- Email: [email protected]
Integrating a multi-segment foot model in simulation of gait to better understand plantar pressure distribution
S66
Abstracts / Gait & Posture 39S (2014) S1–S141
Reference [1] Love SC, Novak I, Kentish M, Desloovere K, Heinen F, Molenaers G, O’Flaherty S, Graham HK. Botulinum toxin assessment, intervention and after-care for lower limb spasticity in children with cerebral palsy: international consensus statement. Eur J Neurol 2010;17(Suppl. 2):9–37. [2] Becher JG. Pediatric Rehabilitation in Children with Cerebral Palsy: General Management, Classification of Motor Disorders. JPO 2002;14:143–9.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.090 086
Session 5B: Foot
Integrating a multi-segment foot model in simulation of gait to better understand plantar pressure distribution Wouter Aerts 1,∗ , Josefien Burg 1,2 , Friedl De Groote 3 , Jos Vander Sloten 1 , Ilse Jonkers 2 1
Department of Mechanical Engineering, Biomechanics section, KU Leuven, Belgium 2 Department of Kinesiology, Human movement biomechanics, KU Leuven, Belgium 3 Department of Mechanical Engineering, PMA, KU Leuven, Belgium Introduction and aim: 3D gait analysis using motion capture has proven its usefulness and its effectiveness in examining pathological gait, and evaluating surgical and rehabilitation treatments. Gait analysis is based on a biomechanical multi-body model and the number of degrees of freedom (DOF) of this model has increased over the years in order to increase the accuracy of the kinematic description. However, for simulation purposes the 1-DOF foot model is still mostly used. The aim of this study is 1) to use a multi-segment foot model to simulate the plantar pressure during gait and 2) to compare the results with a one-segment foot model. Patients/materials and methods: The gait pattern of a subject walking barefoot is measured in a gait lab (MALL, Movement & posture Analysis Laboratory Leuven, 10 Vicon cameras, 2 AMTI-force plates, 2 RSscan pressure plates). Additional markers were placed on the foot, in order to distinguish 5 foot segments (hindfoot, midfoot, lateral & medial forefoot, and hallux). Based on the recorded marker data, gait is simulated in OpenSim [1,2], using two different musculoskeletal models, one with a 1-DOF foot model (ankle joint) and the second one with a 3-DOF foot model (ankle, subtalar, and metatarsophalangeal joint). Inverse Kinematics, Residual Reduction Algorithm, and Computed Muscle Control were consequently applied to calculate the muscle controls underlying the
measured gait motion. The calculated controls were input to a forward dynamic simulation that predicted the resulting GRF based on an elastic foundation (EF) contact model. It describes the contact forces between the foot and the ground based on the indentation of a contact geometry within the floor [3,4]. The outer surface of a foot, retrieved from CT-images, is used as contact geometry. For the 3-DOF foot this contact geometry is separated into two parts, as shown in Fig. 1. Results: Fig. 2 shows the maximal plantar pressure calculated by the forward dynamic simulation from mid-stance to swing, using two different foot models (a and b). A qualitative comparison with the measured plantar pressure (c) shows a better correspondence for the 3-DOF foot then for the 1-DOF foot. For example, the high pressure region at the lateral side of the metatarsal arch is correctly simulated by the 3-DOF foot model but not by the 1-DOF foot model. The center of pressure path of the 3-DOF foot simulation resembles nicely the measured path. Discussion and conclusions: The proposed workflow shows how the plantar pressure can be simulated using an EF contact model. Contrary to finite elements, this method is not limited to quasi-static situations and hence, gait simulation can be performed within a reasonable calculation time enabling evaluation and optimization of shoes, insoles or orthotics design, performed on a computer. It is also shown that incorporating more DOF for the
Fig. 1. 3-DOF foot with a separate contact mesh for the toes and the rest of the foot.
Fig. 2. Peak plantar pressure for a simulation with (a) a 1-DOF foot; (b) a 3-DOF foot compared with (c) the measured pressure. The black dots indicate the COP.
