EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

Teramae, Tatsuya; Noda, Tomoyuki; Morimoto, Jun

doi:10.1109/lra.2017.2737478

Cited by 108 publications

(68 citation statements)

References 25 publications

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“…Compared to the electromyography (EMG) or vision based rehabilitation system [18,19], the proposed system not only can be expected to fully explore the movement intention of subjects, but also can stretch the upper limb to perform recovery exercises smoothly with multiple trajectories recommended by therapists. Moreover, we can also combine the EMG or vision into our EEG-based system and carry out more research on the EEG processing for the purpose of predicting the recovery of the upper limb.…”

Section: Resultsmentioning

confidence: 99%

EEG-modulated robotic rehabilitation system for upper extremity

Song

Zhao³

et al. 2018

Biotechnology & Biotechnological Equipment

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This paper presents a novel electroencephalogram (EEG)-triggered upper extremity training system. Motor imagery EEG of upper extremity movements is adopted to trigger the Barrett WAM to perform rehabilitation training for patients with stroke. We focus on fully exploring the patient's movement intention and attention from movement imagination EEG and controlling the WAM robot to perform training effectively. A position controller based on fuzzy logic is presented for the rehabilitation system to drive the WAM robot smoothly. Experimental results with seven participants are reported to show the feasibility and effectiveness of the robotic therapy system.

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Section: Resultsmentioning

confidence: 99%

EEG-modulated robotic rehabilitation system for upper extremity

Song

Zhao³

et al. 2018

Biotechnology & Biotechnological Equipment

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“…The schematic diagram of sEMG signals segmentation is shown in Figure 4. The generation of sEMG signals was 20-200 ms earlier than the corresponding muscle action [7,8]. sEMG and angle data were collected at the same time.…”

Section: Feature Extraction Of Semg and Time-advanced Feature Signalsmentioning

confidence: 99%

“…Electrodes attached on the surface of muscles can collect on the surface of EMG (sEMG) signals, retaining most of the information. In the case of non-invasive acquisition, sEMG has better stability and stronger anti-interference performance than EEG, occurring about 20-200 ms earlier than the corresponding muscle contraction [7,8]. There is a certain degree of delay when physical signals such as angle and angular velocity are used to estimate human motion intention.…”

Section: Introductionmentioning

confidence: 99%

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

Liu

Song

et al. 2020

Sensors

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Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

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“…They have been demonstrated to provide repeated and progressive rehabilitation training required for the effective recovery of patients with limb disabilities. To meet the requirement of assistance at different rehabilitation stages, active or passive rehabilitation robots of multiple degrees-of-freedom (DoFs) have been developed to assist the motion of human upper limbs [1][2][3][4][5][6][7][8][9][10][11][12][13] or lower limbs [14][15][16][17][18]. Compared with end-effector type robots [2,[11][12][13]18], exoskeleton type robots [1,[3][4][5][6][7][8][9][10][14][15][16][17] can provide direct assistance and recovery evaluation at each human joint.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

Lin

Lai

et al. 2020

IEEE Access

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Demand for robot-assisted therapy has increased at every stage of the neurorehabilitation recovery. This paper presents a controller that is suitable for the assist-as-needed (AAN) training of the wrist when performing the spatial motion. A compact wrist exoskeleton robot is presented to realize the AAN controller. This wrist robot includes series elastic actuators with high torque-to-weight ratios to provide accurate force control required for the AAN controller. In addition to assist-as-needed rehabilitation, the parameters of the AAN controller can be adjusted to deliver passive, active, or resistive rehabilitation. Experimental results demonstrate that the proposed AAN controller can provide the total solution to cover each stage of wrist spatial-motion rehabilitation.

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EMG-Based Model Predictive Control for Physical Human–Robot Interaction: Application for Assist-As-Needed Control

Cited by 108 publications

References 25 publications

EEG-modulated robotic rehabilitation system for upper extremity

EEG-modulated robotic rehabilitation system for upper extremity

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

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