Evidence map›Paper›PMID 39834637›Full record

ArticleFrontiers in bioengineering and biotechnology2024

Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis.

Xiaotian Bai, Xiao Hou, Dazhi Lv, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Jingmin Liu

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiaotian Bai *Department of Physical Education, Tsinghua University, Beijing, China.
Xiao Hou *School of Sport Science, Beijing Sport University, Beijing, China.
Dazhi LvCollege of Physical Education, Hebei Normal University, Shijiazhuang, China.
Jialin WeiCollege of Physical Education, Hebei Normal University, Shijiazhuang, China.
Yiling SongDepartment of Physical Education, Tsinghua University, Beijing, China.
Zhengyan TangDepartment of Physical Education, Tsinghua University, Beijing, China.
Hongfeng HuoCollege of Physical Education, Hebei Normal University, Shijiazhuang, China.
Jingmin LiuDepartment of Physical Education, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Plantar soft tissue properties affect foot biomechanics during movement. This study aims to explore the relationship between plantar pressure features and soft tissue stiffness through interpretable neural network model. The findings could inform orthotic insole design. Methods: A sample of 30 healthy young male subjects with normal feet were recruited (age 23.56 ± 3.28 years, height 1.76 ± 0.04 m, weight 72.21 ± 5.69 kg). Plantar pressure data were collected during 5 trials at the subjects' preferred walking speed (1.15 ± 0.04 m/s). Foot soft tissue stiffness was recorded using a MyotonPRO biological soft tissue stiffness meter before each walking trial. A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. Mean impact value analysis was conducted in parallel to investigate the relative importance of different plantar pressure features. Results: The predicted values for the training set are slightly higher than the actual values (MBE = 0.77N/m, RMSE = 11.89 N/m), with a maximum relative error of 7.82% and an average relative error of 1.98%, and the predicted values for the test set are slightly lower than the actual values (MBE = -4.43N/m, RMSE = 14.73 N/m), with a maximum relative error of 7.35% and an average relative error of 2.55%. Regions with highest contribution rates to foot soft tissue stiffness prediction were the third metatarsal (13.58%), fourth metatarsal (14.71%), midfoot (12.43%) and medial heel (12.58%) regions, which accounted for 53.3% of total contribution. Conclusion: The pressure features in the medial heel, midfoot area, and lateral mid-metatarsal regions during walking can better reflect plantar soft tissue stiffness. Future studies should ensure measurement stability of this region and refine insole designs to mitigate plantar soft tissue fatigue in the specified areas.

Indexed as

biomechanicsgaitneural networkplantar pressureplantar soft tissue

Identifiers

PMID39834637
PMCPMC11743706

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.