Interactive control methods for rehabilitation robot

Liang, Xu; Wang, Weiqun; Hou, Zeng-Guang; Ren, Shixin; Peng, Liang; Jin, Hu

doi:10.1360/n112017-00072

Cited by 5 publications

(6 citation statements)

References 80 publications

(91 reference statements)

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“…Because of the universality of ERPs and higher signal-to-noise ratio, it often requires less training time [17]. As a subclass of event-related units, evoked potentials are generally considered to be related to physiological responses of the nervous system to specific modal stimuli.…”

Section: Eeg Decoding Strategy For Eeg Signalsmentioning

confidence: 99%

“…Because of its rich dimensions and non-invasive characteristics, EMG signals become another major physiological signal of human-computer interaction [17]. Root mean square (RMS), integrated electromyography (iEMG), zero crossing points, waveform length, variance and absolute value are time domain features commonly used to decode EMG [21].…”

Section: Surface Electromyography (Semg) Decoding Strategy For Electr...mentioning

confidence: 99%

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Research on physiological signal analysis based on clinical statistics and machine learning

Feng

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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Neural network evaluation and decoding technology based on physiological signal analysis has become a hot research topic in the field of life and health. The decoding of physiological signals can enable people to break through physical limitations to communicate with the external environment, and can also enhance the effectiveness of the human body by evaluating people's cognitive and motor functions. Electroencephalogram (EEG), as a kind of physiological signal, has become a hot spot of much research due to its non-invasive, high time precision and strong explanatory characteristics. This paper summarizes the applied research based on physiological signal analysis such as EEG from three aspects of evaluation technology, human-computer interaction and clinical application, and looks forward to the development direction in the future.

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Section: Eeg Decoding Strategy For Eeg Signalsmentioning

confidence: 99%

Section: Surface Electromyography (Semg) Decoding Strategy For Electr...mentioning

confidence: 99%

Research on physiological signal analysis based on clinical statistics and machine learning

Feng

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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“…The fitness value in this paper is expressed by mean square error (MSE); (5) The distances are calculated according to (13) by updating the nonlinear convergence factor a. Then the new individual position → X(t + 1) is obtained according to (20); that is, the new SVR parameter values are obtained; (6) If the number of iterations reaches the maximum, the iteration is terminated, and the optimal parameters C and γ are output; Otherwise, return to (3) and continue the iteration; (7) The optimized parameters C and γ are used to establish the SVR estimation model.…”

Section: Igwo-svr Model Constructionmentioning

confidence: 99%

“…Auxiliary training instruments and intelligent prosthetic devices need to realize comfortable, natural, and flexible human-machine interaction and human-machine coordinated movement. The key technology lies in the simple, real-time, and accurate acquisition of human motion intentions and parameters related to human dynamics through biological signals on the surface of the human skin [5,6]. In particular, joint extension force estimation is an effective way to improve the flexibility of human joint auxiliary training equipment.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Gao

et al. 2021

Electronics

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Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.

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“…Rehabilitation of stroke patients usually needs long-term help from physical therapists, which brings a heavy economic burden for those patients. Meanwhile, the gap between the numbers of patients and physical therapists has limited the available rehabilitation care [2]. To solve this issue, rehabilitation robots have been used to assist the repetitive rehabilitation training tasks.…”

Section: Introductionmentioning

confidence: 99%

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Cao

Cheng

Yang

et al. 2021

Sci. China Technol. Sci.

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Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. Control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system's stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient's motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

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Interactive control methods for rehabilitation robot

Cited by 5 publications

References 80 publications

Research on physiological signal analysis based on clinical statistics and machine learning

Research on physiological signal analysis based on clinical statistics and machine learning

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Iterative assist-as-needed control with interaction factor for rehabilitation robots

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