movement Scale (mAIMS). Linear regression models were developed using kinematic features extracted from motion sensor data and extremity, global, or combined (average of extremity and global) mAIMS scores. Results: Dyskinesia occurring during a levodopa dose cycle was successfully measured using a wrist-worn sensor. The logarithm of the power spectrum area between 0.3–3 Hz and the combined clinician scores resulted in the best model performance, with a correlation coefficient between clinician and model scores of 0.81 and root mean square error of 0.55, both averaged across the arms resting and extended postures. Conclusions: One sensor unit worn on either hand can effectively predict global dyskinesia severity during the arms resting or extended positions.
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In Parkinson's disease, wearables were used to detect and quantify medication-evoked adverse symptoms such as dyskinesia and motor fluctuations.
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From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [27]
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BackgroundMethodSelf-citationIncremental Learning in Time-series Data using Reinforcement Learning
Shuqair, M., Shahed, J. J., & Ghoraani, B. (2022) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw58026.2022.00115 · Cited by 7 · see citing works
“A total of 12 PD patients were selected as a case study for the proposed approach following a protocol that was designed to collect movement data from the patients in their medication OFF state as transitioned to medication ON state and back to OFF state [12] [13].”
MethodCurrent perspectives on the role of telemedicine in the management of Parkinson’s disease
Eisenberg, J. L., Hou, J. G., & Barbour, P. J. (2018) · Smart Homecare Technology and TeleHealth · doi:10.2147/shtt.s152018 · Cited by 7 · see citing works
Escudeiro, G. P., Andrade, A. D. O., Cabral, A. M., et al. (2021) · Research Society and Development · doi:10.33448/rsd-v10i15.22825 · Cited by 6 · see citing works
“(Arora et al., 2015; Mera et al., 2013; Rigas et al., 2012) This study was approved by the Institutional Ethics Committee (legal opinion 4,540,114) of the Fluminense Federal University.”
MethodParkinson's disease medication state management using data fusion of wearable sensors
Ramji, V., Hssayeni, M. D., Burack, M. A., & Ghoraani, B. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897238 · Cited by 6 · see citing works
“Fifteen PD patients with motor fluctuations participated in a study under a protocol [11].”
MethodSelf-citationTailoring the use of wearable systems and telehealth for Parkinson's disease
Peacock, D. J. S. J., Yoneda, J. R. K., Thomson, V., et al. (2021) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2021.07.004 · Cited by 6 · see citing works
Deep Regression Modeling for Imbalanced and Incomplete Time-Series Data
Hssayeni, M. D., & Ghoraani, B. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3372435 · Cited by 6 · see citing works
Shuqair, M., Shahed, J. J., & Ghoraani, B. (2024) · Bioengineering · doi:10.3390/bioengineering11070689 · Cited by 7 · see citing works
“Motion data were captured from 24 individuals diagnosed with idiopathic PD as they engaged in various activities of daily living (ADL) following a carefully designed protocol [26,27].”
Mobile health technologies for the management of Parkinson’s disease
Bougea, A. M. (2025) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2025.2580468 · Cited by 4 · see citing works
Wearable-based Mediation State Detection in Individuals with Parkinson's DiseasePreprint
Hssayeni, M. D., Burack, M. A., D., M., Shahed, J. J., D., M., Ghoraani, B., & D, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06973 · Cited by 3 · see citing works
“6 Peak in the power spectral density [23, 17]”
BackgroundMethodSelf-citationCould New Generations of Sensors Reshape the Management of Parkinson’s Disease?
Levin, O. S., Яковлева, О. В., Coloman, I. I., & Kuzmina, A. V. (2021) · Clinical and Translational Neuroscience · doi:10.3390/ctn5020018 · Cited by 2 · see citing works
“The kinesia system provides information about tremors, dyskinesia, and mobility based on temporal and frequency features [87].”
BackgroundAdvancing Parkinson's Disease Management Through Multi-Shared-Task Self-Supervised Signal Processing
Shuqair, M., Shahed, J. J., & Ghoraani, B. (2024) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf60004.2024.10942713 · Cited by 1 · see citing works
“A total of 526 minutes of motion data was captured from 24 individuals diagnosed with PD while they performed activities of daily living (ADL) [16], [17].”
Wearable Sensor Configurations for Effective Tremor Assessment in Parkinson’s Disease
Reardon, S., Shuqair, M., Shahed, J. J., & Ghoraani, B. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782867 · Cited by 2 · see citing works
“Motion data was captured from 24 PD patients [11], [12]. used protocols approved by the Institutional Review Board (IRB) at Rochester Medical Center, and following the Helsinki Declaration, the subjects signed an informed consent.”
