Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Abstract

This paper presents an application of Functional Principal Component Analysis (FPCA) to describe inter-subject variability of multiple waveforms. This technique was applied to the study of sit-to-stand movement in two groups of people, osteoarthritic patients and healthy subjects. Although STS movement has not been much applied to the study of knee osteoarthritis, it can provide relevant information about the effect of osteoarthritis disease on knee joint function. Two waveforms, knee flexion angle and flexion moment, were simultaneously analysed. Instead of using the common multivariate approach we used the functional one, which allows working with continuous functions without neither discretization nor time scale normalization. The results show that time-scale normalization can alter the FPCA solution. Furthermore, FPCA presents a better discriminatory power compared to the classical multivariate approach. Then, this technique can be applied as a functional assessment tool, allowing the identification of relevant variables to discriminate heterogeneous groups, such as healthy and pathological subjects