Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter?

Abstract

This study examines the influence of time series duration on the discriminative power of center-of-pressure (COP) features in distinguishing different population groups via statistical tests and machine learning (ML) models. We used two COP datasets, each containing two groups. One was collected from older adults with low or high risk of falling (dataset I), and the other from healthy and post-stroke adults (dataset II). Each time series was mapped into a vector of 34 features twice: firstly, using the original duration of 60 s, and then using only the first 30 s. We then compared each feature across groups through traditional statistical tests. Next, we trained six popular ML models to distinguish between the groups using features from the original signals and then from the shorter signals. The performance of each ML model was then compared across groups for the 30 s and 60 s time series. The mean percentage of features able to discriminate the groups via statistical tests was 26.5% smaller for 60 s signals in dataset I, but 13.5% greater in dataset II. In terms of ML, better performances were achieved for signals of 60 s in both datasets, mainly for similarity-based algorithms. Hence, we recommend the use of COP time series recorded over at least 60 s. The contribution of this paper also include insights into the robustness of popular ML models to the sampling duration of COP time series.