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Title:
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THE PERFORMANCE OF SOME MACHINE LEARNING
APPROACHES IN HUMAN MOVEMENT ASSESSMENT |
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Author(s):
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Johan Hagelbäck, Pavlo Liapota, Alisa Lincke and Welf Löwe |
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ISBN:
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978-989-8533-89-0 |
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Editors:
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Mário Macedo |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Statistical, Machine Learning, Human Movement Assessment |
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Type:
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Full Paper |
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First Page:
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35 |
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Last Page:
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42 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of
human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to
train models for the automated assessment of new movements. However, there are many variations in selecting the
approaches and setting the parameters for achieving high performance, i.e., high accuracy and low response time. The
present paper researches the design space and the impact of approaches of statistical and machine learning on accuracy
and response time in human movement assessment. Results show that a random forest regression approach outperforms
linear regression, support vector regression and neuronal network approaches. Since the results do not rely on the
movement specifics, they can help improving the performance of automated human movement assessment, in general. |
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