Advanced Robotics for Medical Rehabilitation: Current State of the Art and Recent Advances

Professor Shane Xie
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This might be done with either the physiotherapist controlling the device via teleoperation, in order to study the patient using the device in real-time, or through monitoring the data gathered during the exercises, if this information was uploaded to a cloud-based system. This section describes a number of devices designed for stroke rehabilitation, compares their key features, and divides the devices into 2 groups: commercial devices currently available for purchase; and research-based devices that are either at the prototype or clinical trial stage.

The focus of the current paper is the hand and wrist, and therefore upper arm stroke rehabilitation is not discussed. This can be found in Loureiro et al. A literature search was performed in PubMed, ScienceDirect, Google Scholar and Web of Science, using the following key words and phrases: robotic hand rehabilitation, robotic wrist rehabilitation, lower arm robotic rehabilitation, robotic stroke rehabilitation, BMI in stroke rehabilitation, BMI in rehabilitation, brain computer interface BCI in stroke rehabilitation, arm based exoskeletons, arm-based end-effectors, global stroke statistics, and stroke rehabilitation.

There are many commercial rehabilitation devices; the most prevalent of which are listed in Table SI. These devices have a wide range of cost and complexity. For example, Fig.

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A Power-Web: consisting of a simple circular rubber sheet containing multiple small spaces. The Power-Web allows the patient to insert their fingers and then move the fingers in different motions with the rubber providing a form of resistance training. B Kinetec Maestra: worn mounted to the wrist and the tips of the fingers, the Kinetic Maestra can open and close the fingers in a single motion.

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J Robot. The Rutgers Master II-new design force-feedback glove. It is necessary to focus on the targeted stage of recovery [ 86 ]. This project intends to acquire domestic capabilities of maintaining steam generators, so that this advanced skills could be applied to the related areas. In such circumstances, robotics technologies can provide an effective answer to the problem of securing, manipulating and inventorying all stored materials. Ali H. Journal of Huazhong University of Science and Technology.

Through raising the arm on which the device is worn, the user can achieve a full opening and closing motion. The non-commercial devices, divided into 2 categories according to device type end-effector or exoskeleton.

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The current iteration is SPIDAR-8 52 , a 2-handed multi-finger interactive device designed to incorporate all of the advantages of the previous devices, which is currently undergoing clinical trials. The HapticKnob, developed in by Lambercy et al. In the ReHapticKnob 76 was created to overcome previous limitations with the HapticKnob, such as low output force and only limited DOF measurements.

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HandCARE is portable, as it is lightweight and easy to set up. Although its mounting could be considered an exoskeleton due to it encasing the entire hand, the system could also be classed as an end-effector due to its grasping mechanism.

Advanced Robotics for Medical Rehabilitation - Current State of the Art and Recent Advances

Master Finger-2, developed by Ueki et al. It can also use other fingers and provide force feedback for VR use. All of the end-effector devices discussed here are listed in Table SII. The device, created in by Takahashi et al. Due to its exposed palm design, for additional realism patients could also feel the object they were grasping. The Actuated Finger Exoskeleton AFX , fabricated in , is a single-finger rehabilitation system created as a reach-to-pinch system with 2 motors: 1 motor provides force for extension and a second motor provides force for flexion of each finger.

Full actuation is provided for each joint in the finger. The first hybrid-exoskeleton, constructed in Gifu University by Kawsaki et al. Their next generation device 90 , a semi-exoskeleton, encased both the hand and the wrist in an end-effector-style unit, allowing for full flexion and extension of the fingers, while aiding in wrist stability.

Each individual joint of the finger was controlled by a single motor; with a passive joint to ensure complete alignment. In addition, the wrist arrangement used a single motor to provide motion. This system not only provided the power to move the hand for exercise, but was also capable of recording the motion for use in a VR environment. This system used an arrangement of wire tendons running through a specifically-designed kinematic set-up. This mechanism ensured that the fingers could not move in the wrong alignment. Brokaw et al. An elastic cord was used to aid extension of the hand.