Shuqair, M., Shahed, J. J., & Ghoraani, B. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782431 · Cited by 1 · see citing works
“A study protocol was employed to record motion data from 24 individuals (Table I) diagnosed with PD as they engaged in activities of daily living (ADL) [8], [9].”
Chen, F., Wen, S., Chen, X., et al. (2025) · European Journal of Neurology · doi:10.1111/ene.70329 · Cited by 1 · see citing works
Tensor decomposition of multi-channel wearable sensors for Parkinson’s disease assessment
Ramji, V. (2016) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 1 · see citing works
Motion Sensor Dyskinesia Assessment During Activities of Daily Living (P3.070)
Pulliam, C. L., Burack, M. A., Giuffrida, J. P., & Mera, T. O. (2014) · Neurology · doi:10.1212/wnl.82.10_supplement.p3.070
“Previously, accelerometers, gyroscopes, and electromyographic (EMG) recordings have been used to obtain objective measures of PD symptoms, including tremor, dyskinesia, and bradykinesia [8–15].”
MethodInfluentialSelf-citationContinuous Parkinsonian Tremor Estimation Using Motion Data
Hssayeni, M. D., Shahed, J. J., Burack, M. A., & Ghoraani, B. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969093
“[8], [18] and was approved by the Institutional Review Board at Rochester Medical Center.”
MethodSelf-citationDopaminergic Co-transmission with Sonic Hedgehog Inhibits Abnormal Involuntary MovementsPreprint
Malave, L. B., Zuelke, D. R., Uribe‐Cano, S., Starikov, L., Rebholz, H., Friedman, E., Qin, C., Li, Q., Bézard, E., & Kottmann, A. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.09.983759
Multi-channel Time-series Transformer for Wearable Monitoring of Rigidity in Parkinson’s Disease
Forde, J., Shuqair, M., Shahed, J. J., et al. (2025) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins65080.2025.11125736
“The sensors were placed on the wrist and ankle of the most affected limb, depending on the patient [6] [7].”
Forde, J., Shuqair, M., Shahed, J. J., et al. (2025) · doi:10.1109/embc58623.2025.11251605
“Following IRB approval and receipt of patient consent, the clinical data collection process involved placing wearables (Great Lakes NeuroTechnologies, Cleveland, OH) on the wrist and ankle exhibiting greatest tremor severity for each of the 24 patients [5] [6].”
Begur, N., Shuqair, M., Verbist, A., et al. (2025) · doi:10.1109/embc58623.2025.11253518
“The dataset includes 3.5 hours of data collected from 15 individuals diagnosed with PD, nine males and six females, aged between 42 and 77 years, and disease duration varying from 3.5 to 17 years [11].”
InfluentialVirbel-Fleischman, C. (2021) · theses.fr (ABES) · doi:10.70675/21583008z772az4c86z822dza5cca46513d5
Smartwatch-Based Screening to Identify Candidates for Deep Brain Stimulation in Parkinson’s Disease
Fadil, R., Walter, B. L., Kuhlman, G. D., Zaidi, S. A., & Heldman, D. A. (2026) · Digital Biomarkers · doi:10.1159/000553397
“Our previously validated KinesiaU smartwatch algorithms extract objective features from motion data that are measures of these same factors, and previous studies showed that these measures were highly correlated with clinical tremor, bradykinesia, and dyskinesia scores [19, 24, 26, 28].”
- Authors
- Mera, T. O., Burack, M. A., & Giuffrida, J. P.
- Affiliations
- Great Lakes NeuroTechnologies (United States) (US)
- University of Rochester Medicine (US)
- Corresponding author
- Mera, Thomas O.
- Venue
- Journal of Parkinson’s Disease
- Publisher
- SAGE Publications
- Published
- 2013-01-01
- Volume
- 3
- Issue
- 3
- Pages
- 399-407
- ISSN
- 1877-7171 (ISSN-L)
- 1877-718X
- OpenAlex
- W1508856497
- Cited by
- 66
- References
- 32
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 9 · 2023: 1 · 2022: 3 · 2021: 10 · 2020: 2 · 2019: 8 · 2018: 6 · 2017: 6 · 2016: 7 · 2015: 3 · 2014: 3
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- green
- doaj.org
- Locations
- Journal of Parkinson s Disease (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Rochester
- Georgia Clinical and Translational Science Alliance
- National Institute of Neurological Disorders and Stroke (R43NS071882)
- National Center for Research Resources (KL2RR024136)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease, levodopa-induced dyskinesia, peak-dose dyskinesia, kinematic parameters, power spectrum analysis
- MeSH terms
- Adult, Aged, Algorithms, Antiparkinson Agents, Biomechanical Phenomena, Dyskinesia, Drug-Induced, Female, Humans, Levodopa, Male, Middle Aged, Movement, Neurologic Examination, Parkinson Disease, Tremor, Wrist, Linear Models
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