Path Control of a Rehabilitation Robot Using Virtual Tunnel and Adaptive Impedance Controller

Interactive control strategies have been widely used in many rehabilitation robotic devices. The distinctive feature of these strategies is that the patient can be encouraged to actively participant in the therapy program. In this paper, a novel adaptive impedance control method, which allows the patient to actively influence the robot movement trajectory, is presented. The control algorithm developed in this paper is capable of regulating the desired impedance according to the patient's actual deviation from the desired path and the dynamic relationship between patients' motion intention and the reference trajectory. A virtual tunnel surrounding the reference trajectory is designed to ensure the patient's range of motion is always physiologically meaningful. The proposed rehabilitation strategy encourages participants to make contributions to rehabilitation training task as much as possible, which may facilitate provoking motor plasticity and motor recovery. Preliminary experiments with several healthy subjects were conducted to evaluate the feasibility and effectiveness of this strategy. Experimental results demonstrated that subjects could successfully finish the tracking task assisted by robot with the proposed control algorithm

Hybrid inertial-manipulator based position tracking system for ultrasound imaging application

In medical field, ultrasound imaging is one of the imaging modalities that needs position tracking system (PTS) in enlarging field of view (FoV) of an image. The enlarged FoV will result easier scanning procedure, and produce more accurate and comprehensive results. To overcome the weakness of commercially available PTSs which suffer from interference and occlusion, many researchers proposed improved PTSs. However, the improved PTSs focused on the portability and compact design, neglecting the vertical scanning aspect which is also important in ultrasound imaging. Hence, this research presents the development of hybrid inertial-manipulator based PTS for 3-dimensional (3D) ultrasound imaging system which capable of measuring the horizontal and vertical scanning movements. The proposed PTS uses the combination of inertial measurement unit sensor and manipulator. The research involves design and evaluation processes for the PTS. Once the design process of the PTS is completed, forward kinematics is calculated using Denavit-Hartenberg conversion. The next step is to evaluate the accuracy and repeatability of the output of the designed PTS by comparing with five sets of reference trajectory of ABB robot. A comparison of the accuracy for the proposed PTS with three other available PTSs is done using the horizontal movement’s error. The experimental results showed high repeatability of position output reading of the designed PTS with standard deviation of 0.27 mm in all different movements and speeds. The proposed PTS is suitable to be used in ultrasound imaging as the error is less than 1.45 mm. Furthermore, the proposed PTS can measure the vertical scanning movement which is neglected in all the previous works, thus fulfilling the main objective of the research

Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training

Robot-assisted rehabilitation offers benefits, such as repetitive, intensive, and task-specific training, as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degrees-of-freedom normalized IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalized IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation

MECHANICAL IMPEDANCE OF ANKLE AS A FUNCTION OF ELECTROMYOGRAPHY SIGNALS OF LOWER LEG MUSCLES USING ARTIFICIAL NEURAL NETWORK

This paper reports on the feasibility of developing a model to describe the nonlinear relationship between the mechanical impedance of the human ankle within a specified range of frequency and the root mean square (RMS) value of the Electromyography (EMG) signals of the muscles of human ankle using Artificial Neural Network (ANN). A lower extremity rehabilitation robot — Anklebot was used to apply pseudo-random mechanical perturbations to the ankle and measure the angular displacement of the ankle to estimate the data of ankle mechanical impedance. Meanwhile, the surface EMG signals from the selected muscles were monitored and recorded using a Delsys Trigno® system. The final ANN models in this paper were created in two degrees of freedom — dorsiflexion-plantarflexion (DP) and inversioneversion (IE) at 3 different muscle activation levels. The results of analysis of the ANN model showed the feasibility of developing models with adequate accuracy and to define the mechanical impedance of the human ankle in terms of lower extremity muscles’ EMG statistical properties