Ozkan Celik

Systematic review of Kinect applications in elderly care and stroke rehabilitation

AbstractIn this paper we present a review of the most current avenues of research into Kinect-based elderly care and stroke rehabilitation systems to provide an overview of the state of the art, limitations, and issues of concern as well as suggestions for future work in this direction. The central purpose of this review was to collect all relevant study information into one place in order to support and guide current research as well as inform researchers planning to embark on similar studies or applications. The paper is structured into three main sections, each one presenting a review of the literature for a specific topic. Elderly Care section is comprised of two subsections: Fall detection and Fall risk reduction. Stroke Rehabilitation section contains studies grouped under Evaluation of Kinect’s spatial accuracy, and Kinect-based rehabilitation methods. The third section, Serious and exercise games, contains studies that are indirectly related to the first two sections and present a complete system for elderly care or stroke rehabilitation in a Kinect-based game format. Each of the three main sections conclude with a discussion of limitations of Kinect in its respective applications. The paper concludes with overall remarks regarding use of Kinect in elderly care and stroke rehabilitation applications and suggestions for future work. A concise summary with significant findings and subject demographics (when applicable) of each study included in the review is also provided in table format.

Normalized Movement Quality Measures for Therapeutic Robots Strongly Correlate With Clinical Motor Impairment Measures

In this paper, we analyze the correlations between four clinical measures (Fugl-Meyer upper extremity scale, Motor Activity Log, Action Research Arm Test, and Jebsen-Taylor Hand Function Test) and four robotic measures (smoothness of movement, trajectory error, average number of target hits per minute, and mean tangential speed), used to assess motor recovery. Data were gathered as part of a hybrid robotic and traditional upper extremity rehabilitation program for nine stroke patients. Smoothness of movement and trajectory error, temporally and spatially normalized measures of movement quality defined for point-to-point movements, were found to have significant moderate to strong correlations with all four of the clinical measures. The strong correlations suggest that smoothness of movement and trajectory error may be used to compare outcomes of different rehabilitation protocols and devices effectively, provide improved resolution for tracking patient progress compared to only pre- and post-treatment measurements, enable accurate adaptation of therapy based on patient progress, and deliver immediate and useful feedback to the patient and therapist.

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.

Expertise-based performance measures in a virtual training environment

This paper introduces and validates quantitative performance measures for a rhythmic target-hitting task. These performance measures are derived from a detailed analysis of human performance during a month-long training experiment where participants learned to operate a 2-DOF haptic interface in a virtual environment to execute a manual control task. The motivation for the analysis presented in this paper is to determine measures of participant performance that capture the key skills of the task. This analysis of performance indicates that two quantitative measurestrajectory error and input frequencycapture the key skills of the target-hitting task, as the results show a strong correlation between the performance measures and the task objective of maximizing target hits. The performance trends were further explored by grouping the participants based on expertise and examining trends during training in terms of these measures. In future work, these measures will be used as inputs to a haptic guidance scheme that adjusts its control gains based on a real-time assessment of human performance of the task. Such guidance schemes will be incorporated into virtual training environments for humans to develop manual skills for domains such as surgery, physical therapy, and sports.

Application of Levant's differentiator for velocity estimation and increased Z-width in haptic interfaces

In this paper, we present results from implementation of Levants differentiator for velocity estimation from optical encoder readings. Levants differentiator is a sliding mode control theory-based real-time differentiation algorithm proposed as a velocity estimator. The application of the technique allows stable implementation of higher stiffness virtual walls as compared to using the common finite difference method (FDM) cascaded with low-pass filters for velocity estimation. A single degree-of-freedom (DOF) linear haptic device is used as a test bed and an automated virtual wall hitting task is implemented to experimentally demonstrate that it is possible to extend the impedance-width (or Z-width) of a haptic interface via Levants differentiator.

Experimental evaluation of Microsoft Kinect's accuracy and capture rate for stroke rehabilitation applications

To meet the challenges of ubiquitous computing for stroke rehabilitation, researchers have been trying to break away from traditional therapist-based modes of assessment. In this paper, the suitability of the Kinect to this end is experimentally evaluated. A set of thirteen gross movements, derived from common clinical stroke impairment level assessments (Wolf Motion Function Test, Action Research Arm Test, and Fugl-Meyer Assessment) were utilized to explore the Normalized Root Mean Squared Error (NRMSE) in position for data captured by Kinect as compared to a research-grade OptiTrack motion capture system. The specific joints of interest were the shoulder, elbow and wrist. A latency and capture rate estimation of the Kinect and its effects on data quality was also conducted. The NRMSE in position varied between 0.53cm to 1.74cm per data point among all axes and joints on average, when initial calibration was conducted via the OptiTrack system. The mean capture period was measured as 33.3ms with 3.86ms standard deviation, and the latency was observed to be on the order of two capture periods (66.6ms on average). Our results summarize the capabilities as well as limitations of Kinect in gross movement-based impairment assessment, in game-based rehabilitation paradigms, as well as in full-body motion capture applications in general.

Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques

Modeling human operators behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operators model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as they controlled a nonlinear system. Appropriate reference signals and scenarios were designed according to the system identification and human operator modeling theory, to collect data from subjects. Different modeling schemes, namely ARX models as linear approach, and adaptive-network-based fuzzy inference system (ANFIS) as intelligent modeling approach have been evaluated. A hybrid modeling method, fuzzy-ARX (F-ARX) model, has been developed and its performance was found to be better in terms of predicting human operators control actions as well as replacing the operator as a stand-alone controller. It has been concluded that F-ARX models can be a good alternative for modeling the human operator.

Design of Wrist Gimbal: A forearm and wrist exoskeleton for stroke rehabilitation

In this paper, we present design, implementation and specifications of the Wrist Gimbal, a three degree-of-freedom (DOF) exoskeleton developed for forearm and wrist rehabilitation. Wrist Gimbal has three active DOF, corresponding to pronation/supination, flexion/extension and adduction/abduction joints. We mainly focused on a robust, safe and practical device design to facilitate clinical implementation, testing and acceptance. Robustness and mechanical rigidity was achieved by implementing two bearing supports for each of the pronation/supination and adduction/abduction axes. Rubber hard stops for each axis, an emergency stop button and software measures ensured safe operation. An arm rest with padding and straps, a handle with adjustable distal distance and height and a large inner volume contribute to ease of use, of patient attachment and to comfort. We present the specifications of Wrist Gimbal in comparison with similar devices in the literature and example data collected from a healthy subject.

Comparison of robotic and clinical motor function improvement measures for sub-acute stroke patients

In this paper, preliminary results in motor function improvement for four sub-acute stroke patients that underwent a hybrid robotic and traditional rehabilitation program are presented. The therapy program was scheduled for three days a week, four hours per day (approximately 60% traditional constraint induced therapy activities and 40% robotic therapy). A haptic joystick was used to implement four different operating modes for robotic therapy: unassisted (U), constrained (C), assisted (A), and resisted (R) modes. A target hitting task involving the positioning of a pointer on twelve targets was completed by the patients. Two different robotic measures were utilized to quantify the motor function improvement through the sessions: trajectory error (TE) and smoothness of movement (SM). Fugl-Meyer (FM) and motor activity log (MAL) scales were used as clinical measures. Analysis of results showed that the group demonstrates a significant motor function improvement with respect to both clinical and robotic measures. Regression analyses were carried out on corresponding clinical and robotic measure result pairs. A significant relation between FM scale and robotic measures was found for both of the analyzed modes. Regression of robotic measures on MAL scores resulted in no significance. A regression analysis that compared the two clinical measures revealed a very low agreement. Our findings suggest that it might be possible to obtain objective robotic measures that are significantly correlated to widely-used and reliable clinical measures in considerably different operating modes and control schemes.

Impact of visual error augmentation methods on task performance and motor adaptation

We hypothesized that augmenting the visual error feedback provided to subjects training in a point-to-point reaching task under visual distortion would improve the amount and speed of adaptation. Previous studies showing that human learning is error-driven and that visual error augmentation can improve the rate at which subjects decrease their trajectory error in such a task provided the motivation for our study. In a controlled experiment, subjects were required to perform point-to-point reaching movements in the presence of a rotational visual distortion. The amount and speed of their adaptation to this distortion were calculated based on two performance measures: trajectory error and hit time. We tested how three methods of error augmentation (error amplification, traditional error offsetting, and progressive error offsetting) affected the amount and speed of adaptation, and additionally propose definitions for “amount” and “speed” of adaptation in an absolute sense that are more practical than definitions used in previous studies. It is concluded that traditional error offsetting promotes the fastest learning, while error amplification promotes the most complete learning. Progressive error offsetting, a novel method, resulted in slower training than the control group, but we hypothesize that it could be improved with further tuning and indicate a need for further study of this method. These results have implications for improvement in motor skill learning across many fields, including rehabilitation after stroke, surgical training, and teleoperation.