Sivakumar Balasubramanian

Design and Control of RUPERT: A Device for Robotic Upper Extremity Repetitive Therapy

The structural design, control system, and integrated biofeedback for a wearable exoskeletal robot for upper extremity stroke rehabilitation are presented. Assisted with clinical evaluation, designers, engineers, and scientists have built a device for robotic assisted upper extremity repetitive therapy (RUPERT). Intense, repetitive physical rehabilitation has been shown to be beneficial overcoming upper extremity deficits, but the therapy is labor intensive and expensive and difficult to evaluate quantitatively and objectively. The RUPERT is developed to provide a low cost, safe and easy-to-use, robotic-device to assist the patient and therapist to achieve more systematic therapy at home or in the clinic. The RUPERT has four actuated degrees-of-freedom driven by compliant and safe pneumatic muscles (PMs) on the shoulder, elbow, and wrist. They are programmed to actuate the device to extend the arm and move the arm in 3-D space. It is very important to note that gravity is not compensated and the daily tasks are practiced in a natural setting. Because the device is wearable and lightweight to increase portability, it can be worn standing or sitting providing therapy tasks that better mimic activities of daily living. The sensors feed back position and force information for quantitative evaluation of task performance. The device can also provide real-time, objective assessment of functional improvement. We have tested the device on stroke survivors performing two critical activities of daily living (ADL): reaching out and self feeding. The future improvement of the device involves increased degrees-of-freedom and interactive control to adapt to a users physical conditions.

Robot-assisted rehabilitation of hand function.

PURPOSE OF REVIEW Initial work on robot-assisted neurorehabilitation for the upper extremity aimed primarily at training, reaching movements with the proximal sections of the upper extremity. However, recent years have seen a surge in devices dedicated to hand function. This review describes the state of the art and the promises of this novel therapeutic approach. RECENT FINDINGS Numerous robotic devices for hand function with various levels of complexity and functionality have been developed over the last 10 years. These devices range from simple mechanisms that support single joint movements to mechanisms with as many as 18 degrees-of-freedom (DOF) that can support multijoint movements at the wrist and fingers. The results from clinical studies carried out with eight out of 30 reported devices indicate that robot-assisted hand rehabilitation reduces motor impairments of the affected hand and the arm, and improves the functional use of the affected hand. SUMMARY The current evidence in support of the robot-assisted hand rehabilitation is preliminary but very promising, and provides a strong rationale for more systematic investigations in the future.

A Robust and Sensitive Metric for Quantifying Movement Smoothness

The need for movement smoothness quantification to assess motor learning and recovery has resulted in various measures that look at different aspects of a movements profile. This paper first shows that most of the previously published smoothness measures lack validity, consistency, sensitivity, or robustness. It then introduces and evaluates the spectral arc-length metric that uses a movement speed profiles Fourier magnitude spectrum to quantify movement smoothness. This new metric is systematically tested and compared to other smoothness metrics, using experimental data from stroke and healthy subjects as well as simulated movement data. The results indicate that the spectral arc-length metric is a valid and consistent measure of movement smoothness, which is both sensitive to modifications in motor behavior and robust to measurement noise. We hope that the systematic analysis of this paper is a step toward the standardization of the quantitative assessment of movement smoothness.

RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions

The design of a wearable upper extremity therapy robot RUPERT IVtrade (Robotic Upper Extremity Repetitive Trainer) device is presented. It is designed to assist in repetitive therapy tasks related to activities of daily living which has been advocated for being more effective for functional recovery. RUPERTtrade has five actuated degrees of freedom driven by compliant and safe pneumatic muscle actuators (PMA) assisting shoulder elevation, humeral external rotation, elbow extension, forearm supination and wrist/hand extension. The device is designed to extend the arm and move in a 3D space with no gravity compensation, which is a natural setting for practicing day-to-day activities. Because the device is wearable and lightweight, the device is very portable; it can be worn standing or sitting for performing therapy tasks that better mimic activities of daily living. A closed-loop controller combining a PID-based feedback controller and a iterative learning controller (ILC)-based feedforward controller is proposed for RUPERT for passive repetitive task training. This type of control aids in overcoming the highly nonlinear nature of the plant under control, and also helps in adapting easily to different subjects for performing different tasks. The system was tested on two able-bodied subjects to evaluate its performance.

Robotic assessment of upper limb motor function after stroke.

ABSTRACTTraditional assessment of a stroke subject’s motor ability, carried out by a therapist who observes and rates the subject’s motor behavior using ordinal measurements scales, is subjective, time consuming and lacks sensitivity. Rehabilitation robots, which have been the subject of intense inquiry over the last decade, are equipped with sensors that are used to develop objective measures of motor behaviors in a semiautomated way during therapy. This article reviews the current contributions of robot-assisted motor assessment of the upper limb. It summarizes the various measures related to movement performance, the models of motor recovery in stroke subjects and the relationship of robotic measures to standard clinical measures. It analyses the possibilities offered by current robotic assessment techniques and the aspects to address to make robotic assessment a mainstream motor assessment method.

