TAO Quan. Hand rehabilitation [M]. Shanghai: Shanghai Jiao Tong University Press, 2006. (in Chinese)Google Scholar
KERBS H I, VOLPE B T, AISEN M L, et al. Increasing productivity and quality of care: Robot-aided neuro-rehabilitation[J]. Journal of Rehabilitation Research and Development, 2000, 37(6): 639–652.Google Scholar
LU Guangming, SUN Lining, PENG Longgang. Analysis of the status and the key technology of the robot technology for rehabilitation[J]. Journal of Harbin Institute of Technology, 2004, 36(9): 1 224–1 231.Google Scholar
DOVAT L, LAMBERCY O, JOHNSON V, et al. A cable driven robotic system to train finger function after stroke[C]// Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, Netherlands, June 13–15, 2007: 222–227.Google Scholar
IMMERSION. CyberGrasp™ User’s Guide [EB/OL]. 2009-12-07 [2001-04-20]. http://www.cyberglovesystems.com/products/cybergrasp/overview
LELIEVELD M J, MAENO T, TOMIYAMA T. Design and development of two concepts for a 4 DOF portable haptic interface with active and passive multi-point force feedback for the index finger[C]// ASME International Design Engineering Technical Conference & Computers and Information in Engineering Conference, Philadelphia, Pennsylvania, USA, September 10–13, 2006: 1–10.Google Scholar
FU Yili, WANG Peng, WANG Shuguo, et al. Design and development of a portable exoskeleton based CPM machine for rehabilitation of hand injuries[C]// IEEE International Conference on Robotics and Biomimetics, Sanya, China, December 15–18, 2007:1 476–1 481.Google Scholar
WEGE A, KONDAK K, HOMMEL G. Mechanical design and motion control of a hand exoskeleton for rehabilitation[C]// IEEE International Conference on Mechatronics and Automation, Niagara Falls, Ontario, Canada, July 29–August 1, 2005: 155–159.Google Scholar
WORSNOPP T T, PESHKIN M A, COLGATE J E, et al. An actuated finger exoskeleton for hand rehabilitation following stroke[C]//IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk aan Zee, Netherlands, June 13–15, 2007: 896–901.Google Scholar
YAMAURA H, MATSUSHITA K, KATO R, et al. Development of hand rehabilitation system for paralysis patient-Universal design using wire-driven mechanism[C]//31th Annual International conference of the IEEE-EMBS, MN, USA, September 2–6, 2009:7 122–7 125.Google Scholar
CHIRI A, GIOVACCHINI F, VITIELLO N, et al, HANDEXOS: towards an exoskeleton device for the rehabilitation of the hand[C]// The 2009 IEEE/RSJ International Conf. on Intelligent Robots and Systems, St. Louis, USA, October 11–15, 2009: 1 106–1 111.Google Scholar
MOURI T, KAWASAKI H, NISHIMOTO Y, et al. Development of robot hand for therapist education/training on rehabilitation[C]// Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, Oct. 29–Nov.2, 2007: 2 295–2 300.Google Scholar
WANG Ju, LI Jiting, ZHANG Yuru, et al. Design of an exoskeleton for index finger rehabilitation[C]// Proceedings of the 31th Annual International Conference of the IEEE EMBS, Minneapolis, MN, USA, September 2–6, 2009: 5 957–5 960.Google Scholar
ZHANG Yuru, LI Jiting, LI Jianfeng. Robot dexterous hand: Modeling, Planning and Simulation[M]. Beijing: China Machine Press, 2007.Google Scholar
Posts Tagged robot
[Abstract + References] Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties
This paper investigates the passive and active control strategies to provide a physical assistance and rehabilitation by a 7-DOF exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user’s physiological characteristics. An Integral backstepping controller incorporated with Time Delay Estimation (BITDE) is used, which permits the exoskeleton robot to achieve the desired performance of working under the mentioned uncertainties constraints. Time Delay Estimation (TDE) is employed to estimate the nonlinear uncertain dynamics of the robot and the unknown disturbances. To overcome the limitation of the time delay error inherent of the TDE approach, a recursive algorithm is used to further reduce its effect. The integral action is employed to decrease the impact of the unmodeled dynamics. Besides, the Damped Least Square method is introduced to estimate the desired movement intention of the subject with the objective to provide active rehabilitation. The controller scheme is to ensure that the robot system performs passive and active rehabilitation exercises with a high level of tracking accuracy and robustness, despite the unknown dynamics of the exoskeleton robot and the presence of unknown bounded disturbances. The design, stability, and convergence analysis are formulated and proven based on the Lyapunov–Krasovskii functional theory. Experimental results with healthy subjects, using a virtual environment, show the feasibility, and ease of implementation of the control scheme. Its robustness and flexibility to deal with parameter variations due to the unknown external disturbances are also shown.
