Literature DB >> 31705337

Foot shape and plantar pressure relationships in shod and barefoot populations.

Qichang Mei1,2,3, Yaodong Gu4,5,6, Liangliang Xiang1,2, Peimin Yu1,2, Zixiang Gao1,2, Vickie Shim3, Justin Fernandez2,3,7.   

Abstract

This study presents population-based multivariate regression models for predicting foot plantar pressure from easily measured foot metrics in both shod and barefoot populations for running and walking tasks. Both shod and barefoot models were trained on 50 participants and predicted plantar pressure from anthropometric measurements using a 'leave-one-out' validation with R2 values of 0.72-0.78 across walking and running in both populations. When the model was blindly tested on 16 new data sets, the model performed just as well with R2 values of 0.76-0.79 across both populations. Walking and running peak plantar pressure were predicted with similar levels of accuracy in both populations. It was revealed that forefoot plantar pressure was more sensitive to the hallux-toe distance in barefoot people with shod participants showing little response to this foot characteristic. Lateral forefoot plantar pressure was sensitive to the arch index in both shod and barefoot participants but only for walking. During running, the arch index was not a useful determinant of lateral forefoot pressure. Hence, habitually barefoot people who adopt minimalist footwear should consider additional support in the medial forefoot and walking footwear should include forefoot support stratified by arch index (foot type), but running footwear is challenging due to the variability in strike patterns.

Entities:  

Keywords:  1–2 Toe distance; Barefoot; Hallux angle; Peak pressure; Running; Shod; Walking

Mesh:

Year:  2019        PMID: 31705337     DOI: 10.1007/s10237-019-01255-w

Source DB:  PubMed          Journal:  Biomech Model Mechanobiol        ISSN: 1617-7940


  5 in total

1.  Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.

Authors:  Liangliang Xiang; Alan Wang; Yaodong Gu; Liang Zhao; Vickie Shim; Justin Fernandez
Journal:  Front Neurorobot       Date:  2022-06-02       Impact factor: 3.493

2.  Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.

Authors:  Jose Moon; Dongjun Lee; Hyunwoo Jung; Ahnryul Choi; Joung Hwan Mun
Journal:  Sensors (Basel)       Date:  2022-05-04       Impact factor: 3.847

3.  Automatic Classification of Barefoot and Shod Populations Based on the Foot Metrics and Plantar Pressure Patterns.

Authors:  Liangliang Xiang; Yaodong Gu; Qichang Mei; Alan Wang; Vickie Shim; Justin Fernandez
Journal:  Front Bioeng Biotechnol       Date:  2022-03-23

4.  Relationship between Firefighter Physical Fitness and Special Ability Performance: Predictive Research Based on Machine Learning Algorithms.

Authors:  Datao Xu; Yang Song; Yao Meng; Bíró István; Yaodong Gu
Journal:  Int J Environ Res Public Health       Date:  2020-10-21       Impact factor: 3.390

5.  Understanding Foot Loading and Balance Behavior of Children with Motor Sensory Processing Disorder.

Authors:  Lin Yu; Peimin Yu; Wei Liu; Zixiang Gao; Dong Sun; Qichang Mei; Justin Fernandez; Yaodong Gu
Journal:  Children (Basel)       Date:  2022-03-09
  5 in total

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