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Peiffer M, Duquesne K, Delanghe M, Van Oevelen A, De Mits S, Audenaert E, Burssens A. Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults. Front Bioeng Biotechnol 2024; 12:1348977. [PMID: 38515625 PMCID: PMC10956131 DOI: 10.3389/fbioe.2024.1348977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 02/19/2024] [Indexed: 03/23/2024] Open
Abstract
Background: Given the inherent variability in walking speeds encountered in day-to-day activities, understanding the corresponding alterations in ankle biomechanics would provide valuable clinical insights. Therefore, the objective of this study was to examine the influence of different walking speeds on biomechanical parameters, utilizing gait analysis and musculoskeletal modelling. Methods: Twenty healthy volunteers without any lower limb medical history were included in this study. Treadmill-assisted gait-analysis with walking speeds of 0.8 m/s and 1.1 m/s was performed using the Gait Real-time Analysis Interactive Lab (GRAIL®). Collected kinematic data and ground reaction forces were processed via the AnyBody® modeling system to determine ankle kinetics and muscle forces of the lower leg. Data were statistically analyzed using statistical parametric mapping to reveal both spatiotemporal and magnitude significant differences. Results: Significant differences were found for both magnitude and spatiotemporal curves between 0.8 m/s and 1.1 m/s for the ankle flexion (p < 0.001), subtalar force (p < 0.001), ankle joint reaction force and muscles forces of the M. gastrocnemius, M. soleus and M. peroneus longus (α = 0.05). No significant spatiotemporal differences were found between 0.8 m/s and 1.1 m/s for the M. tibialis anterior and posterior. Discussion: A significant impact on ankle joint kinematics and kinetics was observed when comparing walking speeds of 0.8 m/s and 1.1 m/s. The findings of this study underscore the influence of walking speed on the biomechanics of the ankle. Such insights may provide a biomechanical rationale for several therapeutic and preventative strategies for ankle conditions.
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Affiliation(s)
- M. Peiffer
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Foot & Ankle Research and Innovation Lab (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - K. Duquesne
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - M. Delanghe
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - A. Van Oevelen
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - S. De Mits
- Department of Rheumatology, Ghent University Hospital, Ghent, Belgium
- Smart Space, Ghent University Hospital, Ghent, Belgium
| | - E. Audenaert
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Department of Trauma and Orthopaedics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Electromechanics, Op3Mech Research Group, University of Antwerp, Antwerp, Belgium
| | - A. Burssens
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
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Van Oevelen A, Duquesne K, Peiffer M, Grammens J, Burssens A, Chevalier A, Steenackers G, Victor J, Audenaert E. Personalized statistical modeling of soft tissue structures in the knee. Front Bioeng Biotechnol 2023; 11:1055860. [PMID: 36970632 PMCID: PMC10031007 DOI: 10.3389/fbioe.2023.1055860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 02/21/2023] [Indexed: 03/11/2023] Open
Abstract
Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented.Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment.Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14–0.48), 0.35 mm (range 0.16–0.53), 0.39 mm (range 0.15–0.80) and 0.75 mm (range 0.16–1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99–1.59), 0.91 mm (0.75–1.33), 2.93 mm (range 1.85–4.66) and 2.04 mm (1.88–3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus.Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.
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Affiliation(s)
- A. Van Oevelen
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- InViLab research group, Department of Electromechanics, University of Antwerp, Antwerp, Belgium
| | - K. Duquesne
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - M. Peiffer
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - J. Grammens
- Antwerp Surgical Training, Anatomy and Research Centre (ASTARC), University of Antwerp, Wilrijk, Belgium
- Imec-VisionLab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - A. Burssens
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - A. Chevalier
- Cosys-Lab research group, Department of Electromechanics, University of Antwerp, Antwerp, Belgium
| | - G. Steenackers
- InViLab research group, Department of Electromechanics, University of Antwerp, Antwerp, Belgium
| | - J. Victor
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - E. Audenaert
- Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- InViLab research group, Department of Electromechanics, University of Antwerp, Antwerp, Belgium
- Department of Trauma and Orthopedics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- *Correspondence: E. Audenaert,
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Peiffer M, Burssens A, Duquesne K, Last M, De Mits S, Victor J, Audenaert EA. Personalised statistical modelling of soft tissue structures in the ankle. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106701. [PMID: 35259673 DOI: 10.1016/j.cmpb.2022.106701] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 01/20/2022] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Revealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised 'in silico' biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. METHODS A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an 'in-house' generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. RESULTS Mean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10-17, 1.13 .10-17]) and talus (p = 0.95, CI[ -1.35 .10-17, 1.28 .10-17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area. CONCLUSIONS In this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.