University of Michigan researchers have designed a low-cost, portable arm rehabilitation robot, which they suggest can be used at home and facilitate motor recovery in patients with cerebral palsy, stroke, or spinal cord injury.
The development of the rehab robot, named PaRRo, is described in a study published in the journal IEEE Transactions on Biomedical Engineering.
PaRRo was designed to provide task-specific training, according to the researchers, in a news story from Cerebral Palsy News Today.
It features an effector at the end of a robotic arm, which is engineered to be maneuvered by the patient. The effector is connected to a system of brakes that offer resistance to the arm’s movement, training muscle strength and improving arm resistance.
The amount of resistance can be controlled by each patient, meaning that the arm exercise intensities can be adapted to each patient’s motor skills.
However, the news story continues, the rehab robot is passive, which means it does not have any computer control, nor does it actively operate by taking over from the user.
In their research, the team performed simulations to calculate the robot’s resistive force and workspace. They then constructed a prototype based on these results, which was tested in a healthy male volunteer with no neurological or orthopedic impairments.
Nine surface electrodes were placed in different muscles and recorded the muscle activity via electromyography.
Both the force generated by the robot and the force produced by the user matched those predicted by the simulations when the device was moved across different directions.
Electromyography results also revealed the robot was capable of generating resistive forces adjustable to the subject’s motor abilities, the news story explains.
“These results indicate that PaRRo is a feasible low-cost approach to provide functional resistance training to the muscles of the upper-extremity,” according to the researchers, in the study.
“The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy,” they add.
[Source: Cerebral Palsy News Today]
In order to overcome the drawbacks of traditional rehabilitation method, the robot-aided rehabilitation has been widely investigated for the recent years. And the hand rehabilitation robot, as one of the hot research fields, remains many challenging issues to be investigated. This paper presents a new hand exoskeleton system with some novel characteristics. Firstly, both active and passive rehabilitative motions are realized. Secondly, the device is elaborately designed and brings advantages in many aspects. For example, joint motion is accomplished by a parallelogram mechanism and high level motion control is therefore made very simple without the need of complicated kinematics. The adjustable joint limit design ensures that the actual joint angles don’t exceed the joint range of motion (ROM) and thus the patient safety is guaranteed. This design can fit to the different patients with different joint ROM as well as to the dynamically changing ROM for individual patient. The device can also accommodate to some extent variety of hand sizes. Thirdly, the proposed control strategy simultaneously realizes the position control and force control with the motor driver which only works in force control mode. Meanwhile, the system resistance compensation is preliminary realized and the resisting force is effectively reduced. Some experiments were conducted to verify the proposed system. Experimentally collected data show that the achieved ROM is close to that of a healthy hand and the range of phalange length (ROPL) covers the size of a typical hand, satisfying the size need of regular hand rehabilitation. In order to evaluate the performance when it works as a haptic device in active mode, the equivalent moment of inertia (MOI) of the device was calculated. The results prove that the device has low inertia which is critical in order to obtain good backdrivability. The experiments also show that in the active mode the virtual interactive force is successfully feedback to the finger and the resistance is reduced by one-third; for the passive control mode, the desired trajectory is realized satisfactorily.
