Advancements in data analysis and visualisation techniques to support multiple single-subject analyses: an assessment of movement coordination and coordination variability

NEEDHAM, Robert, NAEMI, Roozbeh and CHOCKALINGAM, Nachiappan (2021) Advancements in data analysis and visualisation techniques to support multiple single-subject analyses: an assessment of movement coordination and coordination variability. In: Research into Spinal Deformities 9. Studies in Health Technology and Informatics (280). IOS Press, pp. 146-149. ISBN 978-1-64368-182-5

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Abstract or description

Vector coding is a data analysis technique that quantifies inter-segmental coordination and coordination variability of human movement. The usual reporting of vector coding time-series data can be difficult to interpret when multiple trials are superimposed on the same figure. This study describes and presents novel data visualisations for displaying data from vector coding that supports multiple single- subject analyses. The dataset used in this study describes the lumbar-pelvis coordination in the transverse plane during a gait cycle. The data visualisation techniques presented in this study consists of the use of colour and data bars to map and profile coordination pattern and coordination variability data. The use of colour mapping provides the option to classify commonalities and differences in patterns of coordination between segment couplings and between individuals across a big dataset. Data bars display segmental dominancy data that can provide an intuitive summary on coupling angle distribution over time. The data visualisation in this study may provide further insight on how people with adolescent idiopathic scoliosis perform goal-orientated movements following an intervention, which would support clinical management strategies

Item Type:	Book Chapter, Section or Conference Proceeding
Faculty:	School of Life Sciences and Education > Sport and Exercise
Depositing User:	Robert NEEDHAM
Date Deposited:	03 Sep 2021 14:58
Last Modified:	24 Feb 2023 14:02
URI:	https://eprints.staffs.ac.uk/id/eprint/6988

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