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BioMed Research International
Volume 2014, Article ID 214156, 7 pages
Research Article

Assessment of Waveform Similarity in Clinical Gait Data: The Linear Fit Method

1Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Fondazione Santa Lucia, Via Ardeatina 306, 00179 Rome, Italy
2Information Engineering Unit, POLCOMING Department, University of Sassari, Viale Mancini 5, 07100 Sassari, Italy
3Motion Analysis Laboratory, AUSL of Reggio Emilia, Rehabilitation Department, Via Circondaria 26, Reggio Emilia, 42015 Correggio, Italy
4Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis 6, 00194 Rome, Italy

Received 24 April 2014; Accepted 19 June 2014; Published 13 July 2014

Academic Editor: Alessia Rabini

Copyright © 2014 M. Iosa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The assessment of waveform similarity is a crucial issue in gait analysis for the comparison of kinematic or kinetic patterns with reference data. A typical scenario is in fact the comparison of a patient’s gait pattern with a relevant physiological pattern. This study aims to propose and validate a simple method for the assessment of waveform similarity in terms of shape, amplitude, and offset. The method relies on the interpretation of these three parameters, obtained through a linear fit applied to the two data sets under comparison plotted one against the other after time normalization. The validity of this linear fit method was tested in terms of appropriateness (comparing real gait data of 34 patients with cerebrovascular accident with those of 15 healthy subjects), reliability, sensitivity, and specificity (applying a cluster analysis on the real data). Results showed for this method good appropriateness, 94.1% of sensitivity, 93.3% of specificity, and good reliability. The LFM resulted in a simple method suitable for analysing the waveform similarity in clinical gait analysis.