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Wei Z, Xu H, Zhong W, Wang L. Comparison of shank, rearfoot and forefoot coordination and its variability between runners with different strike patterns. J Biomech 2025; 180:112494. [PMID: 39756100 DOI: 10.1016/j.jbiomech.2025.112494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 12/19/2024] [Accepted: 01/02/2025] [Indexed: 01/07/2025]
Abstract
This study aims to compare shank, rearfoot and forefoot coordination and its variability between runners with habitual rearfoot strike (RFS) and non-RFS (NRFS). 58 healthy males participated in this study (32 RFS, 26 NRFS). Coordination patterns and variability were assessed for the shank, rearfoot, and forefoot segments using a modified vector coding technique during running. RFS runners exhibited significantly greater proportion of anti-phase with distal dominancy (rearfoot) during early and mid-stance, as well as a lower proportion of anti-phase with proximal dominancy (shank) during early stance compared to NRFS runners in frontal rearfoot - transverse shank (FRTS). Conversely, NRFS runners demonstrated significantly greater proportion of anti-phase with distal dominance (forefoot) in the sagittal, frontal, and transverse planes during early stance compared to RFS runners. Coordination variabilities for the FRTS (late stance), frontal rearfoot - frontal forefoot (FRFF) (early and late stance), and frontal rearfoot - transverse forefoot (FRTF) (mid stance) were greater in NRFS than in RFS runners. In contrast, coordination variability for frontal rearfoot - sagittal forefoot (FRSF) (early stance) was greater in RFS than in NRFS runners. The results could further extend the relationship between foot strike pattern and injuries from the perspective of coordination and its variability. Preliminary findings suggest that NRFS runners could benefit from intrinsic foot muscle training to mitigate the sustained loads on the soft tissues of the foot.
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Affiliation(s)
- Zhen Wei
- The Second Clinical Medical School, Xuzhou Medical University, Tongshan Rd. 209, Xuzhou 221004, Jiangsu Province, China.
| | - Hang Xu
- School of Medical Imaging, Xuzhou Medical University, Tongshan Rd. 209, Xuzhou 221004, Jiangsu Province, China.
| | - Weiquan Zhong
- The Second Clinical Medical School, Xuzhou Medical University, Tongshan Rd. 209, Xuzhou 221004, Jiangsu Province, China.
| | - Lin Wang
- School of Exercise and Health, Shanghai University of Sport, Hengren Rd. 200, Yangpu District, Shanghai 200438, China.
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Veerkamp K, van der Krogt MM, Waterval NFJ, Geijtenbeek T, Walsh HPJ, Harlaar J, Buizer AI, Lloyd DG, Carty CP. Predictive simulations identify potential neuromuscular contributors to idiopathic toe walking. Clin Biomech (Bristol, Avon) 2024; 111:106152. [PMID: 38091916 DOI: 10.1016/j.clinbiomech.2023.106152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Most cases of toe walking in children are idiopathic. We used pathology-specific neuromusculoskeletal predictive simulations to identify potential underlying neural and muscular mechanisms contributing to idiopathic toe walking. METHODS A musculotendon contracture was added to the ankle plantarflexors of a generic musculoskeletal model to represent a pathology-specific contracture model, matching the reduced ankle dorsiflexion range-of-motion in a cohort of children with idiopathic toe walking. This model was employed in a forward dynamic simulation controlled by reflexes and supraspinal drive, governed by a multi-objective cost function to predict gait patterns with the contracture model. We validated the predicted gait using experimental gait data from children with idiopathic toe walking with ankle contracture, by calculating the root mean square errors averaged over all biomechanical variables. FINDINGS A predictive simulation with the pathology-specific model with contracture approached experimental ITW data (root mean square error = 1.37SD). Gastrocnemius activation was doubled from typical gait simulations, but lacked a peak in early stance as present in electromyography. This synthesised idiopathic toe walking was more costly for all cost function criteria than typical gait simulation. Also, it employed a different neural control strategy, with increased length- and velocity-based reflex gains to the plantarflexors in early stance and swing than typical gait simulations. INTERPRETATION The simulations provide insights into how a musculotendon contracture combined with altered neural control could contribute to idiopathic toe walking. Insights into these neuromuscular mechanisms could guide future computational and experimental studies to gain improved insight into the cause of idiopathic toe walking.
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Affiliation(s)
- Kirsten Veerkamp
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia; Griffith Centre of Biomedical & Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University Gold Coast, Australia.
| | - Marjolein M van der Krogt
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands
| | - Niels F J Waterval
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands; Amsterdam UMC, Univ of Amsterdam, Rehabilitation Medicine, Amsterdam Movement Sciences, Meibergdreef 9, Amsterdam, the Netherlands
| | - Thomas Geijtenbeek
- Department of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands
| | - H P John Walsh
- Griffith Centre of Biomedical & Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University Gold Coast, Australia; Department of Orthopaedics, Children's Health Queensland Hospital and Health Service, Queensland Children's Hospital, Brisbane, Australia
| | - Jaap Harlaar
- Department of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands; Department of Orthopedics & Sports Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Annemieke I Buizer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, the Netherlands; Emma Children's Hospital Amsterdam UMC, Amsterdam, the Netherlands
| | - David G Lloyd
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia; Griffith Centre of Biomedical & Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University Gold Coast, Australia
| | - Christopher P Carty
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia; Griffith Centre of Biomedical & Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University Gold Coast, Australia; Department of Orthopaedics, Children's Health Queensland Hospital and Health Service, Queensland Children's Hospital, Brisbane, Australia
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Joo H, Kim H, Ryu JK, Ryu S, Lee KM, Kim SC. Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031279. [PMID: 35162308 PMCID: PMC8835219 DOI: 10.3390/ijerph19031279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 12/14/2022]
Abstract
People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand movements by assuming that striking patterns consequently affect temporal movements of the whole body. The advantage of the proposed approach is that FS patterns can be estimated in a portable and less invasive manner. To this end, first, we developed a wearable system for measuring inertial movements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multivariate time series signals in supervised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.
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Affiliation(s)
- Hyeyeoun Joo
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea; (H.J.); (K.-M.L.)
| | - Hyejoo Kim
- Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea;
| | - Jeh-Kwang Ryu
- Department of Physical Education, College of Education, Dongguk University, Seoul 04620, Korea;
| | - Semin Ryu
- Intelligent Robotics Laboratory, School of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Korea;
| | - Kyoung-Min Lee
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea; (H.J.); (K.-M.L.)
| | - Seung-Chan Kim
- Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea;
- Correspondence: ; Tel.: +82-31-299-6918
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