Dyskinesia detection and monitoring by a single sensor in patients with Parkinson's disease
Abstract
Objective: In current clinical practice, assessment of levodopa‐induced dyskinesias (LIDs) in Parkinson's disease (PD) is based on semiquantitative scales or patients’ diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist‐worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings. Methods: Forty‐six PD patients on l‐dopa therapy, 18 de novo PD patients, and 18 healthy controls were enrolled. Patients underwent clinical assessment of motor signs and dyskinesias and kinetic‐dynamic l‐dopa monitoring, tracked by serial measurements of plasma drug concentrations and motor and postural tests. Results: A subset of features was selected, which showed excellent reliability. Sensitivity and specificity of the selected features for dyskinesia recognition were assessed in both supervised and unsupervised settings with an accuracy of 95% and 86%, respectively. Conclusions: Our preliminary findings suggest that it is feasible to design a reliable sensor‐based application for dyskinesia monitoring at home. © 2015 International Parkinson and Movement Disorder Society
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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 [24]
Dyskinesia measures derived from wearables discriminated between patients with and without dyskinesia.
“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [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 [24]
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 [24]
Measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with Parkinson's disease and healthy controls.
“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [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 [24]
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 [24]
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- Authors
- Lopane, G., Mellone, S., Chiari, L., Cortelli, P., Calandra‐Buonaura, G., & Contin, M.
- Affiliations
- Istituto delle Scienze Neurologiche di Bologna (IT)
- University of Bologna (IT)
- IRCCS–Institute of Neurological Sciences of Bologna Bologna Italy
- Corresponding author
- Contin, Manuela
- Venue
- Movement Disorders
- Publisher
- Wiley
- Published
- 2015-07-14
- Volume
- 30
- Issue
- 9
- Pages
- 1267-1271
- ISSN
- 0885-3185 (ISSN-L)
- 1531-8257
- OpenAlex
- W1767762454
- Cited by
- 43
- References
- 22
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 7 · 2018: 7 · 2017: 5 · 2016: 5 · 2015: 2
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Movement Disorders (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- levodopa-induced dyskinesia, Parkinson's disease, inertial sensors, postural sway, L-dopa therapy, home monitoring, sensor-based detection
- MeSH terms
- Antiparkinson Agents, Dyskinesia, Drug-Induced, Female, Humans, Levodopa, Male, Parkinson Disease, Posture, Psychomotor Performance, Severity of Illness Index, Reproducibility of Results, Signal Detection, Psychological
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