The performance of some machine learning approaches in human movement assessment

Abstract

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.