Cyclostationary modeling of ground reaction force signals

• Published in: Signal processing Vol. 90; no. 4; pp. 1146 - 1152
• Main Authors: Sabri, Khalid, El Badaoui, Mohamed, Guillet, François, Belli, Alain, Millet, Guillaume, Benoit Morin, Jean
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-04-2010; Elsevier
• Subjects: Applied sciences; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Cyclostationarity; Disorders; Exact sciences and technology; Ground reaction force signals; Grounds; Human; Information, signal and communications theory; Locomotion; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Recognition; Runner's fatigue; Running; Second-order cyclic statistics; Signal processing; Statistics; Telecommunications and information theory; Ground reaction force signals; Runner's fatigue; Second-order cyclic statistics; Cyclostationarity; Second order; Order statistic; Non stationary condition; Non stationary process; Failures; Modeling; Statistical method; Locomotion; Gesture; Signal processing; Stationary signal; Diagnostic aid
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2009.09.027

The importance of the measurement of human locomotion for the processes of diagnosis and treatment of locomotion disorders is increasingly being recognized. Human locomotion, in particular walking and running are defined by sequences of cyclic gestures. The variability of these sequences can reveal abilities or motorskill failures. The purpose of this study is to analyze and to characterize a runner's step from the ground reaction forces (GRF) measured during a run on a treadmill. Traditionally, the analysis of GRF signals is performed by the use of signal processing methods, which assume statistically stationary signal features. The originality of this paper consists in proposing an alternative framework for analyzing GRF signals, based on cyclostationary analysis. This framework, being able to model signals with periodically varying statistics, is better at showing the development of runner's fatigue.