[Abstract] Design and Development of a Robot Guided Rehabilitation Scheme for Upper Extremity Rehabilitation
To rehabilitate individuals with impaired upper-limb function, we have designed and developed a robot guided rehabilitation scheme. A humanoid robot, NAO was used for this purpose. NAO has 25 degrees of freedom. With its sensors and actuators, it can walk forward and backward, can sit down and stand up, can wave his hand, can speak to the audience, can feel the touch sensation, and can recognize the person he is meeting. All these qualities have made NAO a perfect coach to guide the subjects to perform rehabilitation exercises. To demonstrate rehabilitation exercises with NAO, a library of recommended rehabilitation exercises involving shoulder (i.e., abduction/adduction, vertical flexion/extension, and internal/external rotation), and elbow (i.e., flexion/extension) joint movements was formed in Choregraphe (graphical programming interface). In experiments, NAO was maneuvered to instruct and demonstrate the exercises from the NRL. A complex ‘touch and play’ game was also developed where NAO plays with the subject that represents a multi-joint movement’s exercise. To develop the proposed tele-rehabilitation scheme, kinematic model of human upper-extremity was developed based modified Denavit-Hartenberg notations. A complete geometric solution was developed to find a unique inverse kinematic solution of human upper-extremity from the Kinect data. In tele-rehabilitation scheme, a therapist can remotely tele-operate the NAO in real-time to instruct and demonstrate subjects different arm movement exercises. Kinect sensor was used in this scheme to get tele-operator’s kinematics data. Experiments results reveals that NAO can be tele-operated successfully to instruct and demonstrate subjects to perform different arm movement exercises. A control algorithm was developed in MATLAB for the proposed robot guided supervised rehabilitation scheme. Experimental results show that the NAO and Kinect sensor can effectively be used to supervise and guide the subjects in performing active rehabilitation exercises for shoulder and elbow joint movements.
Assad-Uz-Zaman, Md, “Design and Development of a Robot Guided Rehabilitation Scheme for Upper Extremity Rehabilitation” (2017). Theses and Dissertations. 1578.
[Abstract] Clinical and kinematic evaluation of the H-Man arm robot for post-stroke upper limb rehabilitation: Preliminary findings of a randomised controlled trial
The H-Man robot, a table-top, portable, 2D planar, end-effector with virtual reality feedback was designed to deliver self-paced, repetitive reaching arm movements. Preliminary results of a randomized clinical trial of 26/44 strokes with hemiparetic arm weakness are presented.
Material and method
Inclusion criteria included first-stroke, > 4 months duration with Fugl Meyer Assessment Scale (FMA) 20–50/66) without contraindications to robot-aided therapy. Following informed consent, subjects were randomized into 2 groups: H-Man-conventional (HCT) group received 18 sessions over 6 weeks of 60 minutes of H-man training then 30 minutes of conventional therapy (CT), while control group (CG) received a similar intensity of 90 minutes of CT. Blinded outcome assessments at weeks 0 (baseline), 3, 6 (end-training), 12 and 24 (follow-up). The primary outcome measure was FMA change at week 6. Parametric analysis was used and level of significance was P < 0.05.
Altogether, data from 26 out of 44 subjects were analyzed. (13 HCT, 13 CG). Mean age was 54.0 years (SD 10.9), 14/26 were male, 15/26 had hemorrhagic strokes, mean stroke duration 227.2 days (SD 207.2), and mean baseline FMA 38.6 (SD 11.1). The HCT group achieved significantly better FMA gains compared to CG (4.15 HCT vs. 1.69 CG, P = 0.03) at week 6 (post-training), and at week 24 (5.77 HCT vs 2.61 CG, P = 0.03). There were no adverse side effects or drop outs. Robotic kinematic measures of line and circle temporal tracing correlated with FMA scores at Week 0.
Combinatory arm rehabilitation with H-Man robot was superior to CT and well tolerated.
ARK are international symposia of the highest level organized every two years since 1988. ARK provides a forum for researchers working in robot kinematics and stimulates new directions of research by forging links between robot kinematics and other areas.The main topics of the symposium of 2018 were: kinematic analysis of robots, robot modeling and simulation, kinematic design of robots, kinematics in robot control, theories and methods in kinematics, singularity analysis, kinematic problems in parallel robots, redundant robots, cable robots, over-constrained linkages, kinematics in biological systems, humanoid robots and humanoid subsystems.
[ARTICLE] Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke – Full Text
Rehabilitation robots can provide intensive physical training after stroke. However, variations of the rehabilitation effects in translation from well-controlled research studies to clinical services have not been well evaluated yet. This study aims to compare the rehabilitation effects of the upper limb training by an electromyography (EMG)-driven robotic hand achieved in a well-controlled research environment and in a practical clinical service.
It was a non-randomized controlled trial, and thirty-two participants with chronic stroke were recruited either in the clinical service (n = 16, clinic group), or in the research setting (n = 16, lab group). Each participant received 20-session EMG-driven robotic hand assisted upper limb training. The training frequency (4 sessions/week) and the pace in a session were fixed for the lab group, while they were flexible (1–3 sessions/week) and adaptive for the clinic group. The training effects were evaluated before and after the treatment with clinical scores of the Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), Functional Independence Measure (FIM), and Modified Ashworth Scale (MAS).
