Archive for category Rehabilitation robotics

[Abstract] A Review: Hand Exoskeleton Systems, Clinical Rehabilitation Practices, and Future Prospects

Abstract

Spinal cord injury (SCI) and stroke are pathologies that often result in the loss of/decrease in hand functionality. Hand function is a critical component of everyday life and therefore, a primary focus of clinical SCI/stroke rehabilitation is hand function recovery/improvement. In recent years, there has been a surge in hand exoskeleton research due to the potential for exoskeletons to improve clinical rehabilitation efficiency through automation. However, there is a disconnect between current clinical practice and exoskeleton research, resulting in a minority of hand exoskeletons being tested on individuals with SCI and/or stroke. This review article provides a comprehensive analysis and review of hand exoskeleton studies based on clinical rehabilitation practices to bridge the knowledge gap between clinical application and laboratory research. The key findings from this paper are: 1) current hand exoskeletons can successfully complete simple ADL tasks but lack the precision for fine motor control, 2) most hand exoskeletons exhibit a low number of degrees-of-freedom compared to the human hand, which may limit movement capability, 3) the majority of hand exoskeletons lack sensing capabilities, restricting viable control methods and user interfaces, and 4) inconsistent evaluation methods across studies do not allow for accurate performance assessment for different exoskeletons. The comparative assessments performed by this survey article show that there remain deficits between clinical hand rehabilitation practices and the current state of hand exoskeletons. By delineating these shortcomings, the information presented in this work can help inform future developments in the field of assistive and rehabilitative hand exoskeletons such that the gap between research and application may be closed.

Published in: IEEE Transactions on Medical Robotics and Bionics ( Early Access )

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[Abstract] Effects of Robot-assisted Upper Extremity Rehabilitation on Change in Functioning and Disability in Patients With Neurologic Impairment: A Pilot Study – Full Text PDF

Abstract

Introduction: The aim is to evaluate the effect of robot-assisted training on the most important aspects of functioning and disability in patients with upper extremity neurologic impairment.

Materials and Methods: A prospective six-week pilot study included robot-assisted training of the upper extremity and conventional neurorehabilitation in 12 participants after a stroke or traumatic brain injury. Outcome measurements were range of motion (ROM), the International Classification of Functioning, Disability and Health (ICF) Core Set for Hand and the Visual Analog Scale (VAS) for pain sensation. A Wilcoxon test was used for the analysis of pre- and post-test differences and Spearman’s correlation was used for connecting the data collected.

Results: A statistically significant difference was found for ROM (shoulder abduction/adduction, shoulder flexion/extension, shoulder internal/external rotation and forearm pronation/supination) and a number of ICF categories (Body Function: b280, b710, b715, b730, b760; Activities and Participation: d230, d430, d440, d445, d5). A significant positive correlation of medium intensity (r=0.589) was found between the duration of movement coordination training and the ICF category b760. We did not find a statistically significant difference in pain sensation (VAS) with regard to the direct use of the device. For all analyses, p<0.05 and CI was 95%.

Conclusion: Robot-assisted training and conventional neurorehabilitation improved motor and functional recovery. There was a correlation between training a specific goal on the device and one of the ICF Body Function categories.

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[ARTICLE] Design of a 3D printed hybrid mechanical structure for a hand exoskeleton – Full Text

By Jens Vertongen and Derek Kamper

Abstract

Stroke survivors often have difficulty performing activities of daily living (ADLs) due to hand impairments. Several assistive devices have been developed for stroke survivors to assist them with ADLs but most of these devices are difficult to don and doff for a stroke survivor due to highly flexed postures of the wrist and digits. This paper presents a hybrid 3D printed mechanical structure for an assistive hand exoskeleton created for stroke survivors. The design facilitates donning and doffing of the assistive exoskeleton by enabling an approach entirely from the dorsal side of the hand, thereby allowing the fingers to stay flexed. The design criteria, resulting design and the prototype development are presented. The initial prototype of the structure, using a hybrid combination of rigid and flexible materials, was lightweight (only 185 g), while maintaining a high range of motion. Future directions for further improvements and user studies are described.

