Design of a Novel Telerehabilitation System with a  Force-Sensing Mechanism

Zhang, Songyuan; Guo, Shuxiang; Gao, Baofeng; Hirata, Hideyuki; Ishihara, Hidenori

doi:10.3390/s150511511

Cited by 54 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Master-slave control mainly moves puncture needle from the starting point to the vicinity of entry point and remotely implants radioactive seeds. In mastersalve mode, the robot's movement follows the doctor's hand, avoids collision with the patient's body, and positions the operation needle in the vicinity of the entry point [30,31].…”

Section: Control Strategymentioning

confidence: 99%

Control Strategy and Experiments for Robot Assisted Craniomaxillofacial Surgery System

Cui

Wang

Duan

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Since the intricate anatomical structure of the craniomaxillofacial region and the limitation of surgical field and instrument, the current surgery is extremely of high risk and difficult to implement. The puncturing operations for biopsy, ablation, and brachytherapy have become vital method for disease diagnosis and treatment. Therefore, a craniomaxillofacial surgery robot system was developed to achieve accurate positioning of the puncture needle and automatic surgical operation. Master-salve control and "kinematic + optics" hybrid automatic motion control based on navigation system, which is proposed in order to improve the needle positioning accuracy, were implemented for different processes of the operation. In addition, the kinematic simulation, kinematic parameters identification, positioning accuracy experiment (0.56 ± 0.21 mm), and phantom experiments (1.42 ± 0.33 mm, 1.62 ± 0.26 mm, and 1.41 ± 0.30 mm for biopsy, radiofrequency, and brachytherapy of phantom experiments) were conducted to verify the feasibility of the hybrid automatic control method and evaluate the function of the surgical robot system.

show abstract

Section: Control Strategymentioning

confidence: 99%

Control Strategy and Experiments for Robot Assisted Craniomaxillofacial Surgery System

Cui

Wang

Duan

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Human joint moment prediction is crucial to rehabilitation evaluation [1][2][3], athlete training evaluation [4][5][6], prosthesis and orthosis design [7][8][9], intramedullary device design [10][11][12] and human-robot interaction [13][14][15][16][17][18][19][20][21]. The precise prediction of joint moment can be fulfilled by the use of instrumented implants [22] which measures the relevant parameters of joint load in real time.…”

Section: Introductionmentioning

confidence: 99%

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Xiong

Zeng

et al. 2020

Sensors

View full text Add to dashboard Cite

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

show abstract

“…No obstante, la movilidad de los miembros superiores es fundamental para llevar a cabo actividades de la vida diaria, siendo su rehabilitación fundamental para reducir los niveles de discapacidad del paciente. Es por ello por lo que en losúltimos años se han propuestos diversos dispositivos robóticos para participar en la rehabilitación de las extremidades superiores [1,11,17].…”

Section: Introductionunclassified