Gait phase recognition of lower limb exoskeleton system based on the integrated network model
[Display omitted] •A new gait recognition method based on integrated network model is proposed for identifying four phases in the gait cycle, including heel strike, foot flat, heel off and swing phase.•This paper adopts sparse autoencoder into the model to extract key features of gait information an...
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| Vydáno v: | Biomedical signal processing and control Ročník 76; s. 103693 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2022
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| Témata: | |
| ISSN: | 1746-8094, 1746-8108 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
•A new gait recognition method based on integrated network model is proposed for identifying four phases in the gait cycle, including heel strike, foot flat, heel off and swing phase.•This paper adopts sparse autoencoder into the model to extract key features of gait information and use bidirectional long short-term memory for learning more critical information from gait data.•The proposed integrated network model has more effective recognition results and better generalization performance for gait phase recognition of the lower limb exoskeleton system compared with other models.
Exoskeleton robots have become an emerging technology in medical, industrial and military applications. Human gait phase recognition is the crucial technology for recognizing movement intention of the exoskeleton wearer and controlling the exoskeleton robot. As a new biometric recognition method, gait phase recognition also plays an important role in clinical disease diagnosis, rehabilitation training and other fields. This paper proposes an integrated network model SBLSTM that combines sparse autoencoder (SAE), bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) aiming at gait phase recognition during human movement. The model can accurately identify four phases in the gait cycle, including heel strike (HS), foot flat (FF), heel off (HO) and swing phase (SW). Normalization and feature extraction of collected sensor signals are performed to enhance the accuracy of recognition during the gait identification process. The processed data are input into the SBLSTM model. The introduction of SAE into the model can extract key information from gait characteristics. BiLSTM is used to learn temporal patterns and periodic changes in gait data. DNN is adopted to identify gait phases and output classification results. Different algorithms such as DNN, LSTM and SBLSTM are applied to the gait phase detection of subjects. The experimental results show that the SBLSTM algorithm is effective in gait recognition. The accuracy and F-score are outperformed by other algorithms, which verifies the effectiveness of the SBLSTM in practice. |
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| AbstractList | [Display omitted]
•A new gait recognition method based on integrated network model is proposed for identifying four phases in the gait cycle, including heel strike, foot flat, heel off and swing phase.•This paper adopts sparse autoencoder into the model to extract key features of gait information and use bidirectional long short-term memory for learning more critical information from gait data.•The proposed integrated network model has more effective recognition results and better generalization performance for gait phase recognition of the lower limb exoskeleton system compared with other models.
Exoskeleton robots have become an emerging technology in medical, industrial and military applications. Human gait phase recognition is the crucial technology for recognizing movement intention of the exoskeleton wearer and controlling the exoskeleton robot. As a new biometric recognition method, gait phase recognition also plays an important role in clinical disease diagnosis, rehabilitation training and other fields. This paper proposes an integrated network model SBLSTM that combines sparse autoencoder (SAE), bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) aiming at gait phase recognition during human movement. The model can accurately identify four phases in the gait cycle, including heel strike (HS), foot flat (FF), heel off (HO) and swing phase (SW). Normalization and feature extraction of collected sensor signals are performed to enhance the accuracy of recognition during the gait identification process. The processed data are input into the SBLSTM model. The introduction of SAE into the model can extract key information from gait characteristics. BiLSTM is used to learn temporal patterns and periodic changes in gait data. DNN is adopted to identify gait phases and output classification results. Different algorithms such as DNN, LSTM and SBLSTM are applied to the gait phase detection of subjects. The experimental results show that the SBLSTM algorithm is effective in gait recognition. The accuracy and F-score are outperformed by other algorithms, which verifies the effectiveness of the SBLSTM in practice. |
| ArticleNumber | 103693 |
| Author | Wang, Zhaoyang Zhang, Zaifang Lei, Han Gu, Wenquan |
| Author_xml | – sequence: 1 givenname: Zaifang surname: Zhang fullname: Zhang, Zaifang organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 2 givenname: Zhaoyang surname: Wang fullname: Wang, Zhaoyang organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 3 givenname: Han surname: Lei fullname: Lei, Han organization: Shanghai Punan Hospital, Shanghai 200125, China – sequence: 4 givenname: Wenquan surname: Gu fullname: Gu, Wenquan email: pdnl3885gwq@126.com organization: Shanghai Punan Hospital, Shanghai 200125, China |
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| CitedBy_id | crossref_primary_10_1109_TNSRE_2023_3336360 crossref_primary_10_1088_1741_2552_accd22 crossref_primary_10_1016_j_gaitpost_2024_12_028 crossref_primary_10_3390_s24051505 crossref_primary_10_1109_ACCESS_2025_3544758 crossref_primary_10_1177_09544062241235084 crossref_primary_10_1109_JSEN_2023_3328615 crossref_primary_10_1007_s11227_023_05156_9 crossref_primary_10_1016_j_measen_2025_101865 crossref_primary_10_1007_s42235_025_00723_7 crossref_primary_10_1109_ACCESS_2024_3414175 crossref_primary_10_3390_electronics14010107 crossref_primary_10_1016_j_engappai_2025_110106 crossref_primary_10_1007_s41315_024_00334_1 crossref_primary_10_1007_s00521_023_09154_z crossref_primary_10_3390_s24186014 crossref_primary_10_1016_j_bspc_2022_104163 crossref_primary_10_1007_s12008_024_02076_7 crossref_primary_10_1186_s12984_025_01605_z crossref_primary_10_1016_j_measurement_2024_114416 crossref_primary_10_1115_1_4068865 crossref_primary_10_1016_j_jneumeth_2025_110469 crossref_primary_10_1109_ACCESS_2025_3598038 crossref_primary_10_1109_TASE_2024_3421276 crossref_primary_10_1109_JSEN_2025_3546182 crossref_primary_10_3390_bios15050305 crossref_primary_10_3390_s23115311 crossref_primary_10_3390_s23135905 |
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| Keywords | Deep neural network Sparse autoencoder Gait phase recognition Bidirectional long short-term memory Exoskeleton robot |
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