Introduction

Stroke often results in a severe impairment of the upper limb, particularly the distal segment, in stroke survivors. In 2010 there were 6.7 million stroke survivors in the United States alone, with 795,000 new or recurring strokes occurring each year [1]. The symptoms of this impairment include paresis, involuntary muscle contraction patterns and impaired movements of the paretic hand [2]. These can make it difficult for stroke survivors to perform activities of daily living (ADLs) such as eating, bathing and dressing. Therefore, an assistive device for the distal upper limb that can assist with these activities has the potential to improve the stroke survivor’s quality of life.

Several assistive devices have been reported in literature. These devices commonly use a rigid structure [3], [4], [5] or a glove that routes tendons along the fingers [6], [7], [8]. These devices are often difficult to don and doff due to the typically flexed hand posture and the lack of voluntary finger extension. Coupling an assistive device to a stroke survivor’s hand can be especially challenging, due to the limited available contact area on the digits and the substantial resistance to even passive extension of the digits that arises from involuntary muscle activation [9].

In this paper we present the design of a hybrid, 3D printed mechanical structure of a hand exoskeleton intended to actuate the fingers of stroke survivors. The aim is to improve the donning process while providing the capability of both flexion and extension assistance from a single actuator for each finger located on the dorsal side of the hand. The actuator drives push-pull cables that can either flex or extend the joints of the digit. Thus, the mechanical structure, composed of rigid and flexible materials, must serve as the interface between the actuator and the digit.

Design requirements

The exoskeleton structure must fulfill multiple roles: guiding the cables actuating the hand, transmitting moments to the fingers and thumb, and preventing excessive joint rotation. The design requirements are listed in the following three categories: structural, safety and transmission requirements.

Structural requirements

The device has to fit the hand of the user while maintaining a low profile (maximum height of 25 mm above the finger) and should be easy to don and doff. The structure has to guide the push-pull cables and should keep the palmar surface free and accessible to minimize interference with the user’s sensory feedback and manipulation of objects. The device has to be lightweight with a mass under 300 g. The range of motion (ROM) of the hand with the device should be close to the normal ROM: 100, 105 and 85° for the MCP, PIP and DIP respectively and 56 (MCP) and 73° (IP) for the thumb [10]. Furthermore, the fingers should be able to abduct.

Safety requirements

The mechanical structure should prevent excessive joint flexion and extension. Moving parts have to be shielded to prevent tissue or body parts to be pinched.

Transmission requirements

Ultimately, the structure has to transmit the moments from the cables to the finger joints. The friction between the cables and the guides should be as low as possible.

Exoskeleton structure design

Full structure

The structure was designed through an iterative process of 3D design, rapid prototyping and verification. Drawing on hand dimensions from the literature [11], we designed a prototype using 3D CAD software (Figure 1). The mechanical structure is a hybrid design of rigid components, attached to the digit segments that guide the cables and prevent excessive joint rotation, and a flexible inner lining that connects the rigid components and improves comfort and functionality. The exoskeleton can be donned entirely from the dorsal side of the hand, with the rigid pieces deforming to accommodate the finger, thereby generating a clamping force against the sides of the finger. The structure extends to the forearm in order to provide bracing to maintain the wrist in a functional extended posture. This splint structure also provides housing for the actuators and electronics. Straps around the hand and wrist help to secure the location of the device on the body.

Figure 1: Full overview of the mechanical structure in red with the flexible lining in gray and actuators in black. Top: dorsal view. Bottom: palmar view.

Figure 1:

Full overview of the mechanical structure in red with the flexible lining in gray and actuators in black. Top: dorsal view. Bottom: palmar view.

Finger structure

The anatomical differences in the distal phalanx (DP) of the thumb and the pinky finger, in comparison with the index, middle and ring fingers, require a different form factor for these segments (see Figure 1). We employed conical shapes for the components of the intermediate phalanx (IP) to ensure proper fitting on the finger segment.

