Joint moments and contact forces in the foot during walking
Introduction
Forces and moments in human joints are important factors in the initiation and progression of joint disease (Andriacchi et al., 2004, Astephen et al., 2008). Abnormal ankle net moments have been reported in patients with ankle instability (Delahunt et al., 2006), neuropathy (DiLiberto et al., 2015), and stroke (Lamontagne et al., 2002). However, the net moment, or external moment, is an oversimplified expression that reduces all connective tissues, such as joint muscles and ligaments, to a single component (Vaughan, 1996). The net moments can be decomposed into active moments due to muscle contraction across the joints and passive moments due to the viscoelasticity of the muscles, ligaments and soft tissues surrounding the joint (Siegler et al., 1984).
For a specific example, ankle instability is composed of mechanical and functional components (Hirai et al., 2009, Hubbard et al., 2007). Mechanical instability refers to the abnormal passive moments of ligaments and other soft tissues, while functional instability is caused by abnormal active moments of peroneal tendons, delayed peroneal reaction time, etc. (Bonnel et al., 2010). Mechanical instability can be treated by surgical tightening of the ankle ligaments, while functional instability can be treated by more conservative methods, including muscle strengthening and proprioceptive exercises. Therefore, categorizing problems according to passive and active moments of the ankle is very critical in deciding treatment strategies for ankle instability.
Calculating the moments in the foot joints in addition to the ankle joint requires multi-segment models. The three-segment foot model has been widely used to understand foot kinematics (Carson et al., 2001, Dixon et al., 2012, van Hoeve et al., 2015, McCahill et al., 2017) and kinetics (Saraswat et al., 2010), but this model has only one joint in the midfoot and is unable to calculate the mechanics of Chopart’s and Lisfranc joints separately, which is important to understanding foot pathology such as flatfoot (Toullec, 2015). The five-segment foot model in which the midfoot joint is divided into Chopart’s and Lisfranc joints has been introduced recently (Malaquias et al., 2017, Scarton et al., 2018).
Advances in computational dynamics have made it possible to estimate forces in the internal structures of the body, such as muscle and ligament forces (Damsgaard et al., 2006, Delp et al., 1990). Recently, a detailed musculoskeletal foot model with muscles and ligaments was developed to estimate muscle and ligament forces through dynamic optimization (Oosterwaal et al., 2011). Detailed musculoskeletal models require a complex experimental protocol to attach a large number of skin markers and obtain bone morphology with CT scans of the foot. Moreover, systems with high degrees of freedom can degrade the ability to find normal patterns and improve the accuracy of the simulation (Stebbins et al., 2006).
The objective of this study was to develop a five-segment musculoskeletal foot model that includes the ankle, Chopart’s, Lisfranc and metatarsophalangeal joints, and quantify the contribution of passive and active moments on the net joint moment during walking. Joint reaction forces in the foot joints were also calculated and compared with previous studies (Al-Munajjed et al., 2016, Giddings et al., 2000, Prinold et al., 2016, Stauffer et al., 1977, Valente et al., 2014) to understand the validity of the kinetics calculations.
Section snippets
Five-segment foot model
A musculoskeletal multi-segment foot model with attached muscles and ligaments, which is called the Glasgow-Maastricht foot model, is available in the AnyBody Managed Model Repository (AMMR, version 1.6) (Oosterwaal et al., 2011). We modified the Glasgow-Maastricht foot model by reducing the number of segments from 26 to five, leaving only the important degrees of freedom of the joints in order to understand the foot pathology, referring to a previous study on foot kinesiology (Hicks, 1953).
Results
The subjects walked at an average (standard deviation) speed of 1.2 ± 0.1 m/s. Duration of the stance phase was 643 ± 41 ms in average (standard deviation). Joint reaction forces normalized by the body weight (BW) of each subject and net moments normalized by the body weight and height (HT) of each subject in the four foot joints were calculated during the stance of walking. Active and passive moments along with net moments in the four foot joints were calculated separately during the stance of
Discussion
Joint reaction forces at four joints in the foot were estimated using a musculoskeletal simulation. The temporal pattern of joint reaction forces at the ankle joint was similar to that in previous studies (Giddings et al., 2000, Prinold et al., 2016, Stauffer et al., 1977, Valente et al., 2014), but the magnitude was larger in our study. The number of foot muscles in our model is larger than any previous studies (Giddings et al., 2000, Prinold et al., 2016, Stauffer et al., 1977, Valente et
Acknowledgements
This work was supported by the Basic Science Research Program through the National Research Foundation (NRF-2017R1A2B2010763) and Projects for Research and Development of Police Science and Technology through Center for Research and Development of Police science and Technology and Korean National Police Agency (PA-C000001) funded by the Ministry of Science, ICT and Future Planning of the Republic of Korea. It was also supported by the International Joint Technology Development Program through
Conflict of interest statement
As far as the authors know, there are no conflicts of interest related to this manuscript. This paper has neither been published nor submitted elsewhere for publication, in whole or in part, either in a serial, professional journal, or as part of a book that has been formally published and made available to the public.
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