EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot.

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. Therefore, it is essential to establish a correlation between the patient's muscle condition and the rehabilitation exercises to improve the physiotherapy sessions. A self-adapting control method based on online fatigue detection in rhythmic arm movements is presented. Experimental tests were performed on twenty healthy subjects to validate the method's feasibility. Electromyography (EMG) and force signals considered the interfaces between users and the robot. In the first stage of the experiment, utilizing the frequency features from EMG signals, a neural network for fatigue detection trained; however, in the end, it substituted with a simple function as a refinement in the time-consuming aspect for the online employment. The initiation of the fatigue process is followed by reducing the admittance controller damping term based on the EMG signal processing. Trajectory tracking with the robot employs the self-adapting admittance controller (SAAC) method and the non-adapting admittance controller (NAAC). Movement accuracy and smoothness were measured and showed a better performance of the SAAC method related to the NAAC. Simulations with two different stiffness levels were performed on an upper-limb OpenSim model to study a stroke-injured arm and evaluate the proposed method's proficiency. The metabolic cost indicated the movement's superiority in a fatigue situation for reduced environment stiffness.

Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot interaction.

A nonlinear Time Delay Estimation (TDE) based model reference adaptive impedance controller was developed for Tarbiat Modares University Upper Limbs Rehabilitation Robot (TUERR). The proposed controller uses a stable reference impedance model, which produces desired dynamic relationship between applied force and position error for the robot End-effector to track the desired trajectory. TDE based model reference adaptive controller estimates unknown system dynamics and uncertainties, and the adaption law modifies the controller gains. Using a Lyapunov function was shown trajectory tracking errors in the overall system are bounded. In addition, a performance-based velocity profile proposed to modify the pace of trajectory planning considering the deviation from the desired path. Finally, the performance of the presented controller and rehabilitation process is experimentally investigated for TUERR.

A novel zero delay low pass filter: Application to precision positioning systems.

In this paper, a nonlinear low-pass filter is presented, which produces significantly less phase lag than linear and some nonlinear filters. The proposed filter employs a saturation function to enhance the linear filter's performance. The gain and phase responses of the filter are derived analytically using a modified describing function, and the efficiency of the proposed method is examined through numerical examples. Based on the required cut-off frequency and noise to signal ratio, a rule of thumb is given to set the filter's parameters. In the frequency domain, simulation results show that the filter's gain response is near 0dB in the pass-band, and the noise attenuation rate is -40dB∕dec, while the phase lag is three times lesser compared to 2nd order Butterworth low-pass filter. Moreover, comparing with Jin et al.'s parabolic sliding mode filter and feed-forwarded parabolic sliding mode filter the gain and phase of the proposed filter are closer to zero in the pass-band and before cut-off frequency. Furthermore, the filter's performance is also evaluated in case of different noise color and concluded that the proposed filter is superior to linear and nonlinear filters in case of white, blue, or purple noise Finally, the filter's effectiveness and the tuning guideline are validated by simulating a precision motion control system in the discrete-time domain.

A Poincare map based analysis of stroke patients' walking after a rehabilitation by a robot.

Since the past decade, rehabilitation robots have become common technologies for recovering gait ability after a stroke. Nevertheless, it is believed that these robots can be further enhanced. Hence, several researches are making progress in optimizing gait rehabilitation robots. However, most of these researches have only assessed the robots and their controllers in improving spatiotemporal and kinetic features of walking. There are not many researchers have focused on the robots' controllers' effects on the central nervous or neuromuscular systems. On the other hand, recently computational methods have been utilized to investigate the rehabilitations of neural disorders, through developing neuromechanical models. However, these methods have neither studied the robot-assisted gait rehabilitation, nor have they theoretically proved why rehabilitation exercises enhance patients' walking ability. Therefore, this paper merged a theoretical approach into a computational method to investigate the effects of gait rehabilitation robots on post-stroke neuromuscular system. To this end, a neuromechanical model of gait has been developed and thereby, the Poincare maps of intact and stroke people have been obtained. Comparison of these maps revealed why a stroke reduces the stability of walking. Then, the effect of an impedance controller, which is used in a rehabilitative robot, is scrutinized in stabilizing a walking motion. Obtaining the Poincare map of this close-loop system, proved that this controller improves motion stability. Finally, the effect of this controller is investigated by simulations and experiments. The experimental tests are performed by Arman rehabilitative robot. Clinical Reference Number: IR.TMU.REC.1394.254.