Significant improvements in the FMA full score, shoulder/elbow and wrist/hand (P < 0.001), ARAT (P < 0.001), and MAS elbow (P < 0.05) were observed after the training for both groups. Significant improvements in the FIM (P < 0.05), MAS wrist (P < 0.001) and MAS hand (P < 0.05) were only obtained after the training in the clinic group. Compared with the lab group, higher FIM improvement in the clinic group was observed (P < 0.05).
The functional improvements after the robotic hand training in the clinical service were comparable to the effectiveness achieved in the research setting, through flexible training schedules even with a lower training frequency every week. Higher independence in the daily living and a more effective release in muscle tones were achieved in the clinic group than the lab group.
Stroke is a major cause of permanent disability in adults . By 2014, the number of stroke survivors in Hong Kong was approximately 300,000, and more than 7 million in Mainland China, with an average of 2 million new cases per year and an annual increase of 8% from 2009 to 2014 in Mainland China [2, 3]. Approximately 80% of stroke survivors experience upper extremity impairment and disability in activities of daily living (ADLs) [4, 5]. However, fewer than 25% of these can regain limited recovery on their paretic arms even after post-stroke rehabilitation . Physical treatment can result in more significant recovery of arm function during the subacute period (i.e., before 6 months after stroke onset) than in the chronic stage (i.e., more than 6 months after the stroke onset) . In current clinical practice, the professional manpower of post-stroke rehabilitation is much more concentrated on the in-patient period in the subacute stage, compared with that in the long-term service for chronic stroke. However, recent studies have demonstrated that with intensive training, significant motor improvements could also be achieved during the chronic period after stroke [8, 9]. The challenge, however, is that rehabilitation manpower is insufficient, even in developed countries with the fast-expanding stroke populations. Hence, effective techniques and services for long-term rehabilitation after stroke are in urgent need.
Rehabilitation robots have been valuable for human therapists in delivering the labor-demanding physical training with the advantages of higher repetition and lower cost than professional manpower . Various robots have been proposed for the upper limb rehabilitation after stroke, and the robots’ effectiveness has been evaluated by clinical trials [11, 12, 13]. Among them, robot-assisted rehabilitation controlled by the voluntary inputs of the user exhibited more significant efficacy than that with continuous passive motions, i.e., no voluntary input was required from a user and the robots dominated the motion of a paralyzed limb . In a voluntary intention driven robot designed by Song et al. , electromyography (EMG) from the residual muscle of the upper limb was used as the indicator of the voluntary motor intention from a stroke survivor. In the related randomized clinical trial, it was found that patients with chronic stroke obtained more significant motor gain when assisted with the EMG-driven robot than with passive motion assistance alone . Another representative study was the large randomized multi-center trial by Lo et al. which compared the MIT-Manus robotic system for upper limb training with the conventional physical treatments by a human therapist . The results suggested that the robot could achieve the equivalent motor improvements to those of the conventional treatment . Thus, according to the findings, robot-assisted post-stroke training could be a cost-effective alternative to the conventional rehabilitation service when human manpower is insufficient.
However, almost all positive reports on robot-assisted rehabilitation were obtained through research-oriented clinical trial studies and not in a real clinical service configuration, with the assumption that the positive improvements reported in the trial studies would be naturally carried on into the real services after commercialization. Differences, or even discounts, in the rehabilitation effectiveness during the translation from well-controlled research studies to more flexible services have not yet been intensely evaluated. Actually, the feasibility and effectiveness of rehabilitation robots in the clinical service setting have been questioned when trial-quality management was difficult to achieve in a real long-term service [18, 19, 20, 21, 22]. There are several factors that increase the difficulty of head-to-head comparison on the training effectiveness in robot-assisted rehabilitation services with the clinical trials. For instance: (1) In a real service setting, the rehabilitation schedule is relatively flexible with payment from a client. In contrast, trial studies have restricted training schedules (are usually free of charge, or in some cases, participants are even paid for their involvement in the trial); (2) Participant (client) variability is large in the service. In the trials, participant inclusion criteria are usually targeted, and therefore, are difficult to replicate and implement exactly in the service management (particularly in the private sectors) due to the financial sustainability required; (3) In a clinical trial, the participants would usually not be allowed to receive other treatments that might interfere with the prescribed physical program under investigation. However, in a service setting, it is impossible to restrict a client and stop him/her from receiving other physical treatments he/she considers useful. An EMG-driven robotic hand was designed in our previous work, and its rehabilitation effectiveness on the upper limb functions in chronic stroke has been reported by a single group clinical trial . From 2011, the EMG-driven robotic hand service open to local communities has been available in a self-financed university clinic in a private sector. The purpose of this work was to quantitatively evaluate the difference between the rehabilitation effects of an EMG-driven robot hand-assisted upper limb training program conducted as a research trial in a laboratory configuration and as real clinical practice in a private clinic, with minimum disturbance to the routine clinical management and service provided to the clients.
