Sub-phase division method for gait.
<div><p>The precise recognition of human lower limb movements based on wearable sensors is very important for human-computer interaction. However, the existing methods tend to ignore the dynamic spatial information in the process of executing human lower limb movements, leading to challe...
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2025
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| _version_ | 1852015934942740480 |
|---|---|
| author | Fo Hu (22394685) |
| author2 | Qinxu Zheng (22394688) Xuanjie Ye (17746134) Zukang Qiao (22394691) Junlong Xiong (12701535) Hongsheng Chang (22394694) |
| author2_role | author author author author author |
| author_facet | Fo Hu (22394685) Qinxu Zheng (22394688) Xuanjie Ye (17746134) Zukang Qiao (22394691) Junlong Xiong (12701535) Hongsheng Chang (22394694) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fo Hu (22394685) Qinxu Zheng (22394688) Xuanjie Ye (17746134) Zukang Qiao (22394691) Junlong Xiong (12701535) Hongsheng Chang (22394694) |
| dc.date.none.fl_str_mv | 2025-10-08T17:39:27Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332947.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Sub-phase_division_method_for_gait_/30309528 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified reduced decoding accuracy predicted gait movements mine meaningful spatial inertial measurement unit highly discriminative power dynamic spatial information different skeleton nodes body partitioning strategy based skeleton graph temporal feature representations existing methods tend scale convolutional sub temporal convolutional module temporal features mainstream methods xlink "> results demonstrate related deep precise recognition limited robustness fused spatio extensive comparison computer interaction classification module attention sub ablation studies |
| dc.title.none.fl_str_mv | Sub-phase division method for gait. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The precise recognition of human lower limb movements based on wearable sensors is very important for human-computer interaction. However, the existing methods tend to ignore the dynamic spatial information in the process of executing human lower limb movements, leading to challenges such as reduced decoding accuracy and limited robustness. In this paper, we construct skeleton graph data based on inertial measurement unit (IMU) sensors. Also, a two-branch deep learning model, termed TCNN-MGCHN, is proposed to mine meaningful spatial and temporal feature representations from IMU-based skeleton graph data. Firstly, a temporal convolutional module (consisting of a multi-scale convolutional sub-module and an attention sub-module) is developed to extract temporal feature information with highly discriminative power. Secondly, a multi-scale graph convolutional module and a spatial graph edges’ importance weight assignment method based on body partitioning strategy are proposed to obtain intrinsic spatial feature information between different skeleton nodes. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted gait movements and sub-phases. Extensive comparison and ablation studies are conducted on our self-constructed human lower limb movement dataset. The results demonstrate that TCNN-MGCHN delivers superior classification performance compared to the mainstream methods. This study can provide a benchmark for IMU-based human lower limb movement recognition and related deep-learning modeling works.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_7cd8aa5aa5840bb2ab91be2db1d6cbdb |
| identifier_str_mv | 10.1371/journal.pone.0332947.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30309528 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sub-phase division method for gait.Fo Hu (22394685)Qinxu Zheng (22394688)Xuanjie Ye (17746134)Zukang Qiao (22394691)Junlong Xiong (12701535)Hongsheng Chang (22394694)SociologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedreduced decoding accuracypredicted gait movementsmine meaningful spatialinertial measurement unithighly discriminative powerdynamic spatial informationdifferent skeleton nodesbody partitioning strategybased skeleton graphtemporal feature representationsexisting methods tendscale convolutional subtemporal convolutional moduletemporal featuresmainstream methodsxlink ">results demonstraterelated deepprecise recognitionlimited robustnessfused spatioextensive comparisoncomputer interactionclassification moduleattention subablation studies<div><p>The precise recognition of human lower limb movements based on wearable sensors is very important for human-computer interaction. However, the existing methods tend to ignore the dynamic spatial information in the process of executing human lower limb movements, leading to challenges such as reduced decoding accuracy and limited robustness. In this paper, we construct skeleton graph data based on inertial measurement unit (IMU) sensors. Also, a two-branch deep learning model, termed TCNN-MGCHN, is proposed to mine meaningful spatial and temporal feature representations from IMU-based skeleton graph data. Firstly, a temporal convolutional module (consisting of a multi-scale convolutional sub-module and an attention sub-module) is developed to extract temporal feature information with highly discriminative power. Secondly, a multi-scale graph convolutional module and a spatial graph edges’ importance weight assignment method based on body partitioning strategy are proposed to obtain intrinsic spatial feature information between different skeleton nodes. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted gait movements and sub-phases. Extensive comparison and ablation studies are conducted on our self-constructed human lower limb movement dataset. The results demonstrate that TCNN-MGCHN delivers superior classification performance compared to the mainstream methods. This study can provide a benchmark for IMU-based human lower limb movement recognition and related deep-learning modeling works.</p></div>2025-10-08T17:39:27ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332947.g003https://figshare.com/articles/figure/Sub-phase_division_method_for_gait_/30309528CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303095282025-10-08T17:39:27Z |
| spellingShingle | Sub-phase division method for gait. Fo Hu (22394685) Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified reduced decoding accuracy predicted gait movements mine meaningful spatial inertial measurement unit highly discriminative power dynamic spatial information different skeleton nodes body partitioning strategy based skeleton graph temporal feature representations existing methods tend scale convolutional sub temporal convolutional module temporal features mainstream methods xlink "> results demonstrate related deep precise recognition limited robustness fused spatio extensive comparison computer interaction classification module attention sub ablation studies |
| status_str | publishedVersion |
| title | Sub-phase division method for gait. |
| title_full | Sub-phase division method for gait. |
| title_fullStr | Sub-phase division method for gait. |
| title_full_unstemmed | Sub-phase division method for gait. |
| title_short | Sub-phase division method for gait. |
| title_sort | Sub-phase division method for gait. |
| topic | Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified reduced decoding accuracy predicted gait movements mine meaningful spatial inertial measurement unit highly discriminative power dynamic spatial information different skeleton nodes body partitioning strategy based skeleton graph temporal feature representations existing methods tend scale convolutional sub temporal convolutional module temporal features mainstream methods xlink "> results demonstrate related deep precise recognition limited robustness fused spatio extensive comparison computer interaction classification module attention sub ablation studies |