Toggle Main Menu Toggle Search

Open Access padlockePrints

Accelerometer-based gait assessment: pragmatic deployment on an international scale

Lookup NU author(s): Dr Silvia Del DinORCiD, Aodhan Hickey, Dr Simon Woodman, Dr Hugo Hiden, Rosie Morris, Professor Paul WatsonORCiD, Professor Kianoush Nazarpour, Professor Mike Catt, Professor Lynn RochesterORCiD, Dr Alan Godfrey



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2016.

For re-use rights please refer to the publisher's terms and conditions.


Gait is emerging as a powerful tool to detect early disease and monitor progression across a number of pathologies. Typically quantitative gait assessment has been limited to specialised laboratory facilities. However, measuring gait in home and community settings may provide a more accurate reflection of gait performance because: (1) it will not be confounded by attention which may be heightened during formal testing; and (2) it allows performance to be captured over time. This work addresses the feasibility and challenges of measuring gait characteristics with a single accelerometer based wearable device during free-living activity. Moreover, it describes the current methodological and statistical processes required to quantify those sensitive surrogate markers for ageing and pathology. A unified framework for large scale analysis is proposed. We present data and workflows from healthy older adults and those with Parkinson’s disease (PD) while presenting current algorithms and scope within modern pervasive healthcare. Our findings suggested that free-living conditions heighten between group differences showing greater sensitivity to PD, and provided encouraging results to support the use of the suggested framework for large clinical application.

Publication metadata

Author(s): Del Din S, Hickey A, Woodman S, Hiden H, Morris R, Watson P, Nazarpour K, Catt M, Rochester L, Godfrey A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Workshop on Statistical Signal Processing (SSP 2016)

Year of Conference: 2016

Online publication date: 25/08/2016

Acceptance date: 27/04/2016

Date deposited: 31/10/2016

Publisher: IEEE


DOI: 10.1109/SSP.2016.7551794

Library holdings: Search Newcastle University Library for this item

ISBN: 9781467378031