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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| Veröffentlicht in: | Biomedical signal processing and control Jg. 76; S. 103693 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.07.2022
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| ISSN: | 1746-8094, 1746-8108 |
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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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| Cites_doi | 10.3390/s20143972 10.3390/app8091462 10.1016/j.neucom.2019.07.104 10.3390/s19132988 10.1109/TCDS.2020.2968845 10.3390/a12120253 10.1007/s11517-021-02335-9 10.1016/j.patcog.2013.06.028 10.1016/j.bspc.2021.102587 10.1631/FITEE.1800601 10.1109/TNSRE.2014.2337914 10.1016/j.robot.2019.06.008 10.1016/j.neucom.2020.01.098 10.1109/JSEN.2019.2959639 10.1007/s11370-021-00367-6 10.1016/j.jot.2015.09.007 10.3390/ijerph17165633 10.1016/j.mechmachtheory.2019.01.016 10.3390/s21082807 10.1016/j.bspc.2021.102781 10.1080/00140139.2016.1174314 10.1109/SAS48726.2020.9220037 10.3390/s20174966 10.1109/UR52253.2021.9494647 10.1007/s12369-021-00794-6 10.1016/j.irbm.2019.11.001 10.3390/s20041104 10.3390/s16010066 10.1016/j.robot.2014.08.012 10.1049/ccs2.12007 10.1016/j.smhl.2020.100162 10.1016/j.gaitpost.2017.06.019 10.1007/s40430-021-03016-2 10.1016/j.bspc.2020.102279 10.3390/bios10090109 10.3390/s19183960 |
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| Keywords | Deep neural network Sparse autoencoder Gait phase recognition Bidirectional long short-term memory Exoskeleton robot |
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| References | Akhil, Ashmi, Rajendrakumar, Sivanandan (b0045) 2020; 41 Zhen, Yan, Yuan (b0070) 2019; 12 Qin, Yang, Wen, Chen, Bao, Dong, Dou, Yang (b0100) 2021; 14 Taborri, Palermo, Rossi, Cappa (b0065) 2016; 16 Mangialardi, Kim (b0075) 2013 Kim, Lee (b0090) 2017; 60 Lu, Wang, Hu, Zhou, Xi (b0125) 2021; 59 Lu, Wang, Qi, Xi (b0195) 2021; 64 Huang, Zheng, Hu (b0150) 2022; 14 Zeng, Qu, Ma, Qu, Yin, Zhao, Xia (b0020) 2021; 43 Zhou, Shan, Fang, Zhang, Sun, Ding, Wang, Zhang (b0160) 2021; 3 S.U. Yunas, K.B. Ozanyan, Gait Activity Classification using Multi-Modality Sensor Fusion: A Deep Learning Approach, IEEE Sens. J., (2021) 1-1. Novak, Riener (b0130) 2015; 73 Tong, Liu, Peng (b0185) 2020; 20 Zhang, Sun, Li, Tang (b0010) 2018; 8 Moon, Kim, Hong (b0015) 2019; 19 Wang, Wu, Ma, Wu, Luo (b0080) 2018; 2018 Liu, Zhou, Mai, Wang (b0030) 2019; 119 Sheng, Li (b0180) 2020; 395 Y.P. Zhang, G.Z. Cao, Z.Q. Ling, B.B. He, H.R. Cheng, W.Z. Li, S.B. Cao, A Real-Time Gait Phase Recognition Method Based on Multi-Information Fusion, 2021 18th International Conference on Ubiquitous Robots (UR), 2021, pp. 249-255. Qiu, Liu, Guo (b0110) 2021; 2021 Yu, Li, Zhang, Xue, Wang (b0050) 2020; 390 Papavasileiou, Qiao, Zhang, Zhang, Bi, Han (b0175) 2021; 19 Ma, Liu, Song, Wang (b0165) 2020; 20 Qiu, Guo, Caldwell, Chen (b0035) 2021; 13 Sánchez Manchola, Bernal, Munera, Cifuentes (b0095) 2019; 19 Chen, Zi, Wang, Qin, Liao (b0060) 2019; 134 Chen, Ma, Qin, Gao, Chan, Law, Qin, Liao (b0005) 2016; 5 Lee, Kim, Lee (b0040) 2021; 21 Ding, Yang, Xing, Ma, Yang, Guo, Yi, Jiang (b0085) 2018; 2018 Su, Smith, Gutierrez Farewik (b0115) 2020; 10 R. Caldas, M. Mundt, W. Potthast, F. Buarque de Lima Neto, B. Markert, A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms, Gait Posture, 57 (2017) 204-210. Vu, Dong, Cao, Verstraten, Lefeber, Vanderborght, Geeroms (b0135) 2020; 20 Ngo, Makihara, Nagahara, Mukaigawa, Yagi (b0205) 2014; 47 Zhen, Yan, Kong (b0170) 2020; 17 Wang, Dong, Chi, Wang, Miao, An, Gavrilov (b0200) 2021; 68 Xie, Li, Zhao, Li (b0055) 2020; 20 Wei, Zhang, Tian, Hong (b0105) 2021; 68 Bejarano, Ambrosini, Pedrocchi, Ferrigno, Monticone, Ferrante (b0140) 2015; 23 Kyeong, Shin, Yang, Heo, Feng, Kim (b0025) 2019; 20 X. Wu, Y. Yuan, X. Zhang, C. Wang, T. Xu, D. Tao, Gait Phase Classification for a Lower Limb Exoskeleton System Based on a Graph Convolutional Network Model, IEEE Trans. Ind. Electron. (2021) 1-1. Xie (10.1016/j.bspc.2022.103693_b0055) 2020; 20 Lu (10.1016/j.bspc.2022.103693_b0125) 2021; 59 10.1016/j.bspc.2022.103693_b0155 Chen (10.1016/j.bspc.2022.103693_b0060) 2019; 134 Lee (10.1016/j.bspc.2022.103693_b0040) 2021; 21 Akhil (10.1016/j.bspc.2022.103693_b0045) 2020; 41 Kim (10.1016/j.bspc.2022.103693_b0090) 2017; 60 Ma (10.1016/j.bspc.2022.103693_b0165) 2020; 20 Ngo (10.1016/j.bspc.2022.103693_b0205) 2014; 47 Wei (10.1016/j.bspc.2022.103693_b0105) 2021; 68 Zhen (10.1016/j.bspc.2022.103693_b0170) 2020; 17 Zhen (10.1016/j.bspc.2022.103693_b0070) 2019; 12 Wang (10.1016/j.bspc.2022.103693_b0200) 2021; 68 Su (10.1016/j.bspc.2022.103693_b0115) 2020; 10 Liu (10.1016/j.bspc.2022.103693_b0030) 2019; 119 10.1016/j.bspc.2022.103693_b0145 Taborri (10.1016/j.bspc.2022.103693_b0065) 2016; 16 Huang (10.1016/j.bspc.2022.103693_b0150) 2022; 14 10.1016/j.bspc.2022.103693_b0120 Vu (10.1016/j.bspc.2022.103693_b0135) 2020; 20 Sánchez Manchola (10.1016/j.bspc.2022.103693_b0095) 2019; 19 Sheng (10.1016/j.bspc.2022.103693_b0180) 2020; 395 Zeng (10.1016/j.bspc.2022.103693_b0020) 2021; 43 Moon (10.1016/j.bspc.2022.103693_b0015) 2019; 19 Mangialardi (10.1016/j.bspc.2022.103693_b0075) 2013 Ding (10.1016/j.bspc.2022.103693_b0085) 2018; 2018 Qin (10.1016/j.bspc.2022.103693_b0100) 2021; 14 Tong (10.1016/j.bspc.2022.103693_b0185) 2020; 20 Kyeong (10.1016/j.bspc.2022.103693_b0025) 2019; 20 Wang (10.1016/j.bspc.2022.103693_b0080) 2018; 2018 Qiu (10.1016/j.bspc.2022.103693_b0035) 2021; 13 Chen (10.1016/j.bspc.2022.103693_b0005) 2016; 5 Novak (10.1016/j.bspc.2022.103693_b0130) 2015; 73 Bejarano (10.1016/j.bspc.2022.103693_b0140) 2015; 23 Zhang (10.1016/j.bspc.2022.103693_b0010) 2018; 8 Qiu (10.1016/j.bspc.2022.103693_b0110) 2021; 2021 Lu (10.1016/j.bspc.2022.103693_b0195) 2021; 64 10.1016/j.bspc.2022.103693_b0190 Zhou (10.1016/j.bspc.2022.103693_b0160) 2021; 3 Papavasileiou (10.1016/j.bspc.2022.103693_b0175) 2021; 19 Yu (10.1016/j.bspc.2022.103693_b0050) 2020; 390 |
| References_xml | – volume: 21 start-page: 2807 year: 2021 ident: b0040 article-title: Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network publication-title: Sensors (Basel) – volume: 16 start-page: 66 year: 2016 ident: b0065 article-title: Gait partitioning methods: A systematic review publication-title: Sensors – volume: 12 start-page: 253 year: 2019 ident: b0070 article-title: Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm publication-title: Algorithms – volume: 68 start-page: 102781 year: 2021 ident: b0200 article-title: sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network publication-title: Biomed. Signal Process. Control – volume: 10 start-page: 109 year: 2020 ident: b0115 article-title: Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units publication-title: Biosensors (Basel) – volume: 390 start-page: 268 year: 2020 end-page: 279 ident: b0050 article-title: Estimation of human impedance and motion intention for constrained human–robot interaction publication-title: Neurocomputing – volume: 60 start-page: 384 year: 2017 end-page: 403 ident: b0090 article-title: Development of an IMU-based foot-ground contact detection (FGCD) algorithm publication-title: Ergonomics – volume: 2018 start-page: 1 year: 2018 end-page: 9 ident: b0080 article-title: A Flexible Lower Extremity Exoskeleton Robot with Deep Locomotion Mode Identification publication-title: Complexity – volume: 14 start-page: 413 year: 2022 end-page: 428 ident: b0150 article-title: Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units publication-title: Int. J. Soc. Robot. – volume: 20 start-page: 3972 year: 2020 ident: b0135 article-title: A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses publication-title: Sensors (Basel) – volume: 19 start-page: 100162 year: 2021 ident: b0175 article-title: GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors publication-title: Smart Health – volume: 17 start-page: 5633 year: 2020 ident: b0170 article-title: An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection publication-title: Int. J. Environ. Res. Public Health – volume: 5 start-page: 26 year: 2016 end-page: 37 ident: b0005 article-title: Recent developments and challenges of lower extremity exoskeletons publication-title: J. Orthop. Translat. – reference: Y.P. Zhang, G.Z. Cao, Z.Q. Ling, B.B. He, H.R. Cheng, W.Z. Li, S.B. Cao, A Real-Time Gait Phase Recognition Method Based on Multi-Information Fusion, 2021 18th International Conference on Ubiquitous Robots (UR), 2021, pp. 249-255. – volume: 64 start-page: 102279 year: 2021 ident: b0195 article-title: Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models publication-title: Biomed. Signal Process. Control – volume: 19 start-page: 3960 year: 2019 ident: