Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?

Rana Zia Ur Rehman, Christopher Buckley, Maria Encarna Micó-Amigo, Cameron Kirk, Michael Dunne-Willows, Claudia Mazzà, Jian Qing Shi, Lisa Alcock, Lynn Rochester, Silvia Del Din

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)
27 Downloads (Pure)


Impact Statement:
Gait characteristics quantified with wearable devices paired with machine learning models can be used as tool in early clinical management of Parkinson's disease.

Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.
Original languageEnglish
Pages (from-to)65-73
Number of pages9
JournalIEEE Open Journal of Engineering in Medicine and Biology
Publication statusPublished - 21 Jan 2020
Externally publishedYes


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