Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis
PurposePlantar 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.MethodsA sample of 30 h...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1482382/full |
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| author | Xiaotian Bai Xiao Hou Dazhi Lv Dazhi Lv Jialin Wei Jialin Wei Yiling Song Zhengyan Tang Hongfeng Huo Hongfeng Huo Jingmin Liu |
| author_facet | Xiaotian Bai Xiao Hou Dazhi Lv Dazhi Lv Jialin Wei Jialin Wei Yiling Song Zhengyan Tang Hongfeng Huo Hongfeng Huo Jingmin Liu |
| author_sort | Xiaotian Bai |
| collection | DOAJ |
| description | PurposePlantar 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.MethodsA 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.ResultsThe 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-55ae00bce8bb403dbd9e408b9b8a5384 |
| institution | DOAJ |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-55ae00bce8bb403dbd9e408b9b8a53842025-08-20T02:53:49ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-01-011210.3389/fbioe.2024.14823821482382Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysisXiaotian Bai0Xiao Hou1Dazhi Lv2Dazhi Lv3Jialin Wei4Jialin Wei5Yiling Song6Zhengyan Tang7Hongfeng Huo8Hongfeng Huo9Jingmin Liu10Department of Physical Education, Tsinghua University, Beijing, ChinaSchool of Sport Science, Beijing Sport University, Beijing, ChinaCollege of Physical Education, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory of Bioinformatics Evaluation of Human Movement, Hebei Normal University, Shijiazhuang, ChinaCollege of Physical Education, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory of Bioinformatics Evaluation of Human Movement, Hebei Normal University, Shijiazhuang, ChinaDepartment of Physical Education, Tsinghua University, Beijing, ChinaDepartment of Physical Education, Tsinghua University, Beijing, ChinaCollege of Physical Education, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory of Bioinformatics Evaluation of Human Movement, Hebei Normal University, Shijiazhuang, ChinaDepartment of Physical Education, Tsinghua University, Beijing, ChinaPurposePlantar 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.MethodsA 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.ResultsThe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fbioe.2024.1482382/fullneural networkplantar soft tissuegaitplantar pressurebiomechanics |
| spellingShingle | Xiaotian Bai Xiao Hou Dazhi Lv Dazhi Lv Jialin Wei Jialin Wei Yiling Song Zhengyan Tang Hongfeng Huo Hongfeng Huo Jingmin Liu Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis Frontiers in Bioengineering and Biotechnology neural network plantar soft tissue gait plantar pressure biomechanics |
| title | Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| title_full | Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| title_fullStr | Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| title_full_unstemmed | Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| title_short | Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| title_sort | development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis |
| topic | neural network plantar soft tissue gait plantar pressure biomechanics |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1482382/full |
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