title: Estimation of muscle forces in gait using a simulation of the electromyographic activity and numerical optimization.
abstract: Clinical gait analysis provides great contributions to the understanding of gait patterns. However, a complete distribution of muscle forces throughout the gait cycle is a current challenge for many researchers. Two techniques are often used to estimate muscle forces: inverse dynamics with static optimization and computer muscle control that uses forward dynamics to minimize tracking. The first method often involves limitations due to changing muscle dynamics and possible signal artefacts that depend on day-to-day variation in the position of electromyographic (EMG) electrodes. Nevertheless, in clinical gait analysis, the method of inverse dynamics is a fundamental and commonly used computational procedure to calculate the force and torque reactions at various body joints. Our aim was to develop a generic musculoskeletal model that could be able to be applied in the clinical setting. The musculoskeletal model of the lower limb presents a simulation for the EMG data to address the common limitations of these techniques. This model presents a new point of view from the inverse dynamics used on clinical gait analysis, including the EMG information, and shows a similar performance to another model available in the OpenSim software. The main problem of these methods to achieve a correct muscle coordination is the lack of complete EMG data for all muscles modelled. We present a technique that simulates the EMG activity and presents a good correlation with the muscle forces throughout the gait cycle. Also, this method showed great similarities whit the real EMG data recorded from the subjects doing the same movement.
authors.affiliation: a Laboratory of Biomechanics, School of Engineering, National University of Entre Ríos , Provincial Route 11 Km. 10, Oro Verde 3100 , Argentina.|b National Council of Scientific and Technical Research , Buenos Aires , Argentina.
authors.forename: Emiliano Pablo
authors.initials: EP
authors.lastname: Ravera
authors.affiliation: c Gait and Motion Analysis Laboratory, FLENI Institute for Neurological Research , National Route 9 Km. 53, Escobar, Buenos Aires B1625XAF , Argentina.
authors.forename: Marcos José
authors.initials: MJ
authors.lastname: Crespo
authors.affiliation: a Laboratory of Biomechanics, School of Engineering, National University of Entre Ríos , Provincial Route 11 Km. 10, Oro Verde 3100 , Argentina.
authors.forename: Ariel Andrés Antonio
authors.initials: AA
authors.lastname: Braidot
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D001835:Body Weight; D002648:Child; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D005684:Gait / Q000502:physiology*; D006801:Humans; D007596:Joints / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D009716:Numerical Analysis, Computer-Assisted; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
keywords: clinical decision-making; electromyography; gait analysis; muscle forces; musculoskeletal model