EMG-driven robotic hand
Continue —> Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke | BioMedical Engineering OnLine | Full Text
[ARTICLE] Automatic Control of Wrist Rehabilitation Therapy (WRist-T) device for Post-Ischemic Stroke Patient – Full Text PDF
Full Text: PDF
N. Bayona,“The role of task-specific training in rehabilitation therapies,”Topics in Stroke Rehabilitation, vol. 12, 2005,pp. 58–65.
R. Bonita, R. Beaglehole, “Recovery of motor function after stroke,”Stroke, 1988,pp. 19.
S. Cramer, J. Riley, “Neuroplasticity and brain repair after stroke,”Current Opinion in Neurology,vol. 21, 2008,pp. 76–82.
D.J. Reinkensmeyer, J. Emken, S. Cramer, “Robotics, motor learning, and neurologic recovery,”Annual Review of Biomedical Engineering, vol. 6, 2004, pp. 497-525.
M. Takaiwa, “Wrist rehabilitation training simulator for P.T. using pneumatic parallel manipulator,”IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2016, pp. 276-281.
H. Al-Fahaam, S. Davis, S. Nefti-Meziani, “Wrist Rehabilitation exoskeleton robot based on pneumatic soft actuators,”International Conference for Students of Applied Engineering (ICSAE), 2016, pp. 491-496.
D. Dauria, F. Persia, B. Siciliano,“Human-Computer Interaction in Healthcare: How to Support Patients during their Wrist Rehabilitation,”IEEE Tenth International Conference on Semantic Computing (ICSC), 2016, pp. 325-328.
W.M. Hsieh, Y.S. Hwang, S.C. Chen, S.Y. Tan,C.C. Chen, and Y.L. Chen, “Application of the Blobo Bluetooth ball in wrist rehabilitation training,”Journal of Physical Therapy Science, vol. 28, 2016, pp. 27- 32.
A. Hacıoğlu, O.F. Özdemir, A,K, Şahin, Y.S. Akgül, “Augmented reality based wrist rehabilitation system,”Signal Processing and Communication Application Conference (SIU), 2016. pp. 1869-1872.
Z.J. Lu, L.C.B. Wang, L.H. Duan, Q.Q. Lui, H.Q. Sun, Z.I. Chen, “Development of a robot MKW-II for hand and Wrist Rehabilitation Training,”The Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, 2016, pp. 302-307.
[ARTICLE] Upper limb robotic rehabilitation for chronic stroke survivors: a single-group preliminary study – Full Text PDF
[Purpose] This study aimed to assess whether robotic rehabilitation can improve upper limb function, activities of daily living performance, and kinematic performance of chronic stroke survivors.
[Subjects and Methods] Participants were 21 chronic stroke survivors (19 men; 60.8 years; Mini-Mental State Examination score: 28; onset duration: 10.2 years). Training exercises were performed with a Whole Arm Manipulator and a 120-inch projective display to provide visual and auditory feedback. Once the training began, red and grey balls appeared on the projective display, and participants performed reaching movements, in the assist-as-needed mode, toward 6 directional targets in a 3-dimensional space. All participants received training for 40 minutes per day, thrice per
week, for 6 weeks. Main outcome measures were upper limb function (Fugl-Meyer Assessment, Action Research Arm Test, and Box and Blocks Test scores), activities of daily living performance (Modified Barthel Index), and kinematic performance (movement velocity) in 6 directions.
[Results] After 6 weeks, significant improvement was observed in upper limb function, activities of daily living performance, and kinematic performance.
This study demonstrated the positive effects of robotic rehabilitation on upper limb function, activities of daily living performance, and kinematic performance in chronic stroke survivors.