Robert A Needham1, Roozbeh Naemi2, Joseph Hamill3, Nachiappan Chockalingam2. 1. Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Leek Road, Stoke on Trent, ST4 2DF, UK. Electronic address: r.needham@staffs.ac.uk. 2. Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Leek Road, Stoke on Trent, ST4 2DF, UK. 3. Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Leek Road, Stoke on Trent, ST4 2DF, UK; School of Public Health and Health Sciences, University of Massachusetts Amherst, 30 Eastman Lane, Amherst 01003, MA, USA.
Abstract
OBJECTIVE: Vector coding is a non-linear data analysis technique that quantifies inter-segmental coordination and coordination variability. The traditional approach of reporting time-series data from vector coding can be problematic when overlaying multiple trials on the same illustration. The objective of this study was to describe and present novel data visualisations for displaying the coordination pattern, segmental dominancy, range of motion on an angle-angle diagram, and coordination variability. This allows for a comparison of data across multiple participants with a focus on single subject analysis. METHODS: Novel data visualisation techniques that involve the use of colour and data bars to map and profile coordination pattern and coordination variability data. The introduction and profiling of inter-data point range of motion quantifies range of motion of the dominant segment on an angle-angle plot and illustrates patterns of movement control. As an example, the dataset used the Istituto Ortopedico Rizzoli foot model to describe rearfoot-forefoot and shank-foot coordination during stance. RESULTS: The use of colour mapping provides the option to inspect an entire dataset and to compare data across multiple participants, groups, and segment couplings. Combining coupling angle mapping with segmental dominancy profiling offers an intuitive and instant summary on coupling angle distribution. The novel inclusion of inter-data point range of motion profiling provides meaning to the interpretation of segmental dominancy data and demonstrates distinct patterns of movement control. CONCLUSIONS: The use of colour mapping and profiling techniques highlighted differences in coordination pattern and coordination variability data across several participants that questions the interpretation and relevance of reporting group data. Colour mapping and profiling techniques are ideal reporting methods to compliment prospective multiple single-subject design studies and to classify commonalities and differences in patterns of coordination and patterns of control between individuals or trials. The data visualisation approaches in the current study may provide further insight on overuse injuries, exercise prescription and rehabilitation interventions.
OBJECTIVE: Vector coding is a non-linear data analysis technique that quantifies inter-segmental coordination and coordination variability. The traditional approach of reporting time-series data from vector coding can be problematic when overlaying multiple trials on the same illustration. The objective of this study was to describe and present novel data visualisations for displaying the coordination pattern, segmental dominancy, range of motion on an angle-angle diagram, and coordination variability. This allows for a comparison of data across multiple participants with a focus on single subject analysis. METHODS: Novel data visualisation techniques that involve the use of colour and data bars to map and profile coordination pattern and coordination variability data. The introduction and profiling of inter-data point range of motion quantifies range of motion of the dominant segment on an angle-angle plot and illustrates patterns of movement control. As an example, the dataset used the Istituto Ortopedico Rizzoli foot model to describe rearfoot-forefoot and shank-foot coordination during stance. RESULTS: The use of colour mapping provides the option to inspect an entire dataset and to compare data across multiple participants, groups, and segment couplings. Combining coupling angle mapping with segmental dominancy profiling offers an intuitive and instant summary on coupling angle distribution. The novel inclusion of inter-data point range of motion profiling provides meaning to the interpretation of segmental dominancy data and demonstrates distinct patterns of movement control. CONCLUSIONS: The use of colour mapping and profiling techniques highlighted differences in coordination pattern and coordination variability data across several participants that questions the interpretation and relevance of reporting group data. Colour mapping and profiling techniques are ideal reporting methods to compliment prospective multiple single-subject design studies and to classify commonalities and differences in patterns of coordination and patterns of control between individuals or trials. The data visualisation approaches in the current study may provide further insight on overuse injuries, exercise prescription and rehabilitation interventions.
Authors: Christina Zong-Hao Ma; Tian Bao; Christopher A DiCesare; Isaac Harris; April Chambers; Peter B Shull; Yong-Ping Zheng; Rakie Cham; Kathleen H Sienko Journal: Sensors (Basel) Date: 2022-05-10 Impact factor: 3.847