b0015 article-title: Development of a single leg knee exoskeleton and sensing knee center of rotation change for intention detection publication-title: Sensors – volume: 14 start-page: 445 year: 2021 end-page: 457 ident: b0100 article-title: Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot publication-title: Intel. Serv. Robot. – volume: 20 start-page: 3667 year: 2020 end-page: 3677 ident: b0185 article-title: LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System publication-title: IEEE Sens. J. – volume: 20 start-page: 342 year: 2019 end-page: 352 ident: b0025 article-title: Recognition of walking environments and gait period by surface electromyography publication-title: Front. Inform. Technol. Electron. Eng. – volume: 47 start-page: 228 year: 2014 end-page: 237 ident: b0205 article-title: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication publication-title: Pattern Recogn. – volume: 20 start-page: 4966 year: 2020 ident: b0165 article-title: Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature publication-title: Sensors (Basel) – volume: 43 year: 2021 ident: b0020 article-title: Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot publication-title: J. Braz. Soc. Mech. Sci. Eng. – volume: 8 start-page: 1462 year: 2018 ident: b0010 article-title: Impact of load variation on the accuracy of gait recognition from surface EMG signals publication-title: Appl. Sci. – volume: 2018 start-page: 33 year: 2018 end-page: 38 ident: b0085 article-title: The Real Time Gait Phase Detection Based on Long Short-Term Memory publication-title: IEEE Third International Conference on Data Science in Cyberspace (DSC) – volume: 3 start-page: 61 year: 2021 end-page: 69 ident: b0160 article-title: Personal-specific gait recognition based on latent orthogonal feature space publication-title: Cognit. Comput. Syst. – volume: 119 start-page: 209 year: 2019 end-page: 220 ident: b0030 article-title: Real-time mode recognition based assistive torque control of bionic knee exoskeleton for sit-to-stand and stand-to-sit transitions publication-title: Rob. Auton. Syst. – year: 2013 ident: b0075 article-title: Quantifying Dynamic Characteristics of Human Walking for Comprehensive Gait publication-title: Cycle – volume: 20 start-page: 1104 year: 2020 ident: b0055 article-title: Prediction of Limb Joint Angles Based on Multi-Source Signals by GS-GRNN for Exoskeleton Wearer publication-title: Sensors (Basel) – volume: 23 start-page: 413 year: 2015 end-page: 422 ident: b0140 article-title: A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 59 start-page: 883 year: 2021 end-page: 899 ident: b0125 article-title: Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning publication-title: Med. Biol. Eng. Comput. – volume: 73 start-page: 155 year: 2015 end-page: 170 ident: b0130 article-title: A survey of sensor fusion methods in wearable robotics publication-title: Rob. Auton. Syst. – volume: 395 start-page: 86 year: 2020 end-page: 94 ident: b0180 article-title: Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition publication-title: Neurocomputing – volume: 2021 start-page: 1 year: 2021 end-page: 13 ident: b0110 article-title: Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition publication-title: Math. Probl. Eng. – volume: 19 start-page: 2988 year: 2019 ident: b0095 article-title: Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals publication-title: Sensors – volume: 68 year: 2021 ident: b0105 article-title: A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition publication-title: Biomed. Signal Process. Control – reference: S.U. Yunas, K.B. Ozanyan, Gait Activity Classification using Multi-Modality Sensor Fusion: A Deep Learning Approach, IEEE Sens. J., (2021) 1-1. – volume: 13 start-page: 67 year: 2021 end-page: 79 ident: b0035 article-title: Exoskeleton Online Learning and Estimation of Human Walking Intention Based on Dynamical Movement Primitives publication-title: IEEE Trans. Cognit. Devel. Syst. – volume: 134 start-page: 499 year: 2019 end-page: 511 ident: b0060 article-title: Knee exoskeletons for gait rehabilitation and human performance augmentation: A state-of-the-art publication-title: Mech. Mach. Theory – volume: 41 start-page: 133 year: 2020 end-page: 140 ident: b0045 article-title: Human Gait Recognition Using Hip, Knee and Ankle Joint Ratios publication-title: IRBM – reference: X. Wu, Y. Yuan, X. Zhang, C. Wang, T. Xu, D. Tao, Gait Phase Classification for a Lower Limb Exoskeleton System Based on a Graph Convolutional Network Model, IEEE Trans. Ind. Electron. (2021) 1-1. – reference: R. Caldas, M. Mundt, W. Potthast, F. Buarque de Lima Neto, B. Markert, A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms, Gait Posture, 57 (2017) 204-210. – volume: 20 start-page: 3972 issue: 14 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0135 article-title: A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses publication-title: Sensors (Basel) doi: 10.3390/s20143972 – volume: 8 start-page: 1462 year: 2018 ident: 10.1016/j.bspc.2022.103693_b0010 article-title: Impact of load variation on the accuracy of gait recognition from surface EMG signals publication-title: Appl. Sci. doi: 10.3390/app8091462 – volume: 390 start-page: 268 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0050 article-title: Estimation of human impedance and motion intention for constrained human–robot interaction publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.07.104 – volume: 19 start-page: 2988 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0095 article-title: Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals publication-title: Sensors doi: 10.3390/s19132988 – volume: 13 start-page: 67 issue: 1 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0035 article-title: Exoskeleton Online Learning and Estimation of Human Walking Intention Based on Dynamical Movement Primitives publication-title: IEEE Trans. Cognit. Devel. Syst. doi: 10.1109/TCDS.2020.2968845 – volume: 12 start-page: 253 issue: 12 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0070 article-title: Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm publication-title: Algorithms doi: 10.3390/a12120253 – volume: 59 start-page: 883 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0125 article-title: Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-021-02335-9 – volume: 47 start-page: 228 year: 2014 ident: 10.1016/j.bspc.2022.103693_b0205 article-title: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2013.06.028 – volume: 2018 start-page: 33 year: 2018 ident: 10.1016/j.bspc.2022.103693_b0085 article-title: The Real Time Gait Phase Detection Based on Long Short-Term Memory publication-title: IEEE Third International Conference on Data Science in Cyberspace (DSC) – volume: 68 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0105 article-title: A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.102587 – volume: 20 start-page: 342 issue: 3 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0025 article-title: Recognition of walking environments and gait period by surface electromyography publication-title: Front. Inform. Technol. Electron. Eng. doi: 10.1631/FITEE.1800601 – volume: 23 start-page: 413 year: 2015 ident: 10.1016/j.bspc.2022.103693_b0140 article-title: A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2014.2337914 – volume: 119 start-page: 209 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0030 article-title: Real-time mode recognition based assistive torque control of bionic knee exoskeleton for sit-to-stand and stand-to-sit transitions publication-title: Rob. Auton. Syst. doi: 10.1016/j.robot.2019.06.008 – volume: 2018 start-page: 1 year: 2018 ident: 10.1016/j.bspc.2022.103693_b0080 article-title: A Flexible Lower Extremity Exoskeleton Robot with Deep Locomotion Mode Identification publication-title: Complexity – volume: 395 start-page: 86 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0180 article-title: Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.01.098 – volume: 2021 start-page: 1 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0110 article-title: Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition publication-title: Math. Probl. Eng. – volume: 20 start-page: 3667 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0185 article-title: LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2959639 – volume: 14 start-page: 445 issue: 3 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0100 article-title: Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot publication-title: Intel. Serv. Robot. doi: 10.1007/s11370-021-00367-6 – volume: 5 start-page: 26 year: 2016 ident: 10.1016/j.bspc.2022.103693_b0005 article-title: Recent developments and challenges of lower