Abstracts / Gait & Posture 39S (2014) S1–S141
S67
foot model is needed to correctly simulate patient specific plantar pressure. Reference [1] [2] [3] [4]
Delp SL, et al. IEEE Bio Med Eng 2007;54:1940–50. Seth A, et al. Proc IUTAM 2011;2:212–32. Sherman, M.A. et al., Procedia IUTAM, 2:241–261. Aerts W, et al. Proc Comput Method Biomech 2013;11:349–50.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.091 087 The influence of foot type on pressure distribution during gait Z. Svoboda 1,∗ , M. Janura 1 , L. Kralova 1 , I. Vareka 1,2 1 Faculty of Physical Culture, Palacky University, Olomouc, Czech Republic 2 Rehabilitation Clinic, University Hospital, Hradec Králové, Czech Republic
Introduction and aim: The human foot plays a fundamental role during gait by linking the body to the ground. In literature are contained numerous studies concerning various division of foot types. The specific foot type can be determined clinically or by certain measuring devices, however usually during static conditions. It is suggested that if a classification method combines data on structure with information on foot function in dynamic loading situations, it should relate more closely to the functional behaviour of the foot during locomotion [1]. Therefore it is desirable to verify the influence of foot types to gait performance (proximal direction) and limbs loading (distal direction). The aim of this study is to compare pressure distribution during gait in various foot types. Patients/materials and methods: Twenty-three males (age 23 ± 2 years, height 183 ± 5 cm, weight 78 ± 7 kg) participated in this study. Foot type was determined clinically by an experienced rehabilitation physician to the following foot types: forefoot valgus (n = 5), forefoot varus (n = 22) and rearfoot varus (n = 19). Five successful trials of gait for each subject measured on 2 m long pressure plate Footscan (RSScan International, Olen, Belgium) were analyzed by software Footscan Gait. In each foot area (big toe, toes, 1st to 5th metatarsals, medial and lateral part of heel), relative contact duration, pressure peak and pressure impulse were evaluated. For statistical comparison (STATISTICA, Version 9.0, Stat-Soft, Inc., Tulsa, USA) between the groups, the Mann–Whitney U test was performed. Results: On medial part of heel, there was found significantly greater pressure peak in the forefoot valgus (Fig. 1) compared to the forefoot varus (p < 0.05). The pressure distribution in the metatarsals area showed greater contact duration, pressure peak
Fig. 1. Pressure in medial part of heel in various foot types. FFvalg – fore-foot valgus, FFvar – fore-foot varus, RFvar – rear-foot varus.
Fig. 2. Pressure in first metatarsal area in various foot types. FFvalg – fore-foot valgus, FFvar – fore-foot varus, RFvar – rear-foot varus.
and pressure impulse in the first metatarsal for forefoot valgus (p < 0.01, Fig. 2). Significantly lower contact duration in big toe for rear-foot varus (p < 0.01) and lower contact duration, pressure peak and pressure impulse in other toes for fore-foot valgus (p < 0.05) was found in the toes area compared to other foot types.. Discussion and conclusions: The study results showed significant differences between foot types mainly in area of first metatarsal and toes. It suggests that foot type has an impact on foot loading especially on the propulsion phase of gait. The forefoot valgus is characterized by greater loading in first metatarsal and lower loading in 2nd to 5th toes in comparison with forefoot and rearfoot varus. Reference [1] Razeghi M, Batt ME. Gait Posture 2002;15:282–91.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.092 088 Foot motion during fore- and rear-foot strike treadmill running R.C. Franzese 1,∗ , J. Leitch 2 , J. Stebbins 3 , A.B. Zavatsky 1 1 Department of Engineering Science, University of Oxford, Oxford, UK 2 Run3D Ltd., Oxford, UK 3 Oxford Gait Laboratory, Nuffield Orthopaedic Centre NHS Trust, Oxford, UK
Introduction and aim: Higher rates of injury in collegiate distance runners have been shown to be associated with a rear-foot strike (RFS) pattern as opposed to fore-foot strike (FFS) pattern [1]. This may be linked to the sharp vertical ground reaction force impact peak (first ∼50 ms of stance phase) characteristic of RFS running [2]. RFS runners contact the ground in a dorsiflexed ankle position, whilst FFS runners first contact the ground in a plantarflexed ankle position. We hypothesize that RFS and FFS runners not only have differences in foot and ankle sagittal plane angles, but also in their angular velocities. Since viscous-type damping at the joints is proportional to angular velocity, lower angular velocities may imply limited damping and a higher likelihood of injury. Patients/materials and methods: Twenty-two habitually shod competitive distance runners (8 female, 14 male (age, 23 ± 3 yrs)) were recruited from a University-level cross country club. Participants ran barefoot on a treadmill (Ultim8 Fitness Ltd., UK) at 12.9 km h−1 while twelve MX cameras (Vicon, Oxford, UK) captured lower-limb kinematic data at 200 Hz. The Oxford Foot Model [3] was used to calculate foot and leg motion. Twelve consecutive strides were time normalized and averaged for each subject to give a representative stride. FS strategy was identified visually
