Guoli Zhu

Reconfigurable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR)

Abstract The novelty of this paper is the adjustable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR). The robot has three rotational degrees of freedom (DOFs) redundantly actuated by four compliant actuators. It suffers from conflicting workspace and actuation torque due to the use of a parallel mechanism and compliant actuators. To address this issue, also considering physical constraints imposed by human users, the CARR is designed with reconfigurability to make a trade-off between workspace and torque capacity for meeting different training requirements. Theoretical analysis indicates that varying kinematic and dynamic performance of the robot can be achieved by reconfiguring the layout of the actuators. Experiments with/without load also demonstrate the validity of the reconfigurable robotic design for practical applications.

A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

Objective This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility. Method This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation. Result All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5u2009bits/min when the biggest one of the robots for this proposed training is set as 24u2009bits/min. Conclusion The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.

Robot-assisted ankle rehabilitation for the treatment of drop foot: A case study

This paper involves the use of an intrinsically-compliant ankle rehabilitation robot for the treatment of drop foot. The robot has a bio-inspired design by employing four Festo fluidic actuators that mimic skeletal muscles to actuate three rotational degrees of freedom (DOFs). A position controller in task space was developed to track the predefined trajectory of the end effector. The position tracking was achieved by the length tracking of each actuator in joint space by inverse kinematics. A stroke patient with drop foot participated in the trial as a case study to evaluate the potential of this robot for clinical applications. The patient gave positive feedback in using the ankle robot for the treatment of drop foot, although some limitations exist. The trajectory tracking showed satisfactory accuracy throughout the whole training with varying ranges of motion, with the root mean square deviation (RMSD) value being 0.0408 rad and the normalized root mean square deviation (NRMSD) value being 8.16%. To summarize, preliminary findings support the potential of the ankle rehabilitation robot for clinical applications. Future work will investigate the effectiveness of the robot for treating drop foot on a large sample of subjects.

Synchronization control of dual-drive system in gantry-type machine tools based on disturbance observer

Precise synchronization motion control has always been a problem need to be solved in the gantry-type NC machine tools (GNCMT) with a dual-drive system. The complexity of the system puts forward a great challenge for the setting of the PID controller parameters. In addition, one of the key elements which seriously affects the synchronization precision of the dual-drive system is disturbance. To realize the precise synchronization control, a mathematical model of the dual-drive system which combined with the mechanical coupling model of the gantry mechanism and the model of transmission system of each axis was presented firstly. And then, model parameters were identified based on Least Square(LS) method. In order to reduce the effects of friction and eccentric disturbance on the synchronization performance and improve the anti-interference performance of the system, a synchronization control strategy based on disturbance observer (DOB) which used the identified model as the nominal model was proposed. Finally, a simulation analysis was carried out. The simulation results showed that the synchronization performance of dual-drive system in GNCMT is obviously improved through the disturbance compensation.

Reviewing Clinical Effectiveness of Active Training Strategies of Platform-Based Ankle Rehabilitation Robots

Objective This review aims to provide a systematical investigation of clinical effectiveness of active training strategies applied in platform-based ankle robots. Method English-language studies published from Jan 1980 to Aug 2017 were searched from four databases using key words of “Ankle∗” AND “Robot∗” AND “Effect∗ OR Improv∗ OR Increas∗.” Following an initial screening, three rounds of discrimination were successively conducted based on the title, the abstract, and the full paper. Result A total of 21 studies were selected with 311 patients involved; of them, 13 studies applied a single group while another eight studies used different groups for comparison to verify the therapeutic effect. Virtual-reality (VR) game training was applied in 19 studies, while two studies used proprioceptive neuromuscular facilitation (PNF) training. Conclusion Active training techniques delivered by platform ankle rehabilitation robots have been demonstrated with great potential for clinical applications. Training strategies are mostly combined with one another by considering rehabilitation schemes and motion ability of ankle joints. VR game environment has been commonly used with active ankle training. Bioelectrical signals integrated with VR game training can implement intelligent identification of movement intention and assessment. These further provide the foundation for advanced interactive training strategies that can lead to enhanced training safety and confidence for patients and better treatment efficacy.

Advanced geological detection instrument for TBM tunneling based on AC induced polarization method

Currently, there are some methods for geological prediction method in tunneling using tunnel boring machine (TBM). The Bore-tunnel Electrical Ahead Monitoring (BEAM) is popular in the advanced detection based on the current method of electrical prospecting. However, the drawbacks of BEAM in the cost and complexity limit the application for geological prediction. This paper proposes a novel detection instrument for advanced geological prediction based on the characteristics of BEAM system. The instrument is implemented by the hardware voltage parallel method, which can realize the high speed and high precision measurement of the geological apparent resistivity and dispersion rate in dynamic tunneling process. It selects several cutting head of TBM as the main electrodes, which are set at two kinds of electrode distribution modes. It can achieve good detection results by switching between electrode distribution modes and scanning modes which is available by the rotating of working face. Experiments have been carried out to test for TBM experiment model in the proposed instrument. The results of experiment verify the feasibility and effectiveness of the proposed detection instrument.

An advanced detection approach based on support vector machine during tunnelling

A new tunnel advanced detection method is proposed using disc cutter of tunnel boring machine(TBM) as a center electrode with DC resistivity principle, and unfavorable geology front or side of tunnel face is predicted. The numerical simulation of electrical resistivity is carried out using finite element method, meanwhile, the electric field distribution is calculated and discussed about abnormal characteristics under different ground conditions. Then the classifier based on support vector machine(SVM) algorithm is built to differentiate the position of abnormal geology body: front or side of tunnel face. The K-cross validation is used to choose the optimal parameters of SVM. According to the results, it can be said that the proposed method is useful and reliable means to predict the position of anomaly and provide the reference for site geological prediction.