On the analysis of movement smoothness

Quantitative measures of smoothness play an important role in the assessment of sensorimotor impairment and motor learning. Traditionally, movement smoothness has been computed mainly for discrete movements, in particular arm, reaching and circle drawing, using kinematic data. There are currently very few studies investigating smoothness of rhythmic movements, and there is no systematic way of analysing the smoothness of such movements. There is also very little work on the smoothness of other movement related variables such as force, impedance etc. In this context, this paper presents the first step towards a unified framework for the analysis of smoothness of arbitrary movements and using various data. It starts with a systematic definition of movement smoothness and the different factors that influence smoothness, followed by a review of existing methods for quantifying the smoothness of discrete movements. A method is then introduced to analyse the smoothness of rhythmic movements by generalising the techniques developed for discrete movements. We finally propose recommendations for analysing smoothness of any general sensorimotor behaviour.

Robotic Gait Trainer Reliability and Stroke Patient Case Study

With over 600,000 people each year surviving a stroke, it has become the leading cause of serious long-term disability in the United States [1, 2, 3]. Studies have proven that through repetitive task training, neural circuits can be re-mapped thus increasing the mobility of the patient [4, 5, 6, 7, 8]. This fuels the emerging field of rehabilitation robotics. As technology advances new therapy robots are developed that are increasingly compliant and captivating to use. This paper examines the robotic gait trainer (RGT) developed in the human machine integration laboratory at Arizona State University. The RGT is a tripod mechanism, where the patients leg is the fixed link, controlled on a Mat-lab and Simulink platform. An eight week case study was conducted with a 22 year old female stroke survivor. Subjective feedback, robot performance and the patients key performance indicators examined throughout the study are analyzed.

Characterization of the Dynamic Properties of Pneumatic Muscle Actuators

The potential effectiveness and adoption of robotic assisted therapy for neural rehabilitation has attracted increased attention to the development of rehabilitation robots. Our research group had developed a lightweight exoskeletal rehabilitation robot actuated by pneumatic muscle actuators. To design a robust control system with intelligent adaptation to individual patients condition is a challenge with pneumatic muscle actuators (PMA). The dynamics of a PMA is affected by dimension (length and diameter), pressure and load. Thus, it is crucial to choose the appropriate PMA with the desired dynamic response for each of the joints to be actuated. In this study, 2nd order phenomenological models have been developed to describe the dynamic behavior of nine pneumatic muscle actuators (3 different lengths and 3 different diameters). The important differences between these pneumatic muscle actuators are compared based on the model parameters. Some of the model parameters like relative muscle contraction, rise natural frequency are affected more by the PMA dimensions, while some of the other parameters are relatively unaffected by the dimensions of the PMA. In addition, analytical expressions were determined for the individual model parameters as functions of the input pressure and external load.

Robot-measured performance metrics in stroke rehabilitation

One of the useful features of robotic rehabilitation is the possibility of movement quantification, which is currently lacking in conventional rehabilitation therapy. Movement performance measures calculated from this quantitative information serves various purposes - (a) a good supplement to clinical assessment measures, (b) can be more sensitive than many clinical measures which use ordinal scales for scoring, (c) can be used to track a patients recovery over time. Our research group has developed a 5 degree-of-freedom wearable exoskeleton robot for upper-extremity rehabilitation (RUPERT); RUPERT provides movement kinematics information in the form of joint angles, and also provides the pressure inside the pneumatic muscle actuators that drive the robot. In this paper we describe some useful robot-measured performance metrics that can be calculated from the sensor information collected from RUPERT. Some of the important performance-metrics described in this paper are - (a) Amount-of-assistance, (b) Smoothness, and (c) Movement synergy. We present a new method for calculating smoothness, which is uses a very different approach from some of the currently available approaches for calculating smoothness.; we call this approach the ‘spectral method’, which looks at the frequency spectrum of the movement velocity signal to estimate movement smoothness. In addition, we also present a method to analyze the effect of target location and DOF on the performance metrics, and also a method to detect fatigue.

RUPERT closed loop control design

Robot-assisted rehabilitation is an active area of research in the field of stroke rehabilitation. RUPERT is a wearable robotic exoskeleton powered by pneumatic muscle actuators. In this study, we described the structure of the controllers for the five degrees of freedom currently used by RUPERT. We applied the RUPERT on 6 stroke patients to provide robot-assisted rehabilitation therapy in a clinical study. Statistical χ2 test on the proportion of successfully reaching targets showed that 3 out of the 6 patients demonstrated significant improvement in reaching targets successfully, and the remaining 3 did not show performance improvement or deterioration. We plan to implement the RUPERT in the patients house for easier access and more frequent use. More significant performance results are expected.