Abstracts / Gait & Posture 39S (2014) S1–S141
Reference [1] Love SC, Novak I, Kentish M, Desloovere K, Heinen F, Molenaers G, O’Flaherty S, Graham HK. Botulinum toxin assessment, intervention and after-care for lower limb spasticity in children with cerebral palsy: international consensus statement. Eur J Neurol 2010;17(Suppl. 2):9–37. [2] Becher JG. Pediatric Rehabilitation in Children with Cerebral Palsy: General Management, Classification of Motor Disorders. JPO 2002;14:143–9.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.090 086
Session 5B: Foot
Integrating a multi-segment foot model in simulation of gait to better understand plantar pressure distribution Wouter Aerts 1,∗ , Josefien Burg 1,2 , Friedl De Groote 3 , Jos Vander Sloten 1 , Ilse Jonkers 2 1
Department of Mechanical Engineering, Biomechanics section, KU Leuven, Belgium 2 Department of Kinesiology, Human movement biomechanics, KU Leuven, Belgium 3 Department of Mechanical Engineering, PMA, KU Leuven, Belgium Introduction and aim: 3D gait analysis using motion capture has proven its usefulness and its effectiveness in examining pathological gait, and evaluating surgical and rehabilitation treatments. Gait analysis is based on a biomechanical multi-body model and the number of degrees of freedom (DOF) of this model has increased over the years in order to increase the accuracy of the kinematic description. However, for simulation purposes the 1-DOF foot model is still mostly used. The aim of this study is 1) to use a multi-segment foot model to simulate the plantar pressure during gait and 2) to compare the results with a one-segment foot model. Patients/materials and methods: The gait pattern of a subject walking barefoot is measured in a gait lab (MALL, Movement & posture Analysis Laboratory Leuven, 10 Vicon cameras, 2 AMTI-force plates, 2 RSscan pressure plates). Additional markers were placed on the foot, in order to distinguish 5 foot segments (hindfoot, midfoot, lateral & medial forefoot, and hallux). Based on the recorded marker data, gait is simulated in OpenSim [1,2], using two different musculoskeletal models, one with a 1-DOF foot model (ankle joint) and the second one with a 3-DOF foot model (ankle, subtalar, and metatarsophalangeal joint). Inverse Kinematics, Residual Reduction Algorithm, and Computed Muscle Control were consequently applied to calculate the muscle controls underlying the
measured gait motion. The calculated controls were input to a forward dynamic simulation that predicted the resulting GRF based on an elastic foundation (EF) contact model. It describes the contact forces between the foot and the ground based on the indentation of a contact geometry within the floor [3,4]. The outer surface of a foot, retrieved from CT-images, is used as contact geometry. For the 3-DOF foot this contact geometry is separated into two parts, as shown in Fig. 1. Results: Fig. 2 shows the maximal plantar pressure calculated by the forward dynamic simulation from mid-stance to swing, using two different foot models (a and b). A qualitative comparison with the measured plantar pressure (c) shows a better correspondence for the 3-DOF foot then for the 1-DOF foot. For example, the high pressure region at the lateral side of the metatarsal arch is correctly simulated by the 3-DOF foot model but not by the 1-DOF foot model. The center of pressure path of the 3-DOF foot simulation resembles nicely the measured path. Discussion and conclusions: The proposed workflow shows how the plantar pressure can be simulated using an EF contact model. Contrary to finite elements, this method is not limited to quasi-static situations and hence, gait simulation can be performed within a reasonable calculation time enabling evaluation and optimization of shoes, insoles or orthotics design, performed on a computer. It is also shown that incorporating more DOF for the
Fig. 1. 3-DOF foot with a separate contact mesh for the toes and the rest of the foot.
Fig. 2. Peak plantar pressure for a simulation with (a) a 1-DOF foot; (b) a 3-DOF foot compared with (c) the measured pressure. The black dots indicate the COP.
