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

摘要

This paper presents an application of Functional Principal Component Analysis (FPCA) todescribe inter-subject variability of multiple waveforms. This technique was applied to the studyof 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 canprovide relevant information about the effect of osteoarthritis disease on knee joint function.Two waveforms, knee flexion angle and flexion moment, were simultaneously analysed. Instead ofusing the common multivariate approach we used the functional one, which allows working withcontinuous functions without neither discretization nor time scale normalization.The results show that time-scale normalization can alter the FPCA solution. Furthermore, FPCApresents a better discriminatory power compared to the classical multivariate approach. Then, thistechnique can be applied as a functional assessment tool, allowing the identification of relevantvariables to discriminate heterogeneous groups, such as healthy and pathological subjects