Mustafa Yalçın

AssistOn-Knee: A self-aligning knee exoskeleton

We present kinematics, actuation, detailed design, characterization results and initial user evaluations of AssistOn-Knee, a novel self-aligning active exoskeleton for robot-assisted knee rehabilitation. AssistOn-Knee can, not only assist flexion/extension movements of the knee joint but also accommodate its translational movements in the sagittal plane. Automatically aligning its joint axes, AssistOn-Knee enables an ideal match between human knee axis and the exoskeleton axis, guaranteeing ergonomy and comfort throughout the therapy. Self-aligning feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time spent on exercises. The proposed exoskeleton actively controls the rotational degree of freedom of the knee through a Bowden cable-driven series elastic actuator, while the translational movements of the knee joints are passively accommodated through use of a 3 degrees of freedom planar parallel mechanism. AssistOn-Knee possesses a lightweight and compact design with significantly low apparent inertia, thanks to its Bowden cable based transmission that allows remote location of the actuator and reduction unit. Furthermore, thanks to its series-elastic actuation, AssistOn-Knee enables high-fidelity force control and active backdrive-ability within its control bandwidth, while featuring passive elasticity for excitations above this bandwidth, ensuring safety and robustness throughout the whole frequency spectrum.

VnSA: Variable negative stiffness actuation based on nonlinear deflection characteristics of buckling beams

We present variable negative stiffness actuation (VnSA), an alternative method of achieving variable stiffness actuation based on the nonlinear deflection characteristics of buckling beams. The approach exploits transverse stiffness variations of axially loaded beams around their critical buckling load to achieve an actuator with adjustable stiffness. In particular, transverse stiffness of buckled beams are positive under tensile loading and for compressive loading below their first critical buckling load, while they display negative stiffness above this critical value. Furthermore, for small deflections transverse stiffness of buckled beams depends linearly on the amount of axial loading. Consequently, the stiffness of a variable stiffness actuator can be modulated (i) by decreasing the transverse stiffness through an increase of the axial compressive loading on a beam, up to values above the first critical buckling load where the overall stiffness of the actuator approaches its lowest negative value, and (ii) by increasing the transverse stiffness through application of tensile axial loading. Capitalizing on the concept of negative stiffness, the lowest stiffness of VnSA can be set arbitrarily close to zero or even to negative values (when counterbalanced), while very high stiffness values are also achievable by tensile loading of the beam. As a result, VnSA can modulate its stiffness over a uniquely large range that includes zero and negative stiffness values. Furthermore, thanks to the negative stiffness characteristics, the stiffness of VnSA can be kept very low without sacrificing the mechanical integrity and load bearing capacity of the actuator. We introduce the design of VnSA, theoretically analyze its stiffness modulation response, and provide implementation details of a prototype. We also provide experimental results detailing range of stiffness modulation and force tracking performance achieved with this prototype and discuss its correspondence with the theory.

Electroencephalographic identifiers of motor adaptation learning

OBJECTIVE Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. APPROACH Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects. MAIN RESULTS Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates. SIGNIFICANCE Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.

AssistOn-Gait: An overground gait trainer with an active pelvis-hip exoskeleton

We propose AssistOn-Gait, a robot-assisted gait and balance trainer, for restoration and improvement of gait and balance of patients with disabilities affecting their lower extremities. In addition to overground gait and balance training, pelvis-hip exercises aimed to correct compensatory movements arising from abnormal gait patterns can be delivered with AssistOn-Gait, extending the type of therapies that can be administered using lower extremity exoskeletons. AssistOn-Gait consists of an active, self-aligning pelvis-hip exoskeleton supported by a series elastic holonomic mobile platform. The series elastic mobile platform enables compensation of the robot dynamics for transparent operation, while also allowing patients to start/stop motion, vary their speed, sidestep to maintain balance and turn to change walking direction. The pelvis-hip exoskeleton can independently actuate 6 degrees of freedom: the hip rotations in the sagittal plane, the pelvic rotation in the transverse plane, the pelvic tilt in the coronal plane, the lateral and the vertical pelvic displacements. Thanks to its backdriveable design, automatically adjusting the center of rotation of its joint axes, the self-aligning exoskeleton enables an ideal match between the hip rotation axes and the device axes, and can do so while allowing for the natural pelvic movements during walking. This feature not only guarantees ergonomy but also extends the usable range of motion of the hip joints.

Pre-movement contralateral EEG low beta power is modulated with motor adaptation learning

Various neuroimaging studies aim to understand the complex nature of human motor behavior. There exists a variety of experimental approaches to study neurophysiological correlates of performance during different motor tasks. As distinct from studies based on visuomotor learning, we investigate changes in electroencephalographic (EEG) activity during an actual physical motor adaptation learning experiment. Based on statistical analysis of EEG signals collected during a force-field adaptation task performed with the dominant hand, we observe a modulation of pre-movement upper alpha (10–12 Hz) and lower beta (13–16 Hz) powers over the contralateral region. This modulation is observed to be stronger in lower beta range and, through a regression analysis, is shown to be related with motor adaptation performance on a subject-specific level.

Redundant kinematics and workspace centering control of AssistOn-Gait overground gait and balance trainer

We present the redundant kinematics and workspace centering control of AssistOn-Gait, an overground gait and balance trainer designed to deliver pelvis-hip exercises to correct compensatory movements arising from abnormal gait patterns. AssistOn-Gait consists of an impedance controlled pelvis-hip exoskeleton module, supported by a motion controlled holonomic mobile platform. The exoskeleton module possesses 7 active degrees of freedom to independently control the rotation of the each hip in the sagittal plane along with the pelvic tilt, pelvic rotation and the horizontal, vertical and lateral displacements of the pelvis. The holonomic mobile base can track the movements of patients on flat surfaces, allowing patients to walk naturally, start/stop motion, vary their speed, sidestep to maintain balance and turn to change their walking direction. The kinematics of AssistOn-Gait is redundant, as the exoskeleton module spans all the degrees of freedom covered by the mobile platform. The device features dual layer actuation, since the exoskeleton module is designed for force control with good transparency, while the mobile base is designed for motion control to carry the weight of the patient and the exoskeleton. The kinematically redundant dual layer actuation enables the mobile base of the system to be controlled using workspace centering control strategy without the need for any additional sensors, since the patient movements are readily measured by the exoskeleton module. The workspace centering controller ensures that the workspace limits of the exoskeleton module are not reached, decoupling the dynamics of the mobile base from the exoskeleton dynamics. Consequently, AssistOn-Gait possesses virtually unlimited workspace, while featuring the same output impedance and force rendering performance as its exoskeleton module

A six degrees of freedom haptic interface for laparoscopic training

We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.

Correlations of Motor Adaptation Learning and Modulation of Resting-State Sensorimotor EEG Activity

There exists a variety of electroencephalogram (EEG) based brain-computer interface (BCI) assisted stroke rehabilitation protocols which exploit the recognized nature of sensorimotor rhythms (SMRs) during motor movements. For novel approaches independent of motor execution, we investigate the changes in resting-state sensorimotor EEG with motor learning, resembling the process of post-stroke recovery. In contrast to the neuroimaging studies based on visuomotor tasks, we study motor learning during an actual physical motor adaptation learning experiment. Based on analysis of EEG data collected throughout a force-field adaptation task, we observed a spectral power increase of resting SMRs across subjects. The modulation across restingstates in an early adaptation phase of the motor task was further shown to predict individual motor adaptation performance measures.