Abstract
The Y-Balance Test (YBT) is a dynamic balance assessment commonly used in sports medicine. In this research we explore how data from a wearable sensor can provide further insights from YBT performance. We do this in a Case-Based Reasoning (CBR) framework where the assessment of similarity on the wearable sensor data is the key challenge. The assessment of similarity on time-series data is not a new topic in CBR research; however the focus here is on working as close to the raw time-series as possible so that no information is lost. We report results on two aspects, the assessment of YBT performance and the insights that can be drawn from comparisons between pre- and post- injury performance.
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Notes
- 1.
This dataset is available at http://mlg.ucd.ie/ybt.
- 2.
- 3.
Similarity computation with DTW between two time-series of unequal length is handled by padding the shorter time-series with zeroes.
- 4.
Edit Distance and Wagner-Fischer measures requires no size matching as it handles the unequal length of the sequences.
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Acknowledgments
This publication has resulted from research supported in part by a grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3872 and is co-funded under the European Regional Development Fund.
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Appendix
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See Fig. 10.
A comparison between the rest four athletes in the dataset who suffered concussion. (Standing on non-dominant leg, anterior reach, Accel Z data.)
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Mahato, V., Johnston, W., Cunningham, P. (2019). Scoring Performance on the Y-Balance Test. In: Bach, K., Marling, C. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_19
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