A machine-learning method for classifying and analyzing foot placement: Application to manual material handling

Abstract

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler’s foot placement strategies with regards to the performed task.