This method, however, could lead to issues if a patient with weak flexion abilities was using the device. The IHRG system is still in development.

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An initial soft-exoskeleton-based system, the Pneu Glove, was designed in by Connelly et al. This device used a combination of servos and a pneumatic pump arrangement to inflate a bladder in each finger of the glove, pushing the finger into extension. This device could be used with or without a VR game and incorporated a headset to allow for a more interactive rehabilitation experience. Soft robotic devices advanced again in , when the Walsh group at Harvard University developed a soft robot glove that allowed for grasping of an object using inflatable cells mounted on the backs of the fingers This device was capable of grasping an object in a realistic manner and, due to its soft robot construction, reduced the chance of injury.

HES is a 3D-printed, inexpensive, portable exoskeleton system designed to run as a cable-driven single-phalanx motion system. This device allows for successful grasping of different objects and is currently being used in tests with patients. SOPHIA , created at Heriot-Watt University in , uses a combination of soft robotic actuators to physically aid rehabilitation by providing extension motion, incorporating a BMI interface to increase neural recovery. Helping Hand, created by Zhao et al. The Helping Hand also uses a novel fibre optic solution to counteract inherent errors in several flexible sensors.

Most currently available commercial systems are of the end-effector type, which are the more mature and stable type of device. However, this review found a gradual decrease in the number of end-effector systems being developed. This might be because these systems cannot aid in full movement of the fingers or wrist, since they have reached maturity in their construction and are primarily only available at fixed locations.

End-effector systems, however, could be regarded as a more robust technology due to their fixed nature. Thus, they can provide accurate feedback and apply resistance to the patient in the same alignment as they are experiencing in a VR simulation. Exoskeleton systems are currently being developed at more than twice the rate of end-effector systems. These new exoskeleton systems counteract some of the flaws found in end-effector systems, by aiding the motion of the fingers and wrist.

Exoskeleton systems also provide accurate feedback to a VR system, but they remain bulky, intimidating and not very robust. These hybrids overcome the problem of not being able to provide force to move the fingers and wrist. However, this approach has the disadvantage of requiring a large complex system design. Even with these issues, there has been an increase in the number of exoskeleton devices now reaching small-scale clinical trials, with either individual or complementary modules to be used in combination with other upper limb rehabilitation aids to gain a better view of robotic rehabilitation overall.

Recently, a minimum of 3 forms of orthosis have been developed every year. As shown previously, all have remarkably different approaches to solving the problems that occur in the different stages of stroke rehabilitation. BMI systems are a novel area of technology with great potential in medical robotics. BMIs have been proposed for motor neurorehabilitation, motor replacement and assistive technologies These systems use physiological signals originating in the brain to activate or deactivate external devices or computers.

They may rely on passively generated brain signals e. Non-invasive BMIs relate to systems that gather brain signals without surgical procedures. Due its availability and ease of use, EEG is widely used in clinical stroke rehabilitation; thus, these systems are the focus of this review.

An EEG can record rhythm activity, such as the mu-rhythm, also called the sensori-motor rhythm SMR , 4— Hz and its harmonics 8—30 Hz , which show a clear functional specificity, disappearing during planned, actual, or imagined movements and event-related potentials ERPs , and occurring in response to a specific sensory, cognitive or motor event. Despite the fact that all these techniques could be used to control devices, only the application of SMRs involving motor imagery and neuroplasticity has been developed as a rehabilitation strategy to date.

Thus, this review only addresses this specific type of BMI. The use of BMIs in stroke rehabilitation is divided into 2 main approaches: a monitoring mechanism, with recorded brain signals serving as feedback for concentration levels of the physiotherapy practice; and a control framework in which an artificial actuator is driven at will. A significant number of patients present with hand motor impairment post-stroke.

In addition, loss of concentration limits the efficacy of conventional physiotherapy. Mental practice of voluntary movement e.

MI, in which patients are instructed to imagine moving their own limbs, but without performing a real movement has been envisaged to alleviate this loss of focus This positive effect may be due to the promotion of neural plasticity and engagement of relevant sensorimotor regions of the brain , Nevertheless, further studies are needed to clarify the effects and mechanisms of MI in stroke rehabilitation. More specifically, voluntary movement or motor intention of the arm, hand and wrist activates the primary sensorimotor area in the brain, which is characterized by a desynchronization of the 8—30 Hz brain rhythms over the hemisphere contralateral to the limb in use This neural signature can be detected in the EEG signals and processed to provide control commands for an artificial actuator.

EEG-based BMI aids the patient, either by bypassing a physical impairment in the ability to control the hand, or by highlighting engagement with the task.

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Brain-machine interface framework. Electrophysiological signals are extracted and processed to produce control signals for external actuators, which in turn provide visual and somatosensory feedback to the user. Birbaumer et al. In turn, feedback would foster neuroplasticity, the ability of the brain to form and reorganize synaptic connections, especially in response to injury, thus facilitating motor recovery , , Two issues challenge the advancement of BMI in post-stroke rehabilitation robotics. Firstly, not every patient can consistently modulate their mu 8—13 Hz and beta 12—30 Hz rhythms in response to MI tasks, thus preventing the extraction of reliable control signals Secondly, the small signal-to-noise ratio, combined with the poor spatial resolution inherent to EEG systems, results in low bit rates Fig.

Nevertheless, recent studies point out that the use of neuromodulation techniques, such as transcranial direct current stimulation, which applies a small current exciting or inhibiting brain activities, may improve performance in MI Although there is increasing clinical evidence showing the benefits of BMI-related tools in stroke neurorehabilitation, more and larger clinical studies are needed to fully establish the efficacy of these systems, as demonstrated by Kansaku et al.

The increasing use of both robotic-aided techniques and BMI systems is reviewed in this paper, through examples from key clinical trials involving these devices. To clarify how the conclusions of these studies were assessed the most common scores used to measure stroke incidence are described here. Multiple clinical trials using robotic-aided rehabilitation have been carried out; among them, the most extensive trial was conducted in by Lo et al. This experiment was performed using the In-motion robot MIT-Manus with horizontal, vertical, wrist and hand modules and involved patients.

The study provided evidence for the benefits of robotic-aided rehabilitation after a stroke. In summary, the researchers found that using the device over 36 1-h sessions of robotic therapy was equally as beneficial as 36 1-h sessions of high-intensity traditional therapy regarding improvement measured over both and week periods. The commercially-available Amadeo system was used in a hand-specific trial , involving 12 patients who performed exercises for 18 h in total. This trial showed comparable improvements in all of the measurements used, compared with the standard physiotherapy treatment a patient would undertake.

It was also noted that this improvement lasted beyond 6 months after stroke. The Haptic Master , a full arm exoskeleton, was used by Timmermans et al. Trial results showed a significant improvement in the ARAT using the robot system and a noticeable, but similar improvement on the MAL for both robot and standard rehabilitation. The researchers noted that the effects of the robotic therapy were sustained after the trial was completed, while the benefits from conventional therapy were not.

The Haptic Master is not directly a hand rehabilitation system, but, due to its common usage in upper limb rehabilitation and that it is grasped by the hand, it has been included in the clinical tests. The PneuGlove was used in a 6-week trial with 7 stroke patients; each performing the exercise in 18 training sessions over the trial period The sample sizes of these trials are not large enough to draw statistically significant conclusions, but the majority of them show a common pattern, that the benefits of robot therapy outlast the standard treatment.

Due to the preliminary nature of these studies, not all of the clinical trials used the same score system or the full list of measurements described earlier. BMI systems were also included within some clinical studies as a rehabilitation technique for stroke recovery. Several examples can be highlighted from this perspective.

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