title: An improved technique for the extraction of stochastic parameters from stabilograms.
abstract: An improved characterization of the dynamics of postural sway can provide a better understanding about the functional organization of the postural control system as well as a more robust tool for postural pattern recognition. To this aim, a novel parameterization was applied to the stabilogram diffusion analysis formerly proposed by Collins and De Luca [Collins JJ, De Luca CJ. Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories. Exp Brain Res 1993;95:308-18] that considered the act of maintaining posture as a stochastic process. The main purpose of the present technique was to overcome some drawbacks of the model presented by Collins and De Luca that may restrain its potential application in clinical practice. The approach uses a unique non-linear model to describe the center of pressure (COP) dynamics that reduces the number of parameters and decreases their intra-subject variability; consequently, fewer trials are required to perform reliable estimates of stochastic parameters and this is of particular importance for subjects that cannot afford many repeated measurements because of age or pathology. Four new statistical mechanics parameters (NSMP) were computed on the log-log stabilogram diffusion plots and their estimates were compared in terms of reliability and sensitivity to the visual conditions with: (1) a minimal set of four summary statistic scores (SSS); and (2) the six statistical mechanics parameters (SMP) proposed by Collins and De Luca. All four NSMP showed at least a fair-to-good reliability (intraclass correlation coefficient, ICC>0.49) while SMP (ICC>0.20) showed some poor reliability. A better overall reliability was also observed with respect to SSS. Moreover, only NSMP had a similar score for eyes open and eyes closed conditions. Three out of four NSMP were also significantly sensitive to eyes open or closed conditions (P<0.001) while only three out of six SMP were sensitive to operating conditions (P<0.01).
authors.affiliation: Biomedical Engineering Unit, Department of Electronics, Computer Science and Systems, University of Bologna, Viale Risorgimento 2, I-40136, Bologna, Italy. lchiari@deis.unibo.it
authors.forename: L
authors.initials: L
authors.lastname: Chiari
authors.forename: A
authors.initials: A
authors.lastname: Cappello
authors.forename: D
authors.initials: D
authors.lastname: Lenzi
authors.forename: U
authors.initials: U
authors.lastname: Della Croce
mesh_terms: D006801:Humans; D008954:Models, Biological; D009142:Musculoskeletal Physiological Phenomena; D004856:Postural Balance / Q000502:physiology; D011187:Posture / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013269:Stochastic Processes; D016138:Walking / Q000502:physiology*