Fatigue study of ultra-runners: Presentation of a new approach for the separation of GRF signals components

• Published in: Mechanical systems and signal processing Vol. 75; pp. 648 - 667
• Main Authors: Zakaria, F.A., El Badaoui, M., Lamraoui, M., Khalil, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.06.2016
• Subjects: Algorithms; Biomechanics; Components separation; Cyclostationarity (CS); Evolution; Fatigue; Fatigue (materials); Human; Human locomotion; Muscles; Passive peak; Running; Separation; Ultra-runners; Human locomotion; Ultra-runners; Fatigue; Components separation; Passive peak; Cyclostationarity (CS)
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2015.12.005

In recent years, ultra-marathon running has become increasingly popular in many countries around the world. The ability to run for long hours has played a role in human evolution. It is known that the etiology of fatigue depends upon the exercise under consideration. In order to characterize and find a full description of the fatigue and its effects on the human locomotion mechanics, and to extract the relevant parameters and information for diagnosis, we have investigated the changes in running mechanics. More specifically, the ground reaction force (GRF) manifestations of fatigue, have been investigated by using advanced signal processing tools. GRF signals are composed of two parts: an active peak representing the propulsive force and a passive peak that represents the impact force. The impact force is the major factor indicating the reaction of muscle, that may reflects the fatigue state and performance of the muscle. In this article, we focused on the treatment of biomechanical signals for the purpose of GRF components separation where the aim is to separate the contribution of the active components and the passive components. For this reason, we proposed a new algorithm, based on the Gaussian decomposition and non-linear least squares method that will achieve the desired goal. We then compare the results of separation with a proposed BSS based method i.e. “FastICA algorithm”. We also compare the results with that obtained by Sabri et al. [1–3] who used different BSS techniques which gave bad and fair results. The separated passive signal is then proved to contain a mixture of a deterministic phenomenon and a stationary random phenomenon, where both phenomena are separated using the cepstral editing procedure (CEP) method. CEP is applied after signal synchronization using method with maximization of the inter-correlation function. The random part is then proved to be cyclostationary of order 2. A real application examined the biomechanical changes occurring in the GRF signals of ten experienced ultra- runners during 24h of continuous running. The aim was to characterize and better understand the mechanical phenomena behind the GRF signals׳ behavior and also to analyze and characterize the runner׳s step in order to quantify the degree of fatigue. This could allow a better characterization and a full innovative description of the different fatigue states of a runner. Moreover, we introduce some parameters which were measured in these subjects during the 24h of running, such as the cyclic autocorrelation function, the cyclic frequency and the energy of the integrated autocorrelation function at alpha equal to zero and at the first cyclic frequency α1. The results quantify the changes induced by runners over time, where after an extreme ultra-long duration of running, could lead to significant insights into the evolution of fatigue. •A new method is proposed to separate the impact and propulsive force of GRF signals.•Non-linear LSM is used for coefficients optimization.•Synchronization method with maximization of the inter-correlation function is used to remove low speed fluctuations.•CEP method is used to remove the deterministic components of passive signal. The residual signal is proved to be cyclostationary of order 2.•A series of proposed parameters proved their efficiency in characterizing the GRF signals of ultra-runners and could be used to define the evolution of fatigue.