Journal Article

Metric learning for Parkinsonian identification from IMU gait measurements


Diagnosis of people with mild Parkinson’s symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10 metre walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson’s with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions.

Attached files


Cuzzolin, F
Sapienza, M
Esser, P
Saha, S
Collett, J
Dawes, H

Oxford Brookes departments

Faculty of Health and Life Sciences\Department of Sport and Health Sciences
Faculty of Technology, Design and Environment\Department of Computing and Communication Technologies


Year of publication: 2017
Date of RADAR deposit: 2017-02-21

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Related resources

This RADAR resource is the Accepted Manuscript of Metric learning for Parkinsonian identification from IMU gait measurements


  • Owner: Rosa Teira Paz
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live
  • Views (since Sept 2022): 444