Pneumatic robotic systems for upper limb rehabilitation

The aim of rehabilitation robotic area is to research on the application of robotic devices to therapeutic procedures. The goal is to achieve the best possible motor, cognitive and functional recovery for people with impairments following various diseases. Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. This article first reviews the current state-of-art in rehabilitation robotic devices with pneumatic actuation systems reporting main features and control issues of each therapeutic device. Then, a new pneumatic rehabilitation robot for proprioceptive neuromuscular facilitation therapies and for relearning daily living skills: like taking a glass, drinking, and placing object on shelves is described as a case study and compared with the current pneumatic rehabilitation devices

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion

Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking. This distance metric reflects some basic gait features of healthy walking and helps us quickly eliminate a majority of unstable controllers. With our approach we can learn policies for walking in less than 100 trials for a range of challenging settings. In simulation, we show results on two different costs and on various terrains including rough ground and ramps, sloping upwards and downwards. We also perturb our models with unknown inertial disturbances analogous with differences between simulation and hardware. These results are promising, as they indicate that this method can potentially be used to learn control policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids '2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally

Submovements During Reaching Movements after Stroke

Neurological deficits after cerebrovascular accidents very frequently disrupt the kinematics of voluntary movements with the consequent impact in daily life activities. Robotic methodologies enable the quantitative characterization of specific control deficits needed to understand the basis of functional impairments and to design effective rehabilitation therapies. In a group of right handed chronic stroke survivors (SS) with right side hemiparesis, intact proprioception, and differing levels of motor impairment, we used a robotic manipulandum to study right arm function during discrete point-to-point reaching movements and reciprocal out-and-back movements to visual targets. We compared these movements with those of neurologically intact individuals (NI). We analyzed the presence of secondary submovements in the initial (i.e. outward) trajectory portion of the two tasks and found that the SS with severe impairment (F

Assistive robotic device: evaluation of intelligent algorithms

Assistive robotic devices can be used to help people with upper body disabilities gaining more autonomy in their daily life. Although basic motions such as positioning and orienting an assistive robot gripper in space allow performance of many tasks, it might be time consuming and tedious to perform more complex tasks. To overcome these difficulties, improvements can be implemented at different levels, such as mechanical design, control interfaces and intelligent control algorithms. In order to guide the design of solutions, it is important to assess the impact and potential of different innovations. This paper thus presents the evaluation of three intelligent algorithms aiming to improve the performance of the JACO robotic arm (Kinova Robotics). The evaluated algorithms are 'preset position', 'fluidity filter' and 'drinking mode'. The algorithm evaluation was performed with 14 motorized wheelchair's users and showed a statistically significant improvement of the robot's performance.Comment: 4 page

A Quantitative and Standardized Robotic Method for the Evaluation of Arm Proprioception After Stroke

Stroke often results in both motor and sensory deficits, which may interact in the manifested functional impairment. Proprioception is known to play important roles in the planning and control of limb posture and movement; however, the impact of proprioceptive deficits on motor function has been difficult to elucidate due in part to the qualitative nature of available clinical tests. We present a quantitative and standardized method for evaluating proprioception in tasks directly relevant to those used to assess motor function. Using a robotic manipulandum that exerted controlled displacements of the hand, stroke participants were evaluated, and compared with a control group, in their ability to detect such displacements in a 2-alternative, forced-choice paradigm. A psychometric function parameterized the decision process underlying the detection of the hand displacements. The shape of this function was determined by a signal detection threshold and by the variability of the response about this threshold. Our automatic procedure differentiates between participants with and without proprioceptive deficits and quantifies functional proprioceptive sensation on a magnitude scale that is meaningful for ongoing studies of degraded motor function in comparable horizontal movements

