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Titolo:
ESTIMATING VELOCITIES AND ACCELERATIONS OF ANIMAL LOCOMOTION - A SIMULATION EXPERIMENT COMPARING NUMERICAL DIFFERENTIATION ALGORITHMS
Autore:
WALKER JA;
Indirizzi:
FIELD MUSEUM NAT HIST,DEPT ZOOL,ROOSEVELT RD & LAKE SHORE DR CHICAGO IL 60605
Titolo Testata:
Journal of Experimental Biology
fascicolo: 7, volume: 201, anno: 1998,
pagine: 981 - 995
SICI:
0022-0949(1998)201:7<981:EVAAOA>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Soggetto:
FAST-START PERFORMANCE; ANGELFISH PTEROPHYLLUM-EIMEKEI; NOISY BIOMECHANICAL DATA; TROUT SALMO-GAIRDNERI; PIKE ESOX-LUCIUS; GASTEROSTEUS-ACULEATUS; MOVEMENT ANALYSIS; DISPLACEMENT DATA; ESCAPE RESPONSES; SPLINE FUNCTIONS;
Keywords:
LOCOMOTION; VELOCITY; ACCELERATION; PERFORMANCE; BIOMECHANICS; MOVEMENT ANALYSIS; FAST START; FOURIER ANALYSIS; SPLINE ANALYSIS; FILTERS; SIMULATED DATA;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
55
Recensione:
Indirizzi per estratti:
Citazione:
J.A. Walker, "ESTIMATING VELOCITIES AND ACCELERATIONS OF ANIMAL LOCOMOTION - A SIMULATION EXPERIMENT COMPARING NUMERICAL DIFFERENTIATION ALGORITHMS", Journal of Experimental Biology, 201(7), 1998, pp. 981-995

Abstract

Functional biologists employ numerical differentiation for many purposes, including (1) estimation of maximum velocities and accelerations as measures of behavioral performance, (2) estimation of velocity and acceleration histories for biomechanical modeling, and (3) estimation of curvature, either of a structure during movement or of the path of movement itself. I used a computer simulation experiment to explore the efficacy of ten numerical differentiation algorithms to reconstruct velocities and accelerations accurately from displacement data, These algorithms include the quadratic moving regression (MR), two variants of an automated Butterworth filter (BF1-2), four variants of a method based on the signal's power spectrum (PSA1-4), an approximation to theWiener filter due to Kosarev and Pantos (KPF), and both a generalizedcross-validatory (GCV) and predicted mean square error (MSE) quintic spline. The displacement data simulated the highly aperiodic escape responses of a rainbow trout Oncorhynchus mykiss and a Northern pike Esox lucius (published previously). I simulated the effects of video speed (60, 125, 250, 500 Hz) and magnification (0.25, 0.5, 1 and 2 screen widths per body length) on algorithmic performance. Four performance measures were compared: the per cent error of the estimated maximum velocity (V-max) and acceleration (A(max)) and the per cent root mean square error over the middle 80 % of the velocity (V-RMSE) and acceleration (A(RMSE)) profiles. The results present a much more optimistic rolefor numerical differentiation than suggested previously. Overall, thetwo quintic spline algorithms performed best, although the rank orderof the methods varied with video speed and magnification. The MSE quintic spline was extremely stable across the entire parameter space andcan be generally recommended. When the MSE spline was outperformed byanother algorithm, both the difference between the estimates and the errors from true values were very small. At high video speeds and low video magnification, the GCV quintic spline proved unstable. KPF and PSA2-4 performed well only at high video speeds. MR and BF1-2 methods, popular in animal locomotion studies, performed well when estimating velocities but poorly when estimating accelerations, Finally, the high variance of the estimates for some methods should be considered when choosing an algorithm.

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Documento generato il 01/10/20 alle ore 07:39:52