Abstracts / Gait & Posture 39S (2014) S1–S141
S67
foot model is needed to correctly simulate patient specific plantar pressure. Reference [1] [2] [3] [4]
Delp SL, et al. IEEE Bio Med Eng 2007;54:1940–50. Seth A, et al. Proc IUTAM 2011;2:212–32. Sherman, M.A. et al., Procedia IUTAM, 2:241–261. Aerts W, et al. Proc Comput Method Biomech 2013;11:349–50.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.091 087 The influence of foot type on pressure distribution during gait Z. Svoboda 1,∗ , M. Janura 1 , L. Kralova 1 , I. Vareka 1,2 1 Faculty of Physical Culture, Palacky University, Olomouc, Czech Republic 2 Rehabilitation Clinic, University Hospital, Hradec Králové, Czech Republic
Introduction and aim: The human foot plays a fundamental role during gait by linking the body to the ground. In literature are contained numerous studies concerning various division of foot types. The specific foot type can be determined clinically or by certain measuring devices, however usually during static conditions. It is suggested that if a classification method combines data on structure with information on foot function in dynamic loading situations, it should relate more closely to the functional behaviour of the foot during locomotion [1]. Therefore it is desirable to verify the influence of foot types to gait performance (proximal direction) and limbs loading (distal direction). The aim of this study is to compare pressure distribution during gait in various foot types. Patients/materials and methods: Twenty-three males (age 23 ± 2 years, height 183 ± 5 cm, weight 78 ± 7 kg) participated in this study. Foot type was determined clinically by an experienced rehabilitation physician to the following foot types: forefoot valgus (n = 5), forefoot varus (n = 22) and rearfoot varus (n = 19). Five successful trials of gait for each subject measured on 2 m long pressure plate Footscan (RSScan International, Olen, Belgium) were analyzed by software Footscan Gait. In each foot area (big toe, toes, 1st to 5th metatarsals, medial and lateral part of heel), relative contact duration, pressure peak and pressure impulse were evaluated. For statistical comparison (STATISTICA, Version 9.0, Stat-Soft, Inc., Tulsa, USA) between the groups, the Mann–Whitney U test was performed. Results: On medial part of heel, there was found significantly greater pressure peak in the forefoot valgus (Fig. 1) compared to the forefoot varus (p < 0.05). The pressure distribution in the metatarsals area showed greater contact duration, pressure peak
Fig. 1. Pressure in medial part of heel in various foot types. FFvalg – fore-foot valgus, FFvar – fore-foot varus, RFvar – rear-foot varus.
Fig. 2. Pressure in first metatarsal area in various foot types. FFvalg – fore-foot valgus, FFvar – fore-foot varus, RFvar – rear-foot varus.
and pressure impulse in the first metatarsal for forefoot valgus (p < 0.01, Fig. 2). Significantly lower contact duration in big toe for rear-foot varus (p < 0.01) and lower contact duration, pressure peak and pressure impulse in other toes for fore-foot valgus (p < 0.05) was found in the toes area compared to other foot types.. Discussion and conclusions: The study results showed significant differences between foot types mainly in area of first metatarsal and toes. It suggests that foot type has an impact on foot loading especially on the propulsion phase of gait. The forefoot valgus is characterized by greater loading in first metatarsal and lower loading in 2nd to 5th toes in comparison with forefoot and rearfoot varus. Reference [1] Razeghi M, Batt ME. Gait Posture 2002;15:282–91.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.092 088 Foot motion during fore- and rear-foot strike treadmill running R.C. Franzese 1,∗ , J. Leitch 2 , J. Stebbins 3 , A.B. Zavatsky 1 1 Department of Engineering Science, University of Oxford, Oxford, UK 2 Run3D Ltd., Oxford, UK 3 Oxford Gait Laboratory, Nuffield Orthopaedic Centre NHS Trust, Oxford, UK
Introduction and aim: Higher rates of injury in collegiate distance runners have been shown to be associated with a rear-foot strike (RFS) pattern as opposed to fore-foot strike (FFS) pattern [1]. This may be linked to the sharp vertical ground reaction force impact peak (first ∼50 ms of stance phase) characteristic of RFS running [2]. RFS runners contact the ground in a dorsiflexed ankle position, whilst FFS runners first contact the ground in a plantarflexed ankle position. We hypothesize that RFS and FFS runners not only have differences in foot and ankle sagittal plane angles, but also in their angular velocities. Since viscous-type damping at the joints is proportional to angular velocity, lower angular velocities may imply limited damping and a higher likelihood of injury. Patients/materials and methods: Twenty-two habitually shod competitive distance runners (8 female, 14 male (age, 23 ± 3 yrs)) were recruited from a University-level cross country club. Participants ran barefoot on a treadmill (Ultim8 Fitness Ltd., UK) at 12.9 km h−1 while twelve MX cameras (Vicon, Oxford, UK) captured lower-limb kinematic data at 200 Hz. The Oxford Foot Model [3] was used to calculate foot and leg motion. Twelve consecutive strides were time normalized and averaged for each subject to give a representative stride. FS strategy was identified visually