Design and Control of Robotic Systems for Lower Limb Stroke Rehabilitation

Lower extremity stroke rehabilitation exhausts considerable health care resources, is labor intensive, and provides mostly qualitative metrics of patient recovery. To overcome these issues, robots can assist patients in physically manipulating their affected limb and measure the output motion. The robots that have been currently designed, however, provide assistance over a limited set of training motions, are not portable for in-home and in-clinic use, have high cost and may not provide sufficient safety or performance. This thesis proposes the idea of incorporating a mobile drive base into lower extremity rehabilitation robots to create a portable, inherently safe system that provides assistance over a wide range of training motions. A set of rehabilitative motion tasks were established and a six-degree-of-freedom (DOF) motion and force-sensing system was designed to meet high-power, large workspace, and affordability requirements. An admittance controller was implemented, and the feasibility of using this portable, low-cost system for movement assistance was shown through tests on a healthy individual. An improved version of the robot was then developed that added torque sensing and known joint elasticity for use in future clinical testing with a flexible-joint impedance controller

Usability evaluation of an interactive leg press training robot for children with neuromuscular impairments.

BACKGROUND The use of robotic technology for neurorehabilitative applications has become increasingly important for adults and children with different motor impairments. OBJECTIVE The aim of this study was to evaluate the technical feasibility and usability of a new interactive leg-press training robot that was developed to train leg muscle strength and control, suitable for children with neuromuscular impairments. METHODS An interactive robotic training system was designed and constructed with various control strategies, actuators and force/position sensors to enable the performance of different training modes (passive, active resistance, and exergames). Five paediatric patients, aged between 7 and 16 years (one girl, age 13.0 ± 3.7 years, [mean ± SD]), with different neuromuscular impairments were recruited to participate in this study. Patients evaluated the device based on a user satisfaction questionnaire and Visual Analog Scale (VAS) scores, and therapists evaluated the device with the modified System Usability Scale (SUS). RESULTS One patient could not perform the training session because of his small knee range of motion. Visual Analog Scale scores were given by the 4 patients who performed the training sessions. All the patients adjudged the training with the interactive device as satisfactory. The average SUS score given by the therapists was 61.2 ± 18.4. CONCLUSION This study proposed an interactive lower limb training device for children with different neuromuscular impairments. The device is deemed feasible for paediatric rehabilitation applications, both in terms of technical feasibility and usability acceptance. Both patients and therapists provided positive feedback regarding the training with the device

Effects on mobility training and de-adaptations in subjects with Spinal Cord Injury due to a Wearable Robot: A preliminary report

open7noopenSale, Patrizio; Russo, Emanuele Francesco; Russo, Michele; Masiero, Stefano; Piccione, Francesco; Calabrò, Rocco Salvatore; Filoni, SerenaSale, Patrizio; Russo, Emanuele Francesco; Russo, Michele; Masiero, Stefano; Piccione, Francesco; Calabrò, Rocco Salvatore; Filoni, Seren

Active robotic training improves locomotor function in a stroke survivor

Abstract Background Clinical outcomes after robotic training are often not superior to conventional therapy. One key factor responsible for this is the use of control strategies that provide substantial guidance. This strategy not only leads to a reduction in volitional physical effort, but also interferes with motor relearning. Methods We tested the feasibility of a novel training approach (active robotic training) using a powered gait orthosis (Lokomat) in mitigating post-stroke gait impairments of a 52-year-old male stroke survivor. This gait training paradigm combined patient-cooperative robot-aided walking with a target-tracking task. The training lasted for 4-weeks (12 visits, 3 × per week). The subject’s neuromotor performance and recovery were evaluated using biomechanical, neuromuscular and clinical measures recorded at various time-points (pre-training, post-training, and 6-weeks after training). Results Active robotic training resulted in considerable increase in target-tracking accuracy and reduction in the kinematic variability of ankle trajectory during robot-aided treadmill walking. These improvements also transferred to overground walking as characterized by larger propulsive forces and more symmetric ground reaction forces (GRFs). Training also resulted in improvements in muscle coordination, which resembled patterns observed in healthy controls. These changes were accompanied by a reduction in motor cortical excitability (MCE) of the vastus medialis, medial hamstrings, and gluteus medius muscles during treadmill walking. Importantly, active robotic training resulted in substantial improvements in several standard clinical and functional parameters. These improvements persisted during the follow-up evaluation at 6 weeks. Conclusions The results indicate that active robotic training appears to be a promising way of facilitating gait and physical function in moderately impaired stroke survivors.http://deepblue.lib.umich.edu/bitstream/2027.42/112853/1/12984_2011_Article_375.pd