Ali Utku Pehlivan

Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy

Stroke is one of the leading causes of long-term disability today; therefore, many research efforts are focused on designing maximally effective and efficient treatment methods. In particular, robotic stroke rehabilitation has received significant attention for upper-limb therapy due to its ability to provide high-intensity repetitive movement therapy with less effort than would be required for traditional methods. Recent research has focused on increasing patient engagement in therapy, which has been shown to be important for inducing neural plasticity to facilitate recovery. Robotic therapy devices enable unique methods for promoting patient engagement by providing assistance only as needed and by detecting patient movement intent to drive to the device. Use of these methods has demonstrated improvements in functional outcomes, but careful comparisons between methods remain to be done. Future work should include controlled clinical trials and comparisons of effectiveness of different methods for patients with different abilities and needs in order to inform future development of patient-specific therapeutic protocols.

ROBOTIC TRAINING AND ClINICAl ASSESSMENT OF UPPER ExTREMITy MOvEMENTS AFTER SPINAl CORD INJURy: A SINGlE CASE REPORT

CASE REPORT A 28-year-old woman, with incomplete spinal cord injury at the C2 level, classified as American Spinal Injury Impairment Scale C (AIS), participated in a robotic rehabilitation program 29 months after injury. Robotic training was provided to both upper extremities using the MAHI Exo-II, an exoskeleton device designed for rehabilitation of the upper limb, for 12 × 3-h sessions over 4 weeks. Training involved elbow flexion/extension, forearm supination/pronation, wrist flexion/extension, and radial/ulnar deviation. RESULTS Outcome measures were Action Research Arm Test, Jebsen-Taylor Hand Function Test, and AIS-upper extremity motor score. Safety measures included fatigue, pain and discomfort level using a 5-point rating scale. Following training, improvements were observed in the left arm and hand function, whereas the right arm and hand function showed no improvement in any of the functional outcome measures. No excessive pain, discomfort or fatigue was reported. CONCLUSION Data from one subject demonstrate valuable information on the feasibility, safety and effectiveness of robotic-assisted training of upper-extremity motor functions after incomplete spinal cord injury.

Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation

Robotic rehabilitation has gained significant traction in recent years, due to the clinical demonstration of its efficacy in restoring function for upper extremity movements and locomotor skills, demonstrated primarily in stroke populations. In this paper, we present the design of MAHI Exo II, a robotic exoskeleton for the rehabilitation of upper extremity after stroke, spinal cord injury, or other brain injuries. The five degree-of-freedom robot enables elbow flexion-extension, forearm pronation-supination, wrist flexion-extension, and radial-ulnar deviation. The device offers several significant design improvements compared to its predecessor, MAHI Exo I. Specifically, issues with backlash and singularities in the wrist mechanism have been resolved, torque output has been increased in the forearm and elbow joints, a passive degree of freedom has been added to allow shoulder abduction thereby improving alignment especially for users who are wheelchair-bound, and the hardware now enables simplified and fast swapping of treatment side. These modifications are discussed in the paper, and results for the range of motion and maximum torque output capabilities of the new design and its predecessor are presented. The efficacy of the MAHI Exo II will soon be validated in a series of clinical evaluations with both stroke and spinal cord injury patients.

Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury

SUMMARY Robotic devices are well-suited to provide high intensity upper limb therapy in order to induce plasticity and facilitate recovery from brain and spinal cord injury. In order to realise gains in functional independence, devices that target the distal joints of the arm are necessary. Further, the robotic device must exhibit key dynamic properties that enable both high dynamic transparency for assessment, and implementation of novel interaction control modes that significantly engage the participant. In this paper, we present the kinematic design, dynamical characterization, and clinical validation of the RiceWrist-S, a serial robotic mechanism that facilitates rehabilitation of the forearm in pronation-supination, and of the wrist in flexion-extension and radial-ulnar deviation. The RiceWrist-Grip, a grip force sensing handle, is shown to provide grip force measurements that correlate well with those acquired from a hand dynamometer. Clinical validation via a single case study of incomplete spinal cord injury rehabilitation for an individual with injury at the C3-5 level showed moderate gains in clinical outcome measures. Robotic measures of movement smoothness also captured gains, supporting our hypothesis that intensive upper limb rehabilitation with the RiceWrist-S would show beneficial outcomes.

