Motion Sensor Dyskinesia Assessment During Activities of Daily Living
Open accessAbstract
BACKGROUND: Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinson's disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement. OBJECTIVE: To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living. METHODS: Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores. RESULTS: Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used. CONCLUSIONS: A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.
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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.
“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [25]
Moderate-to-strong correlations were reported between dyskinesia detected from wearables and clinical ratings.
“Moderate-to-strong correlations were reported between dyskinesia detected from wearables and clinical ratings [24–26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [25]
Gyroscope signals have shown promise for increasing the sensitivity and specificity when measuring dyskinesia and postural instability in laboratory settings.
“Predominantly acceleration signals were used in hospital and free-living settings, even though gyroscope signals have shown promise for increasing the sensitivity and specificity when measuring dyskinesia and postural instability in laboratory settings [23–26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [25]
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Self-citation
- Authors
- Pulliam, C. L., Burack, M. A., Heldman, D. A., Giuffrida, J. P., & Mera, T. O.
- Affiliations
- Great Lakes NeuroTechnologies (United States) (US)
- University of Rochester Medicine (US)
- Corresponding author
- Pulliam, Christopher L.
- Venue
- Journal of Parkinson's Disease
- Publisher
- SAGE Publications
- Published
- 2014
- Volume
- 4
- Issue
- 4
- Pages
- 609-615
- ISSN
- 1877-7171 (ISSN-L)
- 1877-718X
- PMC
- PMC11497122
- OpenAlex
- W1837130411
- Cited by
- 46
- References
- 22
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 4 · 2016: 7 · 2015: 2
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Parkinson s Disease (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health
- Medical Center, University of Rochester
- National Institute on Aging (R44AG044293)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- dyskinesia, Parkinson's disease, motion sensors, activities of daily living, abnormal involuntary movements, kinematic parameters, wearable sensors, symptom fluctuation, wrist-worn sensors
- MeSH terms
- Activities of Daily Living, Adult, Aged, Algorithms, Antiparkinson Agents, Female, Hand, Humans, Levodopa, Male, Middle Aged, Motion Perception, Movement, Parkinson Disease, Severity of Illness Index, Dyskinesias
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