Passive sensing of gait and medication-related fluctuations in Parkinson's Disease
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background: Gait impairment is a hallmark symptom of Parkinson’s Disease (PD). Traditional clinical assessments cannot capture real-world motor fluctuations, as they are sparsely performed. We validated the use of nearables, passive sensing
technologies, including Kinect RGB-D cameras and ultra-wideband (UWB) radar, for continuous, objective assessment of gait fluctuations in PD within a home-like setting.
Methods: Fifteen PD patients with mild symptoms and fourteen age- and sex-matched healthy controls (HC) performed 4-metre walking tasks in a living lab facility. Patients repeated the task during “ON” and “OFF” states of their daily medication cycle. Gait features, including stride length, stride time, and gait speed, were extracted from Kinect, radar, and a ground-truth smart floor. Data were analysed to assess inter-sensor agreements and group-level differences.
Results: Stride time demonstrated the highest agreement between devices (r=0.903), while stride length was weaker (r=0.779). Nevertheless, stride length from both Kinect and radar distinguished PD OFF from HC (camera q=0.020; radar q=0.005), and radar
additionally differentiated ON from OFF (q=0.020). Neither device differentiated PD ON from HC, indicating medication reduced observable gait differences.
Conclusions: Although some spatial metrics show device discrepancies, both systems demonstrate sensitivity to gait patterns and medication-dependent changes, supporting their use for longitudinal, real-world monitoring of motor symptoms.
technologies, including Kinect RGB-D cameras and ultra-wideband (UWB) radar, for continuous, objective assessment of gait fluctuations in PD within a home-like setting.
Methods: Fifteen PD patients with mild symptoms and fourteen age- and sex-matched healthy controls (HC) performed 4-metre walking tasks in a living lab facility. Patients repeated the task during “ON” and “OFF” states of their daily medication cycle. Gait features, including stride length, stride time, and gait speed, were extracted from Kinect, radar, and a ground-truth smart floor. Data were analysed to assess inter-sensor agreements and group-level differences.
Results: Stride time demonstrated the highest agreement between devices (r=0.903), while stride length was weaker (r=0.779). Nevertheless, stride length from both Kinect and radar distinguished PD OFF from HC (camera q=0.020; radar q=0.005), and radar
additionally differentiated ON from OFF (q=0.020). Neither device differentiated PD ON from HC, indicating medication reduced observable gait differences.
Conclusions: Although some spatial metrics show device discrepancies, both systems demonstrate sensitivity to gait patterns and medication-dependent changes, supporting their use for longitudinal, real-world monitoring of motor symptoms.
Date Issued
2026-12-01
Date Acceptance
2026-06-17
Citation
Journal of NeuroEngineering and Rehabilitation, 2026, 23 (1)
ISSN
1743-0003
Publisher
BMC
Journal / Book Title
Journal of NeuroEngineering and Rehabilitation
Volume
23
Issue
1
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1186/s12984-026-02068-6
Publication Status
Published
Article Number
213
Date Publish Online
2026-06-23