Related Experiment Video
Updated: Feb 22, 2026
10:28
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
16.4K
Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales
Alejandro Rodríguez-Molinero1,2, Albert Samà3,4, Carlos Pérez-López3,4
1Fundació Privada Sant Antoni Abat, Consorci Sanitari del Garraf, Vilanova i la Geltrú, Spain.
Frontiers in Neurology
|September 19, 2017
Summary
A new device using accelerometer data accurately detects Parkinson's disease (PD) motor fluctuations by analyzing gait. Its correlation with the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) supports its use for patient monitoring.
Area of Science:
- Biomedical Engineering
- Neurology
- Wearable Technology
Background:
- A novel monitoring device utilizing accelerometer measurements was developed.
- The device algorithm characterizes gait through stride frequency content to detect Parkinson's disease (PD) motor fluctuations (On/Off states).
Purpose of the Study:
- To validate the developed algorithm by assessing its correlation with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III).
Main Methods:
- Seventy-five PD patients wore an inertial sensor at the waist during walking in both Off and On states.
- Patients also underwent the UPDRS-III motor examination in both states.
- Spearman's correlation coefficient was used to evaluate algorithm-scale convergence.
Main Results:
- Moderate correlation (rho -0.56) was found between the algorithm and the overall UPDRS-III.
- Good correlation (rho -0.73) was observed between algorithm outputs and the UPDRS-III gait item.
- A significant correlation (rho -0.67) was noted with UPDRS-III Factor 1 (axial function, balance, gait).
Conclusions:
- The algorithm's correlation with the UPDRS-III suggests its potential as a valuable tool for monitoring PD patients experiencing motor fluctuations.
