Ahmetcan Erdogan

Design of a reconfigurable ankle rehabilitation robot and its use for the estimation of the ankle impedance

This paper presents the design, analysis, and a clinical application of a reconfigurable, parallel mechanism based, force feedback exoskeleton for the human ankle. The device can either be employed as a balance/proprioception trainer or configured to accommodate range of motion (RoM)/strengthening exercises. The exoskeleton can be utilized as a clinical measurement tool to estimate dynamic parameters of the ankle and to assess ankle joint properties in physiological and pathological conditions. Kinematic analysis and control of the device are detailed and a protocol for utilization of the exoskeleton to determine ankle impedance is discussed. The prototype of the device is also presented.

Passive velocity field control of a forearm-wrist rehabilitation robot

This paper presents design, implementation and control of a 3RPS-R exoskeleton, specifically built to impose targeted therapeutic exercises to forearm and wrist. Design of the exoskeleton features enhanced ergonomy, enlarged workspace and optimized device performance when compared to previous versions of the device. Passive velocity field control (PVFC) is implemented at the task space of the manipulator to provide assistance to the patients, such that the exoskeleton follows a desired velocity field asymptotically while maintaining passivity with respect to external applied torque inputs. PVFC is augmented with virtual tunnels and resulting control architecture is integrated into a virtual flight simulator with force-feedback. Experimental results are presented indicating the applicability and effectiveness of using PVFC on 3RPS-R exoskeleton to deliver therapeutic movement exercises.

Causal reasoning for planning and coordination of multiple housekeeping robots

We consider a housekeeping domain with multiple cleaning robots and represent it in the action language C+. With such a formalization of the domain, a plan can be computed using the causal reasoner CCALC for each robot to tidy some part of the house. However, to find a plan that characterizes a feasible trajectory that does not collide with obstacles, we need to consider geometric reasoning as well. For that, we embed motion planning in the domain description using external predicates. For safe execution of feasible plans, we introduce a planning and monitoring algorithm so that the robots can recover from plan execution failures due to heavy objects that cannot be lifted alone. The coordination of robots to help each other is considered for such a recovery. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain.

Brain Computer Interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies.

AssistOn-Mobile : a series elastic holonomic mobile platform for upper extremity rehabilitation

We present the design and control of series elastic holonomic mobile platform, ASSISTON-MOBILE, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device for home use. ASSISTON-MOBILE consists of four actuated Mecanum wheels and a compliant, low-cost, multi degree-of-freedom Series Elastic Element as its force sensing unit. Thanks to its series elastic actuation, ASSISTON-MOBILE is highly back-driveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. Feasibility tests and preliminary usability studies with the robot are presented. The device holds promise in improving accuracy and effectiveness of repetitive movement therapies completed at home, while also providing quantitative measures of patient progress.

Slacking prevention during assistive contour following tasks with guaranteed coupled stability

Passive velocity field control is advantageous to deliver human-in-the-loop contour tracking rehabilitation exercises, since patients can be allowed to proceed with their preferred pace, while assistance can still be provided as determined by the therapist with ensured coupled stability. We introduce a framework based on passive velocity field control for robot assisted rehabilitation that includes prevention mechanisms against undesired slacking behavior of patients. This framework not only provides systematic approaches to prevent slacking, but also can do so while ensuring coupled stability of the overall robot patient system, a property that cannot be assured with any of the other ad-hoc slacking prevention methods. In particular, the proposed approach enables seamless on-line modification of the task difficulty, speed of contour following, and the level of assistance, while preserving passivity of the system with respect to external forces. The proposed slacking prevention schemes encourage active participation of the patients in rehabilitation protocols with even increased number of repetitions and thanks to flexibility introduced by the controller, render delivery of “repetitive tasks without repeating the same task” possible. Experiments with an haptic interface are included to demonstrate the passivity of the proposed control framework and preliminary human subject experiments with healthy volunteers are presented to validate feasibility and usability of the proposed approaches.

Assist On-Ankle: a reconfigurable ankle exoskeleton with series-elastic actuation

We present the kinematics, optimal dimensional synthesis, series-elastic actuation, control, characterization and user evaluation of AssistOn-Ankle, a reconfigurable, powered exoskeleton for ankle rehabilitation. AssistOn-Ankle features reconfigurable kinematics for delivery of both range of motion (RoM)/strengthening and balance/proprioception exercises. In particular, through lockable joints, the underlying kinematics can be configured to either a self-aligning parallel mechanism that can naturally cover the whole RoM of the human ankle, or another parallel mechanism that can support the ground reaction forces/torques transferred to the ankle. Utilizing a single device to treat multiple phases of treatment is advantageous for robotic rehabilitation, since not only does it decrease the device cost and help with the space requirements, but also shorten the time it takes for patients to familiarize with the device. Bowden cable-based series-elastic actuation of AssistOn-Ankle allows for a remote placement of the motors/drivers to result in a compact design with low apparent inertia, while also enabling high-fidelity force/impedance control and active backdriveability of the device.

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented.

Online generation of velocity fields for passive contour following

A new approach to online generation of velocity fields for parametric curves is presented for implementation in passive velocity field controllers (PVFC) that enable human-in-the-loop contour following tasks. In particular, a feedback stabilized closest point tracking algorithm is utilized for real-time determination of the contour error and online construction of the velocity field. The algorithm augments the system dynamics with a new state, and implements a uniformly asymptotically stable controller to update this new state to continual track the closest point to the robot end-effector. Thanks to its feedback-stabilized core, the algorithm is immune to initialization errors, and robust against drift and numerical noise. Furthermore, requiring simple evaluations of the curve and its unit tangents, the approach is computationally efficient. Applicability and effectiveness of the approach to implement passive contour following tasks have been demonstrated through simulations and experiments with a two degrees-of-freedom haptic interface.

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.