Alignment and parameterization of single cycle motion data. Issue 15 (2021)

• Record Type: Journal Article
• Authors: Duquesne, Kate; Roeck, Joris De; Salazar-Torres, Jose-de-Jesus; Audenaert, Emmanuel
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 15(2021)
• Page Start: 436
• Page End: 441
• Publication Date: 2021
• Subjects: PCA -- Variability -- Statistics -- Cyclic motion -- Human Gait -- Single cycle; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.10.295
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Motion capturing systems produce a large amount of information on the motion of individuals. A growing number of data reduction techniques have been developed to reduce the amount of data while keeping relevant information. An overview that compares and identifies the advantages and disadvantages of these methods on cyclic motion data is, however, lacking. Therefore, this study aims to assess the features of different data reduction techniques by applying them to a large public gait data set. Due to the periodicity of cyclic data, an individual cycle can be isolated and analyzed. The analysis of single cycles requires preprocessing steps to segment and align the individual cycles. The latter is needed to isolate the amplitude variability. Three alignment procedures with different complexity, namely Linear Length Normalization (LLN), Piecewise LNN (PLLN) and Continuous Registration (CR), are assessed based on the amount of resulting variation. Subsequently three data reduction techniques (i.e. Principal Component Analysis (PCA), Principal Polynomial Analysis (PPA) and Multivariate Functional PCA (MFPCA)) are applied to the aligned single gait cycles. The data reduction techniques are evaluated based on the in-sample error, the out-of-sample error, the compactness and the computation time to produce a model. The curves aligned with CR have the lowest remaining variation and thus the lowest amount of remaining phase variation. The differences between the different data reduction techniques appear to be minimal. PPA shows to be the most compact and is therefore recommended when compactness is crucial and out-of-sample performance is less essential. The use of MFPCA is advised when one wants to include data from different sources. PCA is suggested when computation time is key.