Adjacent cable-guide segments meet during joint extension to prevent hyperextension of the joints (Figure 2). To prevent hyperflexion of the joints, possible pinching and buckling of the cable, we attached a fabric sleeve at the ends of the cable guides around the wire. These fabrics clamp over the conical end and are secured with a metal ring (Figure 3).

Figure 2: The structure of one finger.

Figure 2:

The structure of one finger.

Figure 3: Detail of the sleeves preventing cable buckling.

Figure 3:

Detail of the sleeves preventing cable buckling.

The cable guides on the dorsal side of the fingers are easily customizable to achieve different moment arms around the joints. We used two cables for a better lateral stability to actuate the fingers. The friction of the cable, governed by the capstan Equation (1), should be minimized [12].(1)Tout= e−μϕTinTout= e−μϕ Tin

In Equation (1)μ is the friction coefficient and ϕ is the deflection angle of the cable segment that is sliding in the tube. We designed the path inside the rigid component with the smallest curvature possible in order to minimize friction along the cable. Figure 4 shows a section of the finger structure where the cable path is visible.

Figure 4: Section of the index finger showing the path of the cable within the guide.

Figure 4:

Section of the index finger showing the path of the cable within the guide.

The push-pull cable locks in the front of the structure in a groove above the DP (see Figure 3). This groove facilitates the assembly of the cable in the structure. The recess in this groove permits easy removal of the cable with a screwdriver.

Arm structure

The arm structure supports the actuators, transmission and electronics, in addition to maintaining a neutral wrist posture (see Figure 5). Velcro straps around the hand, wrist and forearm secure the location on the arm. We placed the actuators locally, on the forearm, to avoid frictional losses inherent to the use of long Bowden cables needed with remotely placed actuators.

Figure 5: The arm structure that supports the actuators. Motors and cables are represented with transparent bodies.

Figure 5:

The arm structure that supports the actuators. Motors and cables are represented with transparent bodies.

Prototype development

The final prototype, realized with acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) through 3D printing, proved to be lightweight and comfortable. The mass of the entire mechanical structure was only 185 g. The distal portion that actually moves with the fingers and thumb was 82 g in total, or an average of roughly 16 g per digit; the forearm brace accounts for the other 103 g. The ROM of the fingers, while wearing the structure when not actuated, is very close to the normal ROM (see Table 1). The finger structures in black and the arm structure in white are shown in Figure 6.Table 1:Weight and ROM of the prototype.

WeightFingers

82 g
Arm part

103 g
Total

185 g
ROMMCP (°)PIP (°)DIP (°)
Thumb5070
Index finger808575
Middle finger859575
Ring finger909575
Pinky finger758575
Normal ROM thumb5673
Normal ROM finger10010585
Figure 6: The final prototype.

Figure 6:

The final prototype.

3D printing and material selection

We used 3D printing as a rapid prototyping method to produce the exoskeleton structure due to the various benefits such as easily available, quick and the ability to produce complex shapes.

The structure was produced with two different 3D printers. The rigid and flexible material of the finger structures was produced by the BCND Sigma R17 3D printer. The rigid material of the arm structure was printed by the Stratasys Dimension 1200 3D printer that has a larger build volume to produce the arm structure.

We employed ABS to produce the rigid components. This material was selected rather than polylactic acid (PLA) due to its greater toughness. A more flexible material, TPU, was chosen for the liner.

Performance

The weight and the range of motion of the exoskeleton are important for the functionality and comfort for the user. Table 1 lists the weight and the range of motion of the exoskeleton. The normal ROM values of the thumb and the fingers [10] are shown for reference. The exoskeleton proved to be comfortable to wear for extended periods of time. This was tested for a period of 1 h of use by one of the authors while performing ADLs. No pain or discomfort was reported for the duration of the test. Due to the large range of motion and slim profile, the author was able to grasp most everyday objects. There was some minor discomfort in pronation and supination, however, due to the location of the structure on the forearm.

Discussion

An actuated hand exoskeleton has the potential to improve the quality of life for stroke survivors by assisting with ADLs. The lightweight mechanical structure developed in this study helps to make such a device possible. Easily customizable to the shape of the user’s hand, the structure can accommodate the push-pull cables used to drive the fingers while preventing joint hyperextension. The ability to put on the device entirely from the dorsal side of the hand greatly improves donning for stroke survivors with a flexed resting posture and resistance to passive extension. The slim profile, high range of motion and low mass result in a functional and comfortable structure.

The major limitation of the current structure is the TPU used for the flexible liner. A material with greater elasticity could be better suited for the application and could improve user comfort. Further development of the design and user testing are needed to improve and validate the design.

References

1. Go, AS, Mozaffarian, D, Roger, VL, Benjamin, EJ, Berry, JD, Borden, WB, et al. Executive summary: heart disease and stroke statistics-2013 update: a report from the American heart association. Circulation 2013;127:143–52. https://doi.org/10.1161/CIR.0b013e318282ab8fSearch in Google Scholar

2. Gresham, GE, Stason, WB, Duncan, PW. Post-stroke rehabilitation. Darby, PA: Diane Publishing Company; 2004. Search in Google Scholar

3. Cempini, M, Cortese, M, Vitiello, N. A powered finger-thumb wearable hand exoskeleton with self-aligning joint axes. IEEE/ASME Trans Mechatron 2015;20:705–16. https://doi.org/10.1109/tmech.2014.2315528Search in Google Scholar

4. Ho, NSK, Tong, KY, Hu, XL, Fung, KL, Wei, XJ, Rong, W. An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation. In: IEEE international conference on rehabilitation robotics. IEEE, New York City; 2011, 1–5. Search in Google Scholar

5. Chiri, A, Giovancchini, F, Vitiello, N, Cattin, E, Roccella, S, Vecchi, F, et al. HANDEXOS: towards an exoskeleton device for the rehabilitation of the hand. In: IEEE/RSJ international conference on intelligent robots and systems. IEEE, New York City; 2009, 1106–11. Search in Google Scholar

6. In, H, Kang, BB, Sin, M, Cho, KJ. Exo-glove: a wearable robot for the hand with a soft tendon routing system. IEEE Robot Autom Mag 2015;22:97–105. https://doi.org/10.1109/MRA.2014.2362863Search in Google Scholar

7. Nycz, CJ, Delph, MA, Fischer, GS. Modeling and design of a tendon actuated soft robotic exoskeleton for hemiparetic upper limb rehabilitation. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, New York City; 2015, 3889–92. Search in Google Scholar

8. Rose, CG, O’Malley, MK. Hybrid rigid-soft hand exoskeleton to assist functional dexterity. IEEE Robot Autom Lett 2019;4:73–80. https://doi.org/10.1109/lra.2018.2878931Search in Google Scholar

9. Kamper, DG, Rymer, WZ. Quantitative features of the stretch response of extrinsic finger muscles in hemiparetic stroke. Muscle Nerve 2000;23:954–61. https://doi.org/10.1002/(sici)1097-4598Search in Google Scholar

10. Hume, M, Gellman, H, McKellop, H, Brumfield, RH. Functional range of motion of the joints of the hand. J Hand Surg 1990;15:240–3. https://doi.org/10.1016/0363-5023(90)90102-wSearch in Google Scholar

11. Buryanov, A, Kotiuk, V. Proportions of hand segments. Int J Morphol 2010;28:755–8. https://doi.org/10.4067/s0717-95022010000300015Search in Google Scholar

12. Kaneko, M, Yamashita, T, Tanie, K. Basic considerations on transmission characteristics for tendon drive robots. In: Robots in unstructured environments: IEEE, New York City; 1991, 827–32. Search in Google Scholar

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[ARTICLE] Peak Activation Shifts in the Sensorimotor Cortex of Chronic Stroke Patients Following Robot-assisted Rehabilitation Therapy – Full Text

ABSTRACT

Background:

Ischemic stroke is the most common cause of complex chronic disability and the third leading cause of death worldwide. In recovering stroke patients, peak activation within the ipsilesional primary motor cortex (M1) during the performance of a simple motor task has been shown to exhibit an anterior shift in many studies and a posterior shift in other studies.

Objective:

We investigated this discrepancy in chronic stroke patients who completed a robot-assisted rehabilitation therapy program.

Methods:

Eight chronic stroke patients with an intact M1 and 13 Healthy Control (HC) volunteers underwent 300 functional magnetic resonance imaging (fMRI) scans while performing a grip task at different force levels with a robotic device. The patients were trained with the same robotic device over a 10-week intervention period and their progress was evaluated serially with the Fugl-Meyer and Modified Ashworth scales. Repeated measure analyses were used to assess group differences in locations of peak activity in the sensorimotor cortex (SM) and the relationship of such changes with scores on the Fugl-Meyer Upper Extremity (FM UE) scale.

Results:

Patients moving their stroke-affected hand had proportionally more peak activations in the primary motor area and fewer peak activations in the somatosensory cortex than the healthy controls (P=0.009). They also showed an anterior shift of peak activity on average of 5.3-mm (P<0.001). The shift correlated negatively with FM UE scores (P=0.002).

Conclusion:

A stroke rehabilitation grip task with a robotic device was confirmed to be feasible during fMRI scanning and thus amenable to be used to assess plastic changes in neurological motor activity. Location of peak activity in the SM is a promising clinical neuroimaging index for the evaluation and monitoring of chronic stroke patients.

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Fig. (2). Spatial distributions of peak brain activation during a grip task with both hands. Dots (red, left hemisphere stroke patients; green, healthy control subjects) are overlaid on an International Consortium for Brain Mapping brain surface template (ICBM-152).

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[ARTICLE] Design and evaluation of a novel upper limb rehabilitation robot with space training based on an end effector – Full Text

Abstract

The target of this paper is to design a lightweight upper limb rehabilitation robot with space training based on end-effector configuration and to evaluate the performance of the proposed mechanism. In order to implement this purpose, an equivalent mechanism to the human being upper limb is proposed before the design. Then, a 4 degrees of freedom (DOF) end-effector-based upper limb rehabilitation robot configuration is designed to help stroke patients perform space rehabilitation training of the shoulder flexion/extension and adduction/abduction and elbow flexion/extension. Thereafter, its kinematical model is established together with the proposed equivalent upper limb mechanism. The Monte Carlo method is employed to establish their workspace. The results show that the overlap of the workspace between the proposed mechanism and the equivalent mechanism is 96.61 %. In addition, this paper also constructs a human–machine closed-chain mechanism to analyze the flexibility of the mechanism. According to the relative manipulability and manipulability ellipsoid, the highly flexible area of the mechanism accounts for 67.6 %, and the mechanism is far away from the singularity on the drinking trajectory. In the end, the single-joint training experiments and a drinking water training trajectory planning experiment are developed and the prototype is manufactured to verify it.

1 Introduction

Strokes affect thousands of people around the world, and nearly half of stroke survivors suffer from upper limb defects, which makes it difficult for them to perform activities of daily living (ADL) independently. For most patients, exercise therapy has the potential to partially restore the loss of motor function (Béjot et al., 2016). Studies indicated that long-term intensive training and practice would be beneficial to the rehabilitation process of patients (Bertani et al., 2017). Robot-assisted therapy equipment can provide high-intensity, repetitive, interactive treatments for the affected upper limbs and obtain physiological data of patients, which has been increasingly used in rehabilitation training (Manna and Dubey, 2017).

The human upper limbs have a complex physiological structure. With the cooperation of multiple bones and muscles, the upper limbs are very flexible, which puts forward many requirements for the structural design of the rehabilitation robot (Pons, 2008). At present, there are mainly two types of upper limb rehabilitation robots, i.e., an end-effector-based type and an exoskeleton type. The end-effector-based-type upper limb rehabilitation robots support and pull the end of the patients through a closed-loop linkage mechanism or a series mechanism, so as to realize the rehabilitation training of the upper limb. The most representative of the robots with the closed-loop linkage mechanisms include MIT-MANUS (Hogan et al., 1992), D-SEMUL (Kikuchi et al., 2018), CASIA-ARM (Luo et al., 2019), and SepaRRo (Chang et al., 2018). They could perform plane compound training for the human shoulder and elbow joints, mainly the adduction/abduction of the shoulder and the flexion/extension of the elbow. The working mode of the robots with the series mechanism is to drive the human upper limbs through the mechanical arms, such as MIME (Lum et al., 2002) and GENTLE/s (Loureiro et al., 2003). Compared to the robots with the closed-chain linkage mechanisms, this type of robot increases the function of assisting the human shoulder joints in performing flexion and extension training, so it can drive the human upper limbs to move in three-dimensional space.

A characteristic of the end-effector-based-type upper limb rehabilitation robots is that it does need to be aligned with the physiological axes of the human joints during training, but it also means that it cannot implement accurate single-joint rehabilitation training for patients. Moreover, the structures of the end-traction robots are relatively simple, most of which are desktop type, that cause the robot’s range of motion (ROM) to be limited, especially the insufficient flexion and extension training of the human shoulder.

The exoskeleton-type upper limb rehabilitation robot has a kinematic structure similar to that of the human upper limb, and it generates driving force on each joint of the patient’s upper limb to drive limb rehabilitation training, such as CADEN-7 (Perry et al., 2007), Harmony (Kim and Deshpande, 2017), ARMin III (Nef et al., 2009), Co-EXos (Zhang et al., 2019), Armeo Power (Jarrase et al., 2015), and LIMPACTA (Otten et al., 2015). Compared with the end-effector-based robot, the exoskeleton robot can drive the patient’s limbs to perform three-dimensional rehabilitation training, especially the large-ROM flexion/extension training of the human shoulder. In addition, the driving force of the exoskeleton directly acts on the patient’s single joint, which can perform accurate single-joint training on the upper limbs. However, this feature also causes the exoskeleton to require more joints and motors, making the exoskeleton bulky and costly. Since the joint axes of the human upper limb is constantly changing during the rehabilitation exercise, the misalignment of the mechanical axes of the exoskeleton and the biological axes of the upper limb will lead to the mismatching of the movement of the exoskeleton with the upper limb, thus causing discomfort for the patients.

In this paper, a novel 4 DOF end-effector-based upper limb rehabilitation robot with space training is proposed by combining the end-effector-based-type and exoskeleton-type robot. The robot can assist the human upper limb in performing rehabilitation training of the shoulder flexion/extension and adduction/abduction and elbow flexion/extension. Different from the desktop-type end-effector-based robot, the proposed robot can provide a wide range of shoulder flexion/extension training for the human upper limb and cover the ROM of the upper limb. Through the mutual restriction of three mutually perpendicular active joints, the robot can perform single-joint and unidirectional rehabilitation training on the human upper limb.

The rest of this paper is structured as follows. In Sect. 2, we introduce the configuration design and mechanical design of a 4 DOF end-effector-based upper limb rehabilitation robot. In Sects. 3 and 4, the kinematical performance of the proposed configuration is analyzed in a global and local area. In Sect. 5, a 4 DOF end-effector-based robot is developed for upper limb rehabilitation, and the pursuit movement experiment and the multi-joint exercise test of the prototype are done to verify the dexterity of the design.

https://ms.copernicus.org/articles/12/639/2021/ms-12-639-2021-f01
Figure 1The configuration of robot and the human–machine closed-chain mechanism.

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[Abstract] Requirements for a home-based rehabilitation device for hand and wrist therapy after stroke

Abstract

Recovering hand function to perform activities of
daily living (ADL), is a significant step for stroke survivors
experiencing paresis in their upper limb. A home-based, robot
mediated training approach for the hand allows the patient to
continue their training independently after discharge to maximise
recovery at the patient’s pace. Developing such a hand/wrist
training device that is comfortable to wear and easy to use is the
objective of this work. Using a user-centred design approach, the
first iteration of the design is based on the requirements derived
from the users and therapists, leading to a first prototype. The
prototype is then compared and evaluated against the required
features. This paper highlights the methodology used in the
process of validating the design against our initial brief.

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[VIDEO] STEPS RehabHub Launch Video – YouTube

Next generation rehabilitation robotic and VR technology delivered by a team of first-class experts.

STEPS is leading the way in rehab technology and is home to an array of cutting edge robotic and virtual reality rehabilitation equipment. This world-leading technology assists with the intensive rehabilitation for people recovering from brain injury, spinal cord injury, strokes and complex trauma injuries.

The latest research in rehabilitation recognises that the best results are achieved through intensity of treatment. By combining the expertise of our clinicians and therapists with state-of-the-art rehabilitation technology, clients can maximise their progress and optimise their outcomes. We are the only place in the UK that gives clients access to rehabilitation robotic and VR technology in a residential setting.

• RehabHub™ – developed in Singapore by Fourier Intelligence, STEPS is the first place in the UK and only the second in Europe to offer clients access to this pioneering upper and lower limb robotic equipment, delivered in partnership with Thor Technologies.

• MindMaze – comprises two revolutionary virtual reality equipment, the MindMotion™ and MindMaze. Both were developed in Switzerland, and this trailblazing VR technology helps clients who have sustained a traumatic brain injury.

• Exoskeletons – STEPS is an assessment centre for the ReWalk™, ExoAtlet II and ReStore™ exoskeletons.

If we think you would benefit from using this specialist equipment as part of your residential rehab programme with us, an assessment will be carried out and a programme for the appropriate equipment will be created.

If you would like to find out more about booking an assessment or trial please contact us- 0114 258 7769.

http://www.stepsrehabilitation.co.uk

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[Short Review] NCyborg Project – A new stroke rehabilitation pattern based on brain computer interface

Abstract

NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo. We will briefly introduce this project in this paper.

NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo.

In China, stroke is the leading cause of death and disability in the adult population, with 1.96 million deaths each year, and 75% of the surviving patients will lose the ability to move independently. Most patients with physical disabilities cannot take care of themselves, and are difficult to carry out daily activities independently, therefore, requiring long-term functional exercises and rehabilitation.

Traditional stroke rehabilitation equipment only allows patients to passively follow the movements of the equipment. Hence, the rehabilitation effect is poor, and the patient’s willingness to train is also low. It can only be utilized as an auxiliary means for rehabilitation by the practitioners, thus increasing the cost of the rehabilitation treatment.

The department of Neurology in Tongji Hospital, affiliated to Tongji Medical College, Huazhong University of Science and Technologyis in a leading position in China in terms of scientific research and clinical strength. It undertakes 54 national, provincial and municipal research projects. The current research projects include 2 Major Special Projects of the Ministry of Science and Technology, 5 National Key R&D Programs, 1 Major Research Cultivation Program of the National Natural Science Foundation of China, 28 General Programs and Distinguished Young Scholars Programs of National Natural Science Foundation of China, and 13 provincial and municipal research projects. In the past three years, Tongji Hospital has obtained 5 various scientific research achievement awards and 19 Chinese invention patents. It has significantly influenced the clinical and basic research of neurology in China and overseas and has had a profound impact on the related textbooks, books and clinical guidelines. Meanwhile, the number of outpatient clinics in the department of Neurology reached more than 210,000 cases in 2020, the majority of which were stroke patients.

Zhejiang BrainCo, Ltd. (www.brainco.cn), incubated by Harvard Innovation Lab (www.innovationlabs.harvard.edu), is in a leading position in brain-computer interfaces, which is known as the next generation of artificial intelligence technology. BrainCo has more than 100 core patents in the field of brain-computer interface. In the “2019 Artificial Intelligence Development Whitepaper”1 released by the China Academy of Science, BrainCo, as the only brain-computer interface company on the list, was selected as the World’s Top 20 Artificial Intelligence Companies. Its intelligent BrainRobotics Prosthetic Hand based on the brain-computer interface technology was named the “Best Invention of 2019”2 by Time Magazine in the United States.

In the NCyborg Project, Tongji Hospital and BrainCo will cooperate to use brain-computer interface technology and brain-inspired intelligent robot technology to realize the rehabilitation process driven by the patients’ initiative and improve the treatment effect of stroke survivors, see Fig. 1.

The cooperative research will be carried out from the following three aspects:

(1) An algorithm for analyzing the movement intention of stroke patients based on brain-computer interface technology. Stroke survivors often experience damage to the central nervous system after a stroke, so that their movement intention cannot be effectively transmitted to the peripheral nervous system and muscles. Through the brain-computer interface technology, the patients’ active intention can be obtained from the damaged nerve tissue of the patients, thereby bypassing the damaged nerve-muscle pathway and realizing the transmission of the movement intention.

(2) Motion control strategy of rehabilitation robot based on brain-inspired motion perception. The purpose is to allow rehabilitation robots to adapt to complex and changeable activities of daily life (ADLs), to achieve the dual role of function rehabilitation and aids to daily living, to endow rehabilitation robots with the capability of brain-inspired motion perception, to realize the perception of the surrounding environment information, and to reduce users’ attention burden.

(3) The mechanism of stroke rehabilitation of brain-inspired intelligent robots. Neuroplasticity and motion function reorganization of brain tissue are the neurological principles of stroke rehabilitation. But the rehabilitation involves not only the recovery of the nervous system, but also the rehabilitation of the blood circulatory system and the muscular system. Hence, we need to study the interactive between neuromuscular and perivascular systems from micro-scale, macro-scale and meso-scale perspectives.

In a word, NCyborg Project aims to develop an easy-to-use, reliable and affordable stroke rehabilitation robot to improve the rehabilitation effect of stroke survivors, speed up the rehabilitation process, and reduce the costs. The robot will first start with hand rehabilitation and is expected to realize the recognition of no less than eight hand movement intentions with the recognition accuracy of ≥ 90% and the response time ≤ 300 ms. Additionally, we are looking forward to, within five years, letting millions of stroke patients use this product with their lives improved after stoke.

In NCyborg ProjectN means Neural and Cyborg means a system of biological and technical mixed type. In fictional stories, Cyborg is often claimed as icon that is enhanced mentally and/or physically over and above the “norm” with technology. In the real word, we believe that NCyborg Project based on brain computer interface and brain-inspired intelligent robots will set up a brand new stroke rehabilitation pattern which could qualitatively improve the treatment effect of stroke survivors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

1 Online access website: https://bdk.ucas.ac.cn/index.php/xyxw/2780-20190113.Google Scholar

2 Online access website: https://time.com/collection/best-inventions-2019/5733081/brainrobotics-ai-prosthetic-hand/.Google Scholar

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[Abstract] Over-ground robotic lower limb exoskeleton in neurological gait rehabilitation: User experiences and effects on walking ability

Abstract

BACKGROUND: Over-ground robotic lower limb exoskeletons are safe and feasible in rehabilitation with individuals with spinal cord injury (SCI) and stroke. Information about effects on stroke rehabilitees is scarce and descriptions of learning process and user experience is lacking.

OBJECTIVE: The objectives of this study were to describe how rehabilitees learn exoskeleton use, to study effects of exoskeleton assisted walking (EAW) training, and to study rehabilitees’ user experiences.

METHODS: One-group pre-test post-test pre-experimental study involved five rehabilitees with stroke or traumatic brain injury (TBI). Participants in chronic phase underwent twice a week an 8-week training intervention with Indego exoskeleton. Process of learning to walk and the level of assistance were documented. Outcome measurements were conducted with 6-minute and 10-meter walk tests (6 MWT, 10 mWT). User experience was assessed with a satisfaction questionnaire.

RESULTS: Rehabilitees learnt to walk using the exoskeleton with the assistance from 2–3 therapists within two sessions and progressed individually. Three participants improved their results in 10 mWT, four in 6 MWT. The rehabilitees felt comfortable and safe when using and exercising with the device.

CONCLUSION: Indego exoskeleton may be beneficial to gait rehabilitation with chronic stroke or TBI rehabilitees. The rehabilitees were satisfied with the exoskeleton as a rehabilitation device.

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[Abstract] Machine Learning in Robot Assisted Upper Limb Rehabilitation: A Focused Review

Abstract

Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.

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