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Yu F, Liu Y, Wu Z, Tan M, Yu J. Adaptive Gait Training of a Lower Limb Rehabilitation Robot Based on Human-Robot Interaction Force Measurement. CYBORG AND BIONIC SYSTEMS 2024; 5:0115. [PMID: 38912323 PMCID: PMC11192188 DOI: 10.34133/cbsystems.0115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/22/2024] [Indexed: 06/25/2024] Open
Abstract
The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients, ignoring their residual muscle strength. To enhance patient participation and safety in training, this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human-robot interaction force measurement. Firstly, a novel lower limb rehabilitation robot system with several active and passive driven joints is developed, and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human-robot interaction forces. Secondly, a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state. Thereafter, based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors, an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer's movement intention. Finally, interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system. The proposed rehabilitation training robot system with adaptive gaits offers great potential for future high-quality rehabilitation training, e.g., improving participation and safety.
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Affiliation(s)
- Fuyang Yu
- School of Artificial Intelligence,
University of Chinese Academy of Sciences, Beijing, China
- The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing, China
- School of Mechanical Engineering & Automation,
Northeastern University, Shenyang, China
| | - Yu Liu
- School of Mechanical Engineering & Automation,
Northeastern University, Shenyang, China
| | - Zhengxing Wu
- School of Artificial Intelligence,
University of Chinese Academy of Sciences, Beijing, China
- The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing, China
| | - Min Tan
- School of Artificial Intelligence,
University of Chinese Academy of Sciences, Beijing, China
- The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing, China
| | - Junzhi Yu
- The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing, China
- State Key Laboratory for Turbulence and Complex System, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering,
Peking University, Beijing, China
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Li Z, Gao L, Zhang G, Lu W, Wang D, Zhang J, Cao H. MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM. Bioengineering (Basel) 2024; 11:470. [PMID: 38790337 PMCID: PMC11117547 DOI: 10.3390/bioengineering11050470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.
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Affiliation(s)
- Zebin Li
- Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center, West Anhui University, Lu’an 237012, China;
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
| | - Lifu Gao
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
- Department of Science Island, University of Science and Technology of China, Hefei 230031, China
| | - Gang Zhang
- Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center, West Anhui University, Lu’an 237012, China;
| | - Wei Lu
- School of Management, Fujian University of Technology, Fuzhou 350118, China;
| | - Daqing Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
| | - Jinzhong Zhang
- Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center, West Anhui University, Lu’an 237012, China;
| | - Huibin Cao
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
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Ding G, Georgilas I, Plummer A. A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes. SENSORS (BASEL, SWITZERLAND) 2023; 24:211. [PMID: 38203073 PMCID: PMC10781404 DOI: 10.3390/s24010211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired t-tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired t-test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns.
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Affiliation(s)
| | - Ioannis Georgilas
- Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK; (G.D.); (A.P.)
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Wang F, Liang W, Afzal HMR, Fan A, Li W, Dai X, Liu S, Hu Y, Li Z, Yang P. Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:9039. [PMID: 38005427 PMCID: PMC10674933 DOI: 10.3390/s23229039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.
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Affiliation(s)
- Fanjie Wang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Wenqi Liang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Hafiz Muhammad Rehan Afzal
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Ao Fan
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Wenjiong Li
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Xiaoqian Dai
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Shujuan Liu
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Yiwei Hu
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
| | - Zhili Li
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing 100094, China; (W.L.); (X.D.); (S.L.)
| | - Pengfei Yang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China; (F.W.); (W.L.); (H.M.R.A.); (A.F.); (Y.H.)
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Liang T, Sun N, Wang Q, Bu J, Li L, Chen Y, Cao M, Ma J, Liu T. sEMG-Based End-to-End Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model. IEEE J Biomed Health Inform 2023; 27:5272-5280. [PMID: 37566511 DOI: 10.1109/jbhi.2023.3304639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots. The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R2, and (CC) values of the model are 3.79°, 0.96, and 0.98, respectively, which are better than traditional deep learning models such as Informer (4.14, 0.95, 0.98), CNN (5.56, 0.89, 0.96) and CNN-BiLSTM (3.97, 0.95, 0.98). In addition, the prediction time of our proposed model is only 11.67 ± 0.67 ms, which is less than 100 ms. Therefore, the real-time and accuracy of the model can meet the continuous prediction of human knee joint angle in practice.
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Wu Q, Wang Z, Chen Y. sEMG-Based Adaptive Cooperative Multi-Mode Control of a Soft Elbow Exoskeleton Using Neural Network Compensation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3384-3396. [PMID: 37590115 DOI: 10.1109/tnsre.2023.3306201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Soft rehabilitation exoskeletons have gained much attention in recent years, striving to assist the paralyzed individuals restore motor functions. However, it is a challenge to promote human-robot interaction property and satisfy personalized training requirements. This article proposes a soft elbow rehabilitation exoskeleton for the multi-mode training of disabled patients. An adaptive cooperative admittance backstepping control strategy combined with surface electromyography (sEMG)-based joint torque estimation and neural network compensation is developed, which can induce the active participation of patients and guarantee the accomplishment and safety of training. The proposed control scheme can be transformed into four rehabilitation training modes to optimize the cooperative training performance. Experimental studies involving four healthy subjects and four paralyzed subjects are carried out. The average root mean square error and peak error in trajectory tracking test are 3.18° and 5.68°. The active cooperation level can be adjusted via admittance model, ranging from 4.51 °/Nm to 10.99 °/Nm. In cooperative training test, the average training mode value and effort score of healthy subjects (i.e., 1.58 and 1.50) are lower than those of paralyzed subjects (i.e., 2.42 and 3.38), while the average smoothness score and stability score of healthy subjects (i.e., 3.25 and 3.42) are higher than those of paralyzed subjects (i.e., 1.67 and 1.71). The experimental results verify the superiority of proposed control strategy in improving position control performance and satisfying the training requirements of the patients with different hemiplegia degrees and training objectives.
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