extremity exoskeletons publication-title: J. Orthop. Translat. doi: 10.1016/j.jot.2015.09.007 – volume: 17 start-page: 5633 issue: 16 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0170 article-title: An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph17165633 – volume: 134 start-page: 499 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0060 article-title: Knee exoskeletons for gait rehabilitation and human performance augmentation: A state-of-the-art publication-title: Mech. Mach. Theory doi: 10.1016/j.mechmachtheory.2019.01.016 – volume: 21 start-page: 2807 issue: 8 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0040 article-title: Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network publication-title: Sensors (Basel) doi: 10.3390/s21082807 – volume: 68 start-page: 102781 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0200 article-title: sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.102781 – volume: 60 start-page: 384 year: 2017 ident: 10.1016/j.bspc.2022.103693_b0090 article-title: Development of an IMU-based foot-ground contact detection (FGCD) algorithm publication-title: Ergonomics doi: 10.1080/00140139.2016.1174314 – ident: 10.1016/j.bspc.2022.103693_b0190 doi: 10.1109/SAS48726.2020.9220037 – volume: 20 start-page: 4966 issue: 17 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0165 article-title: Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature publication-title: Sensors (Basel) doi: 10.3390/s20174966 – ident: 10.1016/j.bspc.2022.103693_b0155 doi: 10.1109/UR52253.2021.9494647 – volume: 14 start-page: 413 issue: 2 year: 2022 ident: 10.1016/j.bspc.2022.103693_b0150 article-title: Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units publication-title: Int. J. Soc. Robot. doi: 10.1007/s12369-021-00794-6 – volume: 41 start-page: 133 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0045 article-title: Human Gait Recognition Using Hip, Knee and Ankle Joint Ratios publication-title: IRBM doi: 10.1016/j.irbm.2019.11.001 – volume: 20 start-page: 1104 issue: 4 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0055 article-title: Prediction of Limb Joint Angles Based on Multi-Source Signals by GS-GRNN for Exoskeleton Wearer publication-title: Sensors (Basel) doi: 10.3390/s20041104 – volume: 16 start-page: 66 year: 2016 ident: 10.1016/j.bspc.2022.103693_b0065 article-title: Gait partitioning methods: A systematic review publication-title: Sensors doi: 10.3390/s16010066 – volume: 73 start-page: 155 year: 2015 ident: 10.1016/j.bspc.2022.103693_b0130 article-title: A survey of sensor fusion methods in wearable robotics publication-title: Rob. Auton. Syst. doi: 10.1016/j.robot.2014.08.012 – volume: 3 start-page: 61 issue: 1 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0160 article-title: Personal-specific gait recognition based on latent orthogonal feature space publication-title: Cognit. Comput. Syst. doi: 10.1049/ccs2.12007 – volume: 19 start-page: 100162 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0175 article-title: GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors publication-title: Smart Health doi: 10.1016/j.smhl.2020.100162 – ident: 10.1016/j.bspc.2022.103693_b0145 doi: 10.1016/j.gaitpost.2017.06.019 – volume: 43 issue: 6 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0020 article-title: Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot publication-title: J. Braz. Soc. Mech. Sci. Eng. doi: 10.1007/s40430-021-03016-2 – volume: 64 start-page: 102279 year: 2021 ident: 10.1016/j.bspc.2022.103693_b0195 article-title: Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2020.102279 – year: 2013 ident: 10.1016/j.bspc.2022.103693_b0075 article-title: Quantifying Dynamic Characteristics of Human Walking for Comprehensive Gait publication-title: Cycle – volume: 10 start-page: 109 issue: 9 year: 2020 ident: 10.1016/j.bspc.2022.103693_b0115 article-title: Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units publication-title: Biosensors (Basel) doi: 10.3390/bios10090109 – ident: 10.1016/j.bspc.2022.103693_b0120 – volume: 19 start-page: 3960 year: 2019 ident: 10.1016/j.bspc.2022.103693_b0015 article-title: Development of a single leg knee exoskeleton and sensing knee center of rotation change for intention detection publication-title: Sensors doi: 10.3390/s19183960 |
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