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Affiliation(s)
- M Peiffer
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent 9000, Belgium.
| | - A Burssens
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent 9000, Belgium
| | - K Duquesne
- Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent 9000, Belgium
| | - M Last
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium
| | - S De Mits
- Department of Reumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Podiatry, Artevelde University of Applied Sciences, Voetweg 66, Ghent 9000, Belgium
| | - J Victor
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent 9000, Belgium
| | - E A Audenaert
- Department of Orthopaedics and Traumatology, Ghent University Hospital, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent 9000, Belgium; Department of Trauma and Orthopedics, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK; Department of Electromechanics, Op3Mech research group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
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De Roeck J, Duquesne K, Van Houcke J, Audenaert EA. Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping-Are Bone Geometry Predictors Helpful? Front Bioeng Biotechnol 2021; 9:696360. [PMID: 34322479 PMCID: PMC8312572 DOI: 10.3389/fbioe.2021.696360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics. Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated. Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°. Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.
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Affiliation(s)
- Joris De Roeck
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Kate Duquesne
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Jan Van Houcke
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium.,Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium
| | - Emmanuel A Audenaert
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium.,Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, Belgium.,Department of Trauma and Orthopedics, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Department of Electromechanics, Op3Mech Research Group, University of Antwerp, Antwerp, Belgium
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Tateuchi H, Yamagata M, Asayama A, Ichihashi N. Influence of simulated hip muscle weakness on hip joint forces during deep squatting. J Sports Sci 2021; 39:2289-2297. [PMID: 34006185 DOI: 10.1080/02640414.2021.1929009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
This study aimed to determine the effects of simulated hip muscle weakness on changes in hip joint forces during deep squat motion. Ten healthy individuals performed squat motion at three different positions (0° foot angle [N-squat], 10° toe-in [IN-squat], and 30° toe-out [OUT-squat]). A scaled musculoskeletal model for each participant was used to calculate the muscle and hip joint forces. For each hip muscle, models of full strength, mild muscle weakness (15% decrease), and severe muscle weakness (30% decrease) were created. The muscles affecting the hip joint forces were identified, and the rate of change in the joint forces was compared among the three squat conditions. The anterior hip joint force was increased in the muscle weakness models of the inferior gluteus maximus (iGlutMax) and iGlutMax+deep external rotator (ExtRot) muscles. With 30% muscle weakness of these muscles, statistically significant differences in the rate of increase in the anterior joint force were observed in the following order: IN-squat (iGlutMax, 29.5%; iGlutMax+ExtRot, 41.4%), N-squat (iGlutMax, 18.3%; iGlutMax+ExtRot, 27.8%), and OUT-squat (iGlutMax, 5.6%; iGlutMax+ExtRot, 9.3%). OUT-squat may be recommended to minimize the increase in hip joint forces if accompanied by hip muscle weakness.
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Affiliation(s)
- Hiroshige Tateuchi
- Department of Physical Therapy, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Momoko Yamagata
- Department of Physical Therapy, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Graduate School of Human Development and Environment, Kobe University, Hyogo, Japan.,Japan Society for the Promotion of Science, Japan
| | - Akihiro Asayama
- Department of Physical Therapy, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Noriaki Ichihashi
- Department of Physical Therapy, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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