Skip to main content

Gait Evaluation using Procrustes and Euclidean Distance Matrix Analysis.

Anwary, A.R., Yu, H. and Vassallo, M., 2019. Gait Evaluation using Procrustes and Euclidean Distance Matrix Analysis. IEEE Journal of Biomedical and Health Informatics, 23 (5), 2021-2029.

Full text available as:

08528389.pdf - Accepted Version


Official URL: Volume: 23 , Issue: 5 , Sept. 2019 )

DOI: 10.1109/JBHI.2018.2875812


IEEE Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two Inertial Measurement Unit (IMU) sensors through Bluetooth connectivity. The data is collected from 12 young (10 for modelling and 2 for validation) and 20 older subjects. We analyse the data collected from real world for stride, step, stance and swing gait features. We validate our method with measurements of gait features. Generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for 10 young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance and Root mean square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form which is considered as a standard normal mean gait form (NMGF) and inter-feature distances are estimated from the set of 10 young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method.

Item Type:Article
Group:Faculty of Science & Technology
ID Code:31497
Deposited By: Symplectic RT2
Deposited On:26 Nov 2018 15:59
Last Modified:14 Mar 2022 14:13


Downloads per month over past year

More statistics for this item...
Repository Staff Only -