A Subject-Adaptive Controller for Wrist Robotic Rehabilitation

In order to derive maximum benefit from robot-assisted rehabilitation, it is critical that the implemented control algorithms promote the participants active engagement in therapy. Assist-as-needed (AAN) controllers address this need by providing only appropriate assistance during movement execution. Often, these controllers depend on the definition of an optimal movement profile, against which the participants movements are compared. In this paper, we present a novel subject-adaptive controller, consisting of two main components: AAN control algorithm and online trajectory recalculation. First, the AAN control algorithm is based on an adaptive controller and introduces a novel feedback gain modification algorithm. Coupled with the uniformly ultimately bounded stability property of the resulting dynamic system, the developed controller is capable of changing the amount of error allowed during movement execution, while simultaneously estimating the forces provided by the participant that contribute to movement execution. Second, we present a real-time trajectory generation algorithm based on a physiologically optimal and experimentally validated asymmetric wrist movement profile. The feedback gain modification and trajectory generation algorithms are validated with the RiceWrist system in an experimental study involving five healthy subjects, with the modified AAN adaptive controller decreasing its feedback control action when a subject shifts his behavior from passively riding along with the robot during movement to actively engaging and initiating movements to the desired on-screen targets.

Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation

Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.

A robotic exoskeleton for rehabilitation and assessment of the upper limb following incomplete spinal cord injury

Robotic devices have been shown to be efficacious in the delivery of therapy to treat upper limb motor impairment following stroke. However, the application of this technology to other types of neurological injury has been limited to case studies. In this paper, we present a multi degree of freedom robotic exoskeleton, the MAHI Exo II, intended for rehabilitation of the upper limb following incomplete spinal cord injury (SCI). We present details about the MAHI Exo II and initial findings from a clinical evaluation of the device with eight subjects with incomplete SCI who completed a multi-session training protocol. Clinical assessments show significant gains when comparing pre- and post-training performance in functional tasks. This paper explores a range of robotic measures capturing movement quality and smoothness that may be useful in tracking performance, providing as feedback to the subject, or incorporating into an adaptive training protocol. Advantages and disadvantages of the various investigated measures are discussed with regard to the type of movement segmentation that can be applied to the data collected during unassisted movements where the robot is backdriven and encoder data is recorded for post-processing.

System characterization of RiceWrist-S: A forearm-wrist exoskeleton for upper extremity rehabilitation

Rehabilitation of the distal joints of the upper extremities is crucial to restore the ability to perform activities of daily living to patients with neurological lesions resulting from stroke or spinal cord injury. Robotic rehabilitation has been identified as a promising new solution, however, much of the existing technology in this field is focused on the more proximal joints of the upper arm. A recently presented device, the RiceWrist-S, focuses on the rehabilitation of the forearm and wrist, and has undergone a few important design changes. This paper first addresses the design improvements achieved in the recent design iteration, and then presents the system characterization of the new device. We show that the RiceWrist-S has capabilities beyond other existing devices, and exhibits favorable system characteristics as a rehabilitation device, in particular torque output, range of motion, closed loop position performance, and high spatial resolution.

Robotic training and kinematic analysis of arm and hand after incomplete spinal cord injury: A case study

Regaining upper extremity function is the primary concern of persons with tetraplegia caused by spinal cord injury (SCI). Robotic rehabilitation has been inadequately tested and underutilized in rehabilitation of the upper extremity in the SCI population. Given the acceptance of robotic training in stroke rehabilitation and SCI gait training, coupled with recent evidence that the spinal cord, like the brain, demonstrates plasticity that can be catalyzed by repetitive movement training such as that available with robotic devices, it is probable that robotic upper-extremity training of persons with SCI could be clinically beneficial. The primary goal of this pilot study was to test the feasibility of using a novel robotic device for the upper extremity (RiceWrist) and to evaluate robotic rehabilitation using the RiceWrist in a tetraplegic person with incomplete SCI. A 24-year-old male with incomplete SCI participated in 10 sessions of robot-assisted therapy involving intensive upper limb training. The subject successfully completed all training sessions and showed improvements in movement smoothness, as well as in the hand function. Results from this study provide valuable information for further developments of robotic devices for upper limb rehabilitation in persons with SCI.

Adaptive control of a serial-in-parallel robotic rehabilitation device

Robotic rehabilitation is an effective platform for sensorimotor training after neurological injuries. In this paper, an adaptive controller is developed and implemented for the RiceWrist, a serial-in-parallel robot mechanism for upper extremity robotic rehabilitation. The model-based adaptive controller implementation requires a closed form dynamic model, valid for a restricted domain of generalized coordinates. We have used an existing method to define this domain and verify that the domain is widely within the range of admissible tasks required for the considered application (movements-based wrist and forearm rehabilitation). Simulation and experimental results that compare the performance of the adaptive controller to a proportional-derivative controller show that the trajectory tracking performance of the adaptive controller is better compared to the performance of a PD controller using the same values of feed-back gains. Further, comparable absolute error performance is obtained with the adaptive controller for feedback gains nearly one third that required for the PD controller. With the lower gains used in the adaptive controller, good tracking performance is achieved with a more compliant controller that will allow the subject to indicate their ability to independently initiate and maintain movement during a rehabilitation session.