Segmentation and Classification of Gait Cycles

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 22; no. 5; pp. 946 - 952
• Main Authors: Agostini, Valentina, Balestra, Gabriella, Knaflitz, Marco
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithm design and analysis; Algorithms; Atypical gait cycles; Biomechanical Phenomena; classification; Classification algorithms; Female; Foot; Foot - physiology; foot-floor contact; foot-switches; Gait - physiology; gait analysis; Gait Disorders, Neurologic - classification; Gait Disorders, Neurologic - physiopathology; gait event detection; gait phases; Humans; Male; Manuals; Parkinson's disease; Pathology; Pediatrics; Reproducibility of Results; Signal Processing, Computer-Assisted; signal segmentation; stride-to-stride variability
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2013.2291907

Gait abnormalities can be studied by means of instrumented gait analysis. Foot-switches are useful to study the foot-floor contact and for timing the gait phases in many gait disorders, provided that a reliable foot-switch signal may be collected. Considering long walks allows reducing the intra-subject variability, but requires automatic and user-independent methods to analyze a large number of gait cycles. The aim of this work is to describe and validate an algorithm for the segmentation of the foot-switch signal and the classification of the gait cycles. The performance of the algorithm was assessed comparing its results against the manual segmentation and classification performed by a gait analysis expert on the same signal. The performance was found to be equal to 100% for healthy subjects and over 98% for pathological subjects. The algorithm allows determining the atypical cycles (cycles that do not match the standard sequence of gait phases) for many different kinds of pathological gait, since it is not based on pathology-specific templates.