Instrumented gait assessment with a single wearable: An introductory tutorial

Silvia Del Din, Aodhan Hickey, Cassim Ladha, Sam Stuart, Alan K. Bourke, Patrick Esser, Lynn Rochester, Alan Godfrey*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
16 Downloads (Pure)

Abstract

Background: Gait is a powerful tool to identify ageing and track disease progression. Yet, its high resolution measurement via traditional instruments remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearables where the synergy between devices and smart algorithms has provided the potential of 'a gait lab on a chip'. Methods: Commercially available wearables for gait quantification remain expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive assessment required within intervention or epidemiological studies. However, the increasing demand for low-cost diagnostics has fuelled the shift in how health-related resources are distributed. As such we adopt open platform technology and validated research methodologies to harmonise engineering solutions to satisfy current epidemiological needs. Results: We provide an introduction to conduct a routine instrumented gait assessment with a discrete, low-cost, accelerometer-based wearable. We show that the capture and interpretation of raw gait signals with a common scripting language can be straightforward and suitable for use within modern studies. We highlight the best approaches and hope that this will help compliment any analytical tool-kit as part of future cohort assessments. Conclusions: Deployment of wearables can allow accurate gait assessment in accordance with advocated methods of data collection as there is a strong demand for sensitive outcomes derived from pragmatic tools. This tutorial shows that instrumentation of gait using a single open source wearable is pragmatic due to low-cost and translational analytical methods to derive sensitive outcomes.

Original languageEnglish
Article number2323
JournalF1000Research
Volume5
DOIs
Publication statusPublished - 14 Sep 2016

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