Gravity compensation of an upper extremity exoskeleton

Ergodic Shared Control: Closing the Loop on pHRI Based on Information Encoded in Motion

Advances in exoskeletons and robot arms have given us increasing opportunities for providing physical support and meaningful feedback in training and rehabilitation settings. However, the chosen control strategies must support motor learning and provide mathematical task definitions that are actionable for the actuation. Typical robot control architectures rely on measuring error from a reference trajectory. In physical human-robot interaction, this leads to low engagement, invariant practice, and few errors, which are not conducive to motor learning. A reliance on reference trajectories means that the task definition is both over-specified—requiring specific timings not critical to task success—and lacking information about normal variability. In this article, we examine a way to define tasks and close the loop using an ergodic measure that quantifies how much information about a task is encoded in the human-robot motion. This measure can capture the natural variability that exists in typical human motion—enabling therapy based on scientific principles of motor learning. We implement an ergodic hybrid shared controller(HSC) on a robotic arm as well as an error-based controller—virtual fixtures—in a timed drawing task. In a study of 24 participants, we compare ergodic HSC with virtual fixtures and find that ergodic HSC leads to improved training outcomes.

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke

BACKGROUND

Chronic hand and wrist impairment are frequently present following stroke and severely limit independence in everyday life. The wrist orientates and stabilizes the hand before and during grasping, and is therefore of critical importance in activities of daily living (ADL). To improve rehabilitation outcomes, classical therapy could be supplemented by novel therapies that can be applied in unsupervised settings. This would enable more distributed practice and could potentially increase overall training dose. Robotic technology offers new possibilities to address this challenge, but it is critical that devices for independent training are easy and appealing to use. Here, we present the development, characterization and wearability evaluation of a fully portable exoskeleton for active wrist extension/flexion support in stroke rehabilitation.

METHODS

First we defined the requirements, and based on these, constructed the exoskeleton. We then characterized the device with standardized haptic and human-robot interaction metrics. The exoskeleton is composed of two modules placed on the forearm/hand and the upper arm. These modules weigh 238 g and 224 g, respectively. The forearm module actively supports wrist extension and flexion with a torque up to 3.7 Nm and an angular velocity up to 530 deg/s over a range of 154∘. The upper arm module includes the control electronics and battery, which can power the device for about 125 min in normal use. Special emphasis was put on independent donning and doffing of the device, which was tested via a wearability evaluation in 15 healthy participants and 2 stroke survivors using both qualitative and quantitative methods.

RESULTS

All participants were able to independently don and doff the device after only 4 practice trials. For healthy participants the donning and doffing process took 61 ±15 s and 24 ±6 s, respectively. The two stroke survivors donned and doffed the exoskeleton in 54 s/22 s and 113 s/32 s, respectively. Usability questionnaires revealed that despite minor difficulties, all participants were positive regarding the device.

CONCLUSIONS

This study describes an actuated wrist exoskeleton which weighs less than 500 g, and which is easy and fast to don and doff with one hand. Our design has put special emphasis on the donning aspect of robotic devices which constitutes the first barrier a user will face in unsupervised settings. The proposed device is a first and intermediate step towards wearable rehabilitation technologies that can be used independently by the patient and in unsupervised settings.

System Framework of Robotics in Upper Limb Rehabilitation on Poststroke Motor Recovery

Neurological impairments such as stroke cause damage to the functional mobility of survivors and affect their ability to perform activities of daily living. Recently, robotic treatment for upper limb stroke rehabilitation has received significant attention because it can provide high-intensity and repetitive movement therapy. In this review, the current status of upper limb rehabilitation robots is explored. Firstly, an overview of mechanical design of robotics for upper-limb rehabilitation and clinical effects of part robots are provided. Then, the comparisons of human-machine interactions, control strategies, driving modes, and training modes are described. Finally, the development and the possible future directions of the upper limb rehabilitation robot are discussed.

A pilot study into reaching performance after severe to moderate stroke using upper arm support

Stroke effects millions of people each year and can have a significant impact on the ability to use the impaired arm and hand. One of the results of stroke is the development of an abnormal shoulder-elbow flexion synergy, where lifting the arm can cause the elbow, wrist, and finger flexors to involuntarily contract, reducing the ability to reach with the arm and hand opening. This study explored the effect of using support at the upper arm to improve hand and arm reaching performance. Nine participants were studied while performing a virtual reaching task under three conditions: while the weight of their impaired arm was supported by a robot arm, while unsupported, and while using their non-impaired arm. Most subjects exhibited faster and more accurate reaching while supported compared to unsupported. For the subjects who could voluntarily open their hand, most were able to more swiftly open their hand when using upper arm support. In many cases, performance with support was not statistically different than the unaffected arm and hand. Muscle activity of the impaired limb with upper arm support showed decreased effort to lift the arm and reduced biceps activity in most subjects, pointing to a reduction in the abnormal flexion synergy while using upper arm support. While arm support can help to reduce the activation of abnormal synergies, weakness resulting from hemiparesis remains an issue impacting performance. Future systems will need to address both of these causes of disability to more fully restore function after stroke.

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.

The use of an exoskeleton to investigate the self advantage phenomenon

This paper presents an upper extremity exoskeleton with an original application in neuroscience. The novelty of this study is the investigation of the self-advantage phenomenon under various experimental conditions. Usually this kind of experiments lies only on human visual ability to explicitly and/or implicitly recognize their own arm movements. Using an exoskeleton to replay recorded trajectories allows to give another perspective to the previous studies in including the proprioceptive ability of humans. Twelve healthy subjects were involved in this study. The results show that the self advantage phenomenon is even more present in the implicit tasks.

Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients

Upper-limb impairment after stroke is caused by weakness, loss of individual joint control, spasticity, and abnormal synergies. Upper-limb movement frequently involves abnormal, stereotyped, and fixed synergies, likely related to the increased use of sub-cortical networks following the stroke. The flexible coordination of the shoulder and elbow joints is also disrupted. New methods for motor learning, based on the stimulation of activity-dependent neural plasticity have been developed. These include robots that can adaptively assist active movements and generate many movement repetitions. However, most of these robots only control the movement of the hand in space. The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination. First, a review of studies on upper-limb coordination in stroke patients is presented and the potential for recovery of coordination is examined. Second, issues relating to the mechanical design of exoskeletons and the transmission of constraints between the robotic and human limbs are discussed. The third section considers the development of different methods to control exoskeletons: existing rehabilitation devices and approaches to the control and rehabilitation of joint coordinations are then reviewed, along with preliminary clinical results available. Finally, perspectives and future strategies for the design of control mechanisms for rehabilitation exoskeletons are discussed.

An arc-length warping algorithm for gesture recognition using quaternion representation

This paper presents a new algorithm, called Dynamic Arc-Length Warping algorithm (DALW) for hand gesture recognition based on the orientation data. In this algorithm, after calculating the quaternion for each orientation measurement, we use DALW algorithm to obtain a similarity measure between different trajectories. We present the benefits of using quaternion alongside the implementation of Dynamic Arc Length Warping to present an optimized tool for gesture recognition.We show the advantages of this approach compared with other techniques. This tool can be used to distinguish similar and different gestures. An experimental validation is carried out to classify a series of simple human gestures.