title: Automatic algorithm for filtering kinematic signals with impacts in the Wigner representation.
abstract: An automatic filtering algorithm is proposed for the accurate estimation of the second derivatives of kinematic signals with impacts. The impacts considered here occur when a moving object hits a rigid surface. The algorithm performs time-frequency filtering in the Wigner representation, to deal efficiently with the non-stationarities caused by such impacts, and adjusts the parameters of its time-frequency filtering function so that the filtering process adapts to the individual characteristics of the signal in hand. Performance analysis and comparative evaluation with experimentally acquired kinematic impact signals demonstrated a higher accuracy, with performance advantages over two widely used conventional automatic methods: linear phase autoregressive model-based derivative assessment (LAMBDA) and generalised cross-validation using quintic splines (GCVQS). For high impacts, the average absolute relative error in estimating the peak acceleration was 5.7% with the proposed method, 17.2% with a Butterworth low-pass filter optimised to yield minimum overall acceleration RMS error (best-case result), 18.3% with the LAMBDA method, and 37.2% with the GCVQS method. For signals with low impacts, the average absolute relative error was 19.4%, 6.9%, 8.3% and 19.1%, respectively, in each case, which indicates that, for signals with a low-frequency content, there is no need for such time-frequency filtering.
authors.affiliation: Department of Communication Systems, Lancaster University, Lancaster, UK. a.georgakis@lancaster.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Georgakis
authors.forename: L K
authors.initials: LK
authors.lastname: Stergioulas
authors.forename: G
authors.initials: G
authors.lastname: Giakas
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted