An FRR is developed for active and passive training using two PMs and an MR damper.
An underactuated mechanism is proposed for independent training of all finger joints.
Modelling of kinematics, statics and dynamics of the FRR is presented.
The motion and force properties of the FRR are experimentally evaluated.
This paper presents the development of a finger rehabilitation robot (FRR) for active and passive training to fulfill the requirements of different rehabilitation stages. In the design, an antagonistic pair of pneumatic muscles (PMs) are utilized to exert a bidirectional force for passive training, and a controllable magnetorheological (MR) damper is used to provide a damping force for active training. In this paper, first, a detailed illustration of the mechanical design of the FRR, including the driving, transmission and actuating mechanisms, and the damping device, is presented. Subsequently, the kinematic analysis and simulation are described, followed by the static and dynamic analysis of the designed FRR. This paper details the static force transfer of the transmission mechanism, and the establishment of dynamic equations for the passive training system. Finally, an experimental set-up is established, and several passive and active training experiments are conducted for the performance evaluation of the FRR prototype. The results validate the feasibility and stability of the developed FRR.
According to the structural design requirements of upper limb rehabilitation robot, the static analysis and harmonic response analysis are made in this paper. The structure of the upper limb rehabilitation robot is simplified in different ways according to the importance of each part of the structure. Selecting different element types and establishing finite element model Based on its analysis results, some unnecessary parts are removed and simplified, and the main forced parts are optimized by defining parameters to optimize and re-design the structure. The static analysis and harmonious response analysis of the modified model, comparing the structure before and after optimization, show that the modified structural mechanical properties are obviously improved and the design requirements are fully met.
A frequent consequence of stroke is a limited hand function. Numerous studies have shown, that repetitive passive training enhances the rehabilitation process. As there are high anthropometric variances in hand and finger anatomy, this contribution presents a custom-made hand rehabilitation robot. The individual design is proposed to ensure an ergonomic interface which allows long-time wearing. To provide a cost-effective production, we present an automated design process. The individual fingers are manufactured monolithically using the selective laser sintering of polyamide. The presented device is portable and can be used for training as well as for grasping things.
Proprioception or body awareness is an essential sense that aids in the neural control of movement. Proprioceptive impairments are commonly found in people with neurological conditions such as stroke and Parkinson’s disease. Such impairments are known to impact the patient’s quality of life. Robot-aided proprioceptive training has been proposed and tested to improve sensorimotor performance. However, such robot-aided exercises are implemented similar to many physical rehabilitation exercises, requiring task-specific and repetitive movements from patients. Monotonous nature of such repetitive exercises can result in reduced patient motivation, thereby, impacting treatment adherence and therapy gains. Gamification of exercises can make physical rehabilitation more engaging and rewarding. In this work, we discuss our ongoing efforts to develop a game that can accompany a robot-aided wrist proprioceptive training exercise.
Proprioception, the sense of body awareness, is essential for normal motor function. Proprioceptive deficits are common in neurological conditions [Coupar et al. 2012; Konczak et al. 2009]. Such deficits cause a decline in precision of goal-directed movements, and altered postural and spinal reflexes resulting in balance and gait problems [Rothwell et al. 1982]. Proprioceptive training is an intervention aiming to improve proprioceptive function [Aman et al. 2015]. Previous work has established the efficacy of a robot-aided proprioceptive training using WristBot [Elangovan et al. 2017, 2018, 2019]. The WristBot (Figure 1. Left) is a three degrees-of-freedom (3-DoF) exoskeleton robot that allows full range of motion (ROM), delivers precise haptic, position, and velocity stimuli at the wrist, and accurately encodes wrist position across time. Additional details about the WristBot can found in [Cappello et al. 2015].
Nevertheless, while the WristBot has demonstrated its efficacy, it shares a limitation that is often encountered in rehabilitation settings. In a clinical setting, patients are often required to perform task-specific and repetitive movements [Kwakkel et al. 1999]. Initial patient enthusiasm to complete such activities rapidly declines as a result of the monotonous nature of movements. Patient engagement can be improved by complementing therapy with a virtual environment (VE). Prior research has shown that users have favored exercises complemented with a VE rather than conventional approaches [Hoffman et al. 2014]. Thus, our project objective is to turn these tedious movements into an interactive VE experience.
2 GAMIFICATION OF PROPRIOCEPTIVE TRAINING
Gamification process accounted for two key considerations: (1) the game should foster patient motivation and attention (2) and be clinically meaningful. To address these objectives, we reviewed the literature on game development [Bond 2014; Fullerton 2018] and identified four essential components: (1) Variability, (2) Feedback, (3) Rewards, and (4) a Compelling Purpose. The user will be gradually exposed to increasing levels of difficulty, which will likely reduce user frustrations. The user will receive meaningful feedback on concurrent metrics (e.g., Optimal ROM), as well as on previous treatment sessions. During game progress, the user will be alerted about deviations from the target movement requirements. Achievement badges will be rewarded to the user upon reaching therapy milestones, such as target ROM. Lastly, to encourage game completion, we establish an interesting backstory and a meaningful character arc for our virtual avatars. The developed game will be adaptable based on the user’s current clinical status, thus, making the game clinically meaningful. The clinician will have the ability to prescribe exercises based on user needs such as 1 DoF vs 3 DoF movements, continuous vs discrete movements, and strength training vs mobility training. WristBot will provide supportive forces aiding the user to achieve therapy milestones.
Gamified exercise is being developed using the Unity Game Engine, Python and libraries which interface with the WristBot. The game closely resembles an endless runner type game (Figure 1. Right) and utilizes the WrsitBot’s 3-DoF functionality to interact with the VE. Wrist flexion, extension, and abduction can be used to traverse their environment. The remaining 3 movements will allow interactions with their VE in unique ways, such as opening/closing doors, crouching, and pulling levers. In the VE, coins are strategically placed to maximize and improve the use of available ROM. Upon contact with either a wall or obstacle, visual feedback will be provided in the form of avatar damage and coin deduction. Consequently, users achieve improved mobility.
In Python, the connection between Unity and the WristBot library is managed through the use of a local WebSocket, a protocol for two-way communication over a single Transmission Control Protocol (TCP) connection [Fette and Melnikov 2011]. Through the WebSocket, reciprocal data are transferred between the WristBot and Unity. For example, wrist kinematic data will be streamed to the game while game progress is being relayed to the WristBot library. Game progress data will be utilized to compute and deliver haptic feedback to the user. Haptic feedback provided in the form of haptic assistance will aid users to improve their available ROM, while haptic resistance will improve muscle strength within the desired ROM. The clinical motive of the game is to transition the user from use of haptic assistance to resistance during game play. WristBot will adapt haptic feedback based on time spent and progress achieved in game play.
3 USABILITY TESTING
Usability testing will be conducted to ensure proper game usage by the clinical population and healthcare professionals. Specifically, the usability testing will evaluate areas such as 1) ease of game play, 2) game efficiency, and 3) user engagement. We will test the assumptions in each of these areas are accurately depicted in game development and met during game play. For example, we expect online visual feedback of deviations from target to help user focus on achieving the movement requirements. The users will be asked to verify the benefits of visual feedback in modifying their movements. Similarly, other assumptions such as performance badges and coins as rewards, and increase in difficulty levels will be evaluated. A common pitfall of usability studies involving physical rehabilitation setting is not recruiting from the representative population, most notably elderly population [Laver et al. 2017] as age has been shown to interfere with interactions in VE [Meldrum et al. 2012]. Therefore, to ensure our game is intuitive, we will recruit representative users from our patient populations.
This project was supported by National Science Foundation Partnerships For Innovation Technology Translation Award to Jürgen Konczak (1919036). Christopher Curry was supported by National Research Trainee-Understanding the Brain: Graduate Training Program in Sensory Science: Optimizing the Information Available for Mind and Brain (1734815).
Joshua E Aman, Naveen Elangovan, I Yeh, Jürgen Konczak, et al. 2015. The effectiveness of proprioceptive training for improving motor function: a systematic review. Frontiers in human neuroscience 8 (2015), 1075.
Jeremy Gibson Bond. 2014. Introduction to Game Design, Prototyping, and Development: From Concept to Playable Game with Unity and C. Addison-Wesley Professional.
Leonardo Cappello, Naveen Elangovan, Sara Contu, Sanaz Khosravani, Jürgen Konczak, and Lorenzo Masia. 2015. Robot-aided assessment of wrist proprioception. Frontiers in human neuroscience 9 (2015), 198.
Fiona Coupar, Alex Pollock, Phil Rowe, Christopher Weir, and Peter Langhorne. 2012. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clinical rehabilitation 26, 4 (2012), 291–313.
Naveen Elangovan, Leonardo Cappello, Lorenzo Masia, Joshua Aman, and Jürgen Konczak. 2017. A robot-aided visuo-motor training that improves proprioception and spatial accuracy of untrained movement. Scientific reports 7, 1 (2017), 17054.
Naveen Elangovan, Paul Tuite, and Jürgen Konczak. 2018. Somatosensory training improves proprioception and untrained motor function in Parkinson’s disease. Frontiers in neurology 9(2018), 1053.
Naveen Elangovan, I-Ling Yeh, Jessica Holst-Wolf, and Jürgen Konczak. 2019. A robot-assisted sensorimotor training program can improve proprioception and motor function in stroke survivors. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 660–664.
I. Fette and A. Melnikov. 2011. The WebSocket Protocol. Technical Report.
Tracy Fullerton. 2018. Game design workshop: a playcentric approach to creating innovative games. AK Peters/CRC Press.
Hunter G Hoffman, Walter J Meyer III, Maribel Ramirez, Linda Roberts, Eric J Seibel, Barbara Atzori, Sam R Sharar, and David R Patterson. 2014. Feasibility of articulated arm mounted Oculus Rift Virtual Reality goggles for adjunctive pain control during occupational therapy in pediatric burn patients. Cyberpsychology, Behavior, and Social Networking 17, 6(2014), 397–401.
Jürgen Konczak, Daniel M Corcos, Fay Horak, Howard Poizner, Mark Shapiro, Paul Tuite, Jens Volkmann, and Matthias Maschke. 2009. Proprioception and motor control in Parkinson’s disease. Journal of motor behavior 41, 6 (2009), 543–552.
Gert Kwakkel, Boudewijn J Kollen, and Robert C Wagenaar. 1999. Therapy impact on functional recovery in stroke rehabilitation: a critical review of the literature. Physiotherapy 85, 7 (1999), 377–391.
Kate E Laver, Belinda Lange, Stacey George, Judith E Deutsch, Gustavo Saposnik, and Maria Crotty. 2017. Virtual reality for stroke rehabilitation. Cochrane database of systematic reviews11 (2017).
Dara Meldrum, Aine Glennon, Susan Herdman, Deirdre Murray, and Rory McConn-Walsh. 2012. Virtual reality rehabilitation of balance: assessment of the usability of the Nintendo Wii® Fit Plus. Disability and rehabilitation: assistive technology 7, 3 (2012), 205–210.
JC Rothwell, MM Traub, BL Day, JA Obeso, PK Thomas, and CD Marsden. 1982. Manual motor performance in a deafferented man. Brain 105, 3 (1982), 515–542.
The rehabilitation effects of the NMES robotic hand and robotic hand were compared.
Both training systems could significantly improve the motor function of upper limb.
The NMES robot was more effective than the pure robot.
NMES applied on distal muscle could benefit the recovery in the entire upper limb.
To compare the rehabilitation effects of the electromyography (EMG)-driven neuromuscular electrical stimulation (NMES) robotic hand and EMG-driven robotic hand for chronic stroke.
This study was a randomized controlled trial with a 3-month follow-up. Thirty chronic stroke patients were randomly assigned to receive 20-session upper limb training with either EMG-driven NMES robotic hand (NMES group, n = 15) or EMG-driven robotic hand (pure group, n = 15). The training effects were evaluated before and after the training, as well as 3 months later, using the clinical scores of Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Action Research Arm Test (ARAT), and Functional Independence Measure (FIM). Session-by-session EMG parameters, including the normalized EMG activation level and co-contraction indexes (CIs) of the target muscles were applied to monitor the recovery progress in muscular coordination patterns.
Both groups achieved significantly increased FMA and ARAT scores (p < 0.05), and the NMES group improved more (p < 0.05). A significant improvement in MAS was obtained in the NMES group (p < 0.05) but absence in the pure group. Meanwhile, better performance could be obtained in the NMES group in releasing the EMG activation levels and CIs than the pure group across the training sessions (p < 0.05).
Both training systems were effective in improving the long-term distal motor functions in upper limb, where the NMES robot-assisted training achieved better voluntary motor recovery and muscle coordination and more release in muscle spasticity.
This study indicated more effective distal rehabilitation using the NMES robot than the pure robot-assisted rehabilitation.
Upper limb motor deficits are common after stroke, and observed in over 80% of stroke survivors [1,2]. Various rehabilitation devices have been purposed to assist human physical therapists to provide effective long-term rehabilitation programs [, , ]. Among them, rehabilitation robots and neuromuscular electrical stimulation (NMES) are most widely used in stroke rehabilitation practices. Rehabilitation robots have been recognized as efficient in such cases and could represent a cost-effective addition to conventional rehabilitation services because they provide highly intensive and repetitive training [, , , ]. It has been reported that the integration of voluntary effort (e.g. electromyography, EMG) into robotic design could contribute significantly to motor recovery in stroke patients [6,10]. This is because an EMG-driven strategy can maximize the involvement of voluntary effort in the training, and its effectiveness at improving upper limb voluntary motor functions have been proved by many EMG-driven robot-assisted upper-limb training systems [, , ]. However, rehabilitation robots are unable to directly activate the desired muscle groups, which may only assist, or even dominate limb movement such as continuous passive motions (CPM) . In addition, stroke patients usually cooperate with compensatory motions from other muscular activities to activate the target muscles, which may lead to ‘learned disuse’ . However, NMES can effectively limit compensatory motions by stimulating specific muscles via cyclic electrical currents, which provides repetitive sensorimotor experiences . With the advantage of precisely activating the target muscle, NMES has been reported to be effective in evoking sensory feedback, improving muscle force, and thus promoting motor function in stroke patients [17,18]. Nevertheless, training programs assisted by NMES alone are also suboptimal due to the difficulty of controlling movement trajectories and the early appearance of fatigue [19,20].
Accordingly, various NMES robot-assisted upper-limb training programs which combine these two unique techniques have been proposed to integrate the benefits and minimize the disadvantages [7,12,14,21,22]. The rehabilitation effectiveness of these combined systems has been investigated and reported to be effective in improving motor recovery. Several studies have compared the training outcomes of NMES robot-assisted training and other training programs. For example, Qian et al.  reported that NMES-robot-assisted upper-limb training could achieve better motor outcomes when compared with conventional therapies for subacute stroke patients. Meanwhile, another study which compared the training effects between robot-aided training with NMES and robot-aided training solely using the InMotion ARM™ Robot in the subacute period demonstrated that the active ranges of motion of the NMES robot-training group were significantly higher compared with the robot-training group . Coincidentally, investigations into applications in chronic stroke patients have also been carried out. For instance, Hu et al.  proposed an EMG-driven NMES robot system for wrist training; this combined device improved muscle activation levels related to the wrist and reduced compensatory muscular activities at the elbow, while these training outcomes were absent for the EMG-driven robot-assisted training alone. Indeed, a similar study by another research group also achieved better rehabilitation outcomes on some clinical assessments using the combined system compared to robot-assisted therapy alone .
In the literature, most studies on current rehabilitation devices combining the NMES and robotic systems targeted the elbow and wrist joints [7,, , ], while very few focused on the hand and fingers . In addition, a comparison of the training effects for hand rehabilitation between the NMES robot and other hand rehabilitation devices has not yet been adequately conducted. Indeed, the primary upper-limb disability post-stroke is the loss of hand function, and rehabilitation of the distal joints after stroke is much more difficult than the motor recovery of the proximal joints due to the compensatory motions from the proximal joints . Hence, developing effective rehabilitation devices to minimize compensatory movements for hand motor recovery is especially meaningful for stroke rehabilitation. In our previous work, we developed an EMG-driven NMES robotic hand and suggested it for use in hand rehabilitation after stroke . Our device provides fine control of hand movements and activates the target muscles selectively for finger extension/flexion, and its feasibility and effectiveness have been verified by a single group trial . However, whether the long-term rehabilitation effect of this EMG-driven NMES robotic hand is comparable or even better than other hand rehabilitation devices are still unclear and need to be investigated quantitively. Therefore, the objective of this study is to compare the training effects of hand rehabilitation assisted by an NMES robotic hand and by a pure robotic hand though a randomized controlled trial with a 3-month follow-up (3MFU).
This work was approved by the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University. A total of 53 stroke survivors were screened for the training from local districts. 30 participants with chronic stroke satisfied the following inclusion criteria: (1) The participants were at least 6 months after the onset of a singular and unilateral brain lesion due to stroke, (2) both the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints could be extended to 180° passively, (3) muscle spasticity during extension at the finger joints and the wrist joint was below 3 as measured by the Modified Ashworth Scale (MAS) , ranged from 0 (no increase in muscle tone) to 4 (affected part rigid), (4) detectable voluntary EMG signals from the driving muscle on the affected side (three times of the standard deviation (SD) above the EMG baseline), and (5) no visual deficit and able to understand and follow simple instructions as assessed by the Mini-Mental State Examination (MMSE > 21) .
This work involved a randomized controlled trial with a 3-month follow-up (3MFU). The potential participants were first told that the training program they would receive could be either NMES robotic hand training or pure robotic hand training, and all recruited participants submitted their written consent before randomization. Then, the recruited participants were randomly assigned into two groups according to a computer-based random number generator, i.e., the computer program generated either “1” (denoting the NMES robotic hand training group) or “2” (the pure robotic hand group) with an equal probability of 0.5 (Matlab, 2017, Mathworks, Inc.). Fig. 1 shows the Consolidated Standards of Reporting Trials flowchart of the training program.
For both groups, each participant was invited to attend a 20-session robotic hand training with an intensity of 3–5 sessions/week, completed within 7 consecutive weeks. The training setup of both groups is shown in Fig. 2. This robotic hand training system can assist with finger extension and flexion of the paretic limb for patients after stroke. In this work, real-time voluntary EMG detected from the abductor pollicis brevis (APB) and extensor digitorum (ED) muscles were used to control the respective hand closing and opening movements, and the threshold level of each motion phase was set at three times the SD above the EMG baseline at resting state . For example, during the motions of finger flexion, once the EMG activation level of the APB muscle reached a preset threshold, the robotic hand would provide mechanical assistance for hand closing. Similarly, during the motions of finger extension, the robotic hand would assist with hand opening when the EMG activation level of the ED muscle reached a preset threshold. For the NMES robot group, synchronized support from the NMES and the robot were both provided. The NMES electrode pair (30 mm diameter; Axelgaard Corp., Fallbrook, CA, USA) was attached over the ED muscle to provide stimulation during finger extension. The outputs of NMES were square pulses with a constant amplitude of 70 V, a stimulation frequency of 40 Hz, and a manually adjustable pulse width in the range 0–300 μs. Before the training, the pulse width was set at the minimum intensity, which achieved a fully extended position of the fingers in each patient. During the training, NMES would be triggered by the EMG from the ED muscle first and then provided stimulation to the ED muscle to assist hand-opening motions for the entire phase of finger extension, while no assistance from NMES was provided during finger flexion to avoid the possible increase of finger spasticity after stimulation . For the pure robot group, the difference between the two groups was that no NMES was applied in the pure robot group. A detailed account of the working principles of the robotic hand have been described in our previous work [12,30,31].
Background. Robot-assisted therapy provides high-intensity arm rehabilitation that can significantly reduce stroke-related upper extremity (UE) deficits. Motor improvement has been shown at the joints trained, but generalization to real-world function has not been profound.
Objective. To investigate the efficacy of robot-assisted therapy combined with therapist-assisted task training versus robot-assisted therapy alone on motor outcomes and use in participants with moderate to severe chronic stroke-related arm disability.
Methods. This was a single-blind randomized controlled trial of two 12-week robot-assisted interventions; 45 participants were stratified by Fugl-Meyer (FMA) impairment (mean 21 ± 1.36) to 60 minutes of robot therapy (RT; n = 22) or 45 minutes of RT combined with 15 minutes therapist-assisted transition-to-task training (TTT; n = 23). The primary outcome was the mean FMA change at week 12 using a linear mixed-model analysis. A subanalysis included the Wolf Motor Function Test (WMFT) and Stroke Impact Scale (SIS), with significance P <.05.
Results. There was no significant 12-week difference in FMA change between groups, and mean FMA gains were 2.87 ± 0.70 and 4.81 ± 0.68 for RT and TTT, respectively. TTT had greater 12-week secondary outcome improvements in the log WMFT (-0.52 ± 0.06 vs -0.18 ± 0.06; P = .01) and SIS hand (20.52 ± 2.94 vs 8.27 ± 3.03; P = .03).
Conclusion. Chronic UE motor deficits are responsive to intensive robot-assisted therapy of 45 or 60 minutes per session duration. The replacement of part of the robotic training with nonrobotic tasks did not reduce treatment effect and may benefit stroke-affected hand use and motor task performance.
Background: Robot-assisted therapy has become a promising technology in the field of rehabilitation for poststroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for most stroke survivors. With current advancements in technology there has been the introduction of virtual reality (VR), augmented reality (AR), customizable games, or a combination thereof, that aid robotic therapy in retaining, or increasing the interests of, patients so they keep performing their exercises. However, there are gaps in the evidence regarding the transition from clinical rehabilitation to home-based therapy which calls for an updated synthesis of the literature that showcases this trend. The present review proposes a categorization of these studies according to technologies used, and details research in both upper limb and lower limb applications.
Objective: The goal of this work was to review the practices and technologies implemented in the rehabilitation of poststroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies (VR, AR, or gamification) in improving activity and participation in poststroke survivors.
Methods: A systematic search of the literature on exoskeleton robotics applied with any of the three technologies of interest (VR, AR, or gamification) was performed in the following databases: MEDLINE, EMBASE, Science Direct & The Cochrane Library. Exoskeleton-based studies that did not include any VR, AR or gamification elements were excluded, but publications from the years 2010 to 2017 were included. Results in the form of improvements in the patients’ condition were also recorded and taken into consideration in determining the effectiveness of any of the therapies on the patients.
Results: Thirty studies were identified based on the inclusion criteria, and this included randomized controlled trials as well as exploratory research pieces. There were a total of about 385 participants across the various studies. The use of technologies such as VR-, AR-, or gamification-based exoskeletons could fill the transition from the clinic to a home-based setting. Our analysis showed that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation.
Conclusions: Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the individual therapies.
Stroke refers to a sudden, often catastrophic neurological event that can lead to long-term adult disability. The American Heart Association (AHA) is responsible for providing up-to-date statistics related to heart disease and stroke. According to Benjamin et al , the AHA released a 2017 statistics report on heart disease and stroke that stated that approximately 795,000 stroke episodes occur in the US each year. With current advancements in medical technology there has been a decrease in the rate of stroke incidents, but it can still cause paralysis and muscle weakness. Such impairments can result in motor deficits that disturb a stroke survivor’s capacity to live independently.
There are several reasons for stroke occurrence, which could be related to an increased risk of a collection of symptoms caused by disorders affecting the brain (eg, dementia) . Various rehabilitation techniques have been used in the area of rehabilitation-based interactive technology to assist patients in recovering from impairments, and those techniques come under the umbrella of conventional therapy, exoskeleton or robot-aided therapy, virtual reality (VR) or augmented reality (AR) therapy, games-based therapy, or a combination of any of these. These forms of therapy can be done either in the clinic or in an in-home setting. In addition to these, there is a new technology known as telerehabilitation  that leverages the use of VR in home settings by providing patients access to real-time rehabilitation services through the internet while they sit at home.
One of the most effective techniques is robot-aided therapy, which has been gradually increasing in use primarily because patients may consider traditional rehabilitation therapy to be tiring and exhaustive. This may decrease their motivation and cohesion to the treatment, thus resulting in only minor improvement in the health of poststroke patients [4–6]. Various experimental evidence suggests that robot-assisted (or exoskeleton) rehabilitation has been effective in keeping patients motivated and interested in treatment for both upper or lower limb impairments [7,8]. With advancements in technology, there has also been an uptake of VR, AR, and Gamification for the purposes of rehabilitation , along with robotic rehabilitation [10,11], primarily to increase engagement, immersion and motivation on behalf of the patient. Both Colombo et al and Alankus et al [12,13] concluded and showed the positive effect of exoskeleton robots and games in poststroke rehabilitation. Wearable devices such as exoskeletons can also relay real-time feedback for any VR-based interactions .
Apart from these studies, Housman et al  showed user satisfaction survey results in which 90% of participants agreed to the fact that robot- or games-assisted therapies were less confusing, and improvements were very easy to track compared to traditional or conventional therapies. Further, it is thought that gamification can increase repetition, engagement, and range of care within the context of rehabilitation [16,17]. Games are not only useful for the field of rehabilitation, but they are also considered to be highly impactful and relevant in other medical and health fields. Russoniello et al  conducted a randomized controlled trial (RCT) study in which the effects of video games on stress-related disorders were tested, with the conclusion being that games were beneficial for their prevention and treatment. In another study, children who had cerebral palsy made use of a game (EyeToy) which was able to improve their upper extremity functions over time .
To demonstrate the feasibility of algorithmic prediction utilizing a model of baseline arm movement, genetic factors, demographic characteristics, and multi-modal assessment of the structure and function of motor pathways. To identify prognostic factors and the biological substrate for reductions in arm impairment in response to repetitive task practice.
This prospective single-group interventional study seeks to predict response to a repetitive task practice program using an intent-to-treat paradigm. Response is measured as a change of ≥5 points on the Upper Extremity Fugl-Meyer from baseline to final evaluation (at the end of training).
Anticipated enrollment of 96 community-dwelling adults with chronic stroke (onset ≥6 months) and moderate to severe residual hemiparesis of the upper limb as defined by a score of 10-45 points on the Upper Extremity Fugl-Meyer.
The intervention is a form of repetitive task practice using a combination of robot-assisted therapy coupled with functional arm use in real-world tasks administered over 12 weeks.
Main outcome measures
Upper extremity Fugl-Meyer Assessment (primary outcome), Wolf Motor Function Test, Action Research Arm Test, Stroke Impact Scale, questionnaires on pain and expectancy, magnetic resonance imaging, transcranial magnetic stimulation, arm kinematics, accelerometry, and a saliva sample for genetic testing.
Methods for this trial are outlined and an illustration of inter-individual variability is provided by example of two participants who present similarly at baseline but achieve markedly different outcomes.
This article presents the design, methodology, and rationale of an ongoing study to develop a predictive model of response to a standardized therapy for stroke survivors with chronic hemiparesis. Applying concepts from precision medicine to neurorehabilitation is practicable and needed to establish realistic rehabilitation goals and to effectively allocate resources.
The rehabilitation process of human fingers is a coupling movement of wearable hand rehabilitation equipment and human fingers, and its design must be based on the kinematics of human fingers. In this paper, the forward kinematics and inverse kinematics models are established for the index finger. Kinematics analysis is carried out. Then a bionic finger rehabilitation robot is designed according to the movement characteristics of the finger, A parallelogram linkage mechanism is proposed to make the joint independent drive, realize the flexion/extension movement, and perform positive kinematics and inverse kinematics analysis on the mechanism. The results show that it conforms to the kinematics of the index finger and can be used as the mechanism model of the finger rehabilitation robot.
1. Ibrahim Yildiz, “A Low-Cost and Lightweight Alternative to Rehabilitation Robots: Omnidirectional Interactive Mobile Robot for Arm Rehabilitation” in Arabian Journal for Science & Engineering, Springer Science & Business Media B.V., vol. 43, no. 3, pp. 1053-1059, 2018.
6. Wu Hongjian, Li Lina, Li Long, Liu Tian, Jue Wang, “Review of comprehensive intervention by hand rehabilitation robot after stroke [J]”, Journal of biomedical engineering, vol. 36, no. 01, pp. 151-156, 2019.
9. N A I M Rosli, M A A Rahman, S A Mazlan et al., “Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application[C]”, IEEE Student Conference on Research and Development, pp. 1-5, 2015.
10. K O Thielbar, K M Triandafilou, H C Fischer et al., “Benefits of using a voice and EMG- Driven actuated glove to support occupational therapy for stroke survivors”, IEEE Trans Neural Syst Rehabil Eng, vol. 25, no. 3, pp. 297-305, 2017.
Traditional rigid robots exist many problems in rehabilitation training. Soft robotics is conducive to breaking the limitations of rigid robots. This paper presents a soft Rehabilitation training, Soft robot, Pneumatic actuator device for the rehabilitation of hands, including soft pneumatic actuators that are embedded in the device for motion assistance. The key feature of this design is the stiffness of each actuator at different positions is different, which results in the bending posture of the actuator is more accordant with the bending figure of human hand. In addition, another key point is the use of a fabric sleeves allow actuators to gain greater bending force when pressurized, which gives the hand greater bending force. We verified the feasibility of actuator through simulation, the performance of soft actuator and the device also are evaluated through experiments. Finally, the results show that this device can finish some of the hand rehabilitation tasks.
Yap, H.K., Lim, J.H., Goh, J.C.H., et al.: Design of a soft robotic glove for hand rehabilitation of stroke patients with clenched fist deformity using inflatable plastic actuators. J. Med. Devices 10(4), 044504 (2016)CrossRefGoogle Scholar
Kemna, S., Culmer, P.R., Jackson, A.E., et al.: Developing a user interface for the iPAM stroke rehabilitation system. In: IEEE International Conference on Rehabilitation Robotics, pp. 879–884. IEEE (2009)Google Scholar
Cai, Z., Tong, D., Meadmore, K.L., et al.: Design & control of a 3D stroke rehabilitation platform. In: IEEE International Conference on Rehabilitation Robotics (2011). 5975412Google Scholar
Lum, P.S., Burgar, C.G., Van der Loos, M.: MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: a follow-up study. J. Rehabil. Res. Dev. 43(5), 631 (2006)CrossRefGoogle Scholar
Pehlivan, A.U., Celik, O., O’Malley, M.K.: Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation. In: IEEE International Conference on Rehabilitation Robotics. IEEE (2011). 5975428Google Scholar
Polygerinos, P., Wang, Z., Galloway, K.C., et al.: Soft robotic glove for combined assistance and at-home rehabilitation. Robot. Auton. Syst. 73(C), 135–143 (2015)CrossRefGoogle Scholar
Yap, H.K., Lim, J.H., Nasrallah, F., et al.: MRC-glove: A fMRI compatible soft robotic glove for hand rehabilitation application. In: IEEE International Conference on Rehabilitation Robotics, pp. 735–740. IEEE (2015)Google Scholar
Yap, H.K., Lim, J.H., Nasrallah, F., et al.: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: IEEE International Conference on Robotics and Automation, pp. 4967–4972. IEEE (2015)Google Scholar
Yap, H.K., Khin, P.M., Koh, T.H., et al.: A fully fabric-based bidirectional soft robotic glove for assistance and rehabilitation of hand impaired patients. IEEE Robot. Autom. Lett. PP(99), 1 (2017)Google Scholar
Mosadegh, B., Polygerinos, P., Keplinger, C., et al.: Soft robotics: pneumatic networks for soft robotics that actuate rapidly. Adv. Funct. Mater. 24(15), 2109 (2014)CrossRefGoogle Scholar
Galloway, K.C., Polygerinos, P., Walsh, C.J., et al.: Mechanically programmable bend radius for fiber-reinforced soft actuators. In: International Conference on Advanced Robotics. IEEE (2014)Google Scholar
Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Front. Neurosci. 11, 547 (2017)CrossRefGoogle Scholar
Yap, H.K., Ang, B.W., Lim, J.H., et al.: A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. In: IEEE International Conference on Robotics and Automation, pp. 3537–3542. IEEE (2016)Google Scholar
Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Characterisation and evaluation of soft elastomeric actuators for hand assistive and rehabilitation applications. J. Med. Eng. Technol. 40, 1–11 (2016)CrossRefGoogle Scholar
Tong, M.: Design, Modeling and Fabrication of a Massage Neck Support Using Soft Robot Mechanis. The Ohio State University (2014)Google Scholar
Aubin, P.M., Sallum, H., Walsh, C., et al.: A pediatric robotic thumb exoskeleton for at-home rehabilitation: the Isolated Orthosis for Thumb Actuation (IOTA). In: Proceedings of IEEE International Conference on Rehabilitation Robotics, pp. 1–6 (2013)Google Scholar