Robotic rehabilitation devices have gained high popularity in upper limb physical therapy for stroke patients. Dual-Arm rehabilitation robot system has advantages in achieving coordinated motions for the upper arm and forearm segments. In this paper, an efficient method for the design and evaluation of the kinematics of a dual-arm robot for upper limb rehabilitation, is presented. First, requirements for an upper limb rehabilitation robot are analyzed and candidate manipulator structures are presented. Then, workspace and manipulability, which are served as the criterion of the optimization configuration of a dual-arm rehabilitation robot, are analyzed. Thereafter, the optimal configuration is modeled and simulated to verify the method. Finally, simulation results are shown.
via Configuration Optimization of a Dual-Arm Rehabilitation Robot – IEEE Conference Publication
Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.
Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe , . In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming .
1. M. Cortese, M. Cempini, P. R. de Almeida Ribeiro, S. R. Soekadar, M. C. Carrozza, N. Vitiello, “A mechatronic system for robot-mediated hand telerehabilitation”, IEEE/ASME Transactions on Mechatronics, vol. 20, pp. 1753-1764, September 2015.
2. P. S. Lum, C. G. Burgar, P. C. Shor, “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke”, Archives of physical medicine and rehabilitation, vol. 83, pp. 952-959, July 2002.
3. B. Sheng, Y. Zhang, W. Meng, C. Deng, S. Xie, “Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects”, Medical engineering & physics, vol. 38, pp. 587-606, July 2016.
4. P. R. Culmer, A. E. Jackson, S. Makower, R. Richardson, J. A. Cozens, M. C. Levesley et al., “A control strategy for upper limb robotic rehabilitation with a dual robot system”, IEEE/ASME Transactions on Mechatronics, vol. 15, pp. 575-585, September 2010.
5. Z. Song, S. Guo, M. Pang, S. Zhang, N. Xiao, B. Gao et al., “Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint”, J. Med. Biol. Eng, vol. 34, pp. 188-196, 2014.
6. R. C. Loureiro, W. S. Harwin, K. Nagai, M. Johnson, “Advances in upper limb stroke rehabilitation: a technology push”, Medical & biological engineering & computing, vol. 49, pp. 1103, July 2011.
7. S. Hesse, C. Werner, M. Pohl, S. Rueckriem, J. Mehrholz, M. Lingnau, “Computerized arm training improves the motor control of the severely affected arm after stroke”, Stroke, vol. 36, pp. 1960-1966, August 2005.
8. C.-L. Yang, K.-C. Lin, H.-C. Chen, C.-Y. Wu, C.-L. Chen, “Pilot comparative study of unilateral and bilateral robot-assisted training on upper-extremity performance in patients with stroke”, American Journal of Occupational Therapy, vol. 66, pp. 198-206, March 2012.
9. E. Taub, G. Uswatte, R. Pidikiti, “Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation-a clinical review”, Journal of rehabilitation research and development, vol. 36, pp. 237, July 1999.
10. S. B. Brotzman, R. C. Manske, “Clinical Orthopaedic Rehabilitation E-Book: An Evidence-Based Approach-Expert Consult” in Elsevier Health Sciences, 2011.
11. K. C. Lin, Y. F. Chang, C. Y. Wu, Y. A. Chen, “Effects of constraint-induced therapy versus bilateral arm training on motor performance daily functions and quality of life in stroke survivors”, Neurorehabilitation and Neural Repair, vol. 23, pp. 441-448, December 2009.
12. J. Chen, N. Y. Yu, D. G. Huang, B. T. Ann, G. C. Chang, “Applying fuzzy logic to control cycling movement induced by functional electrical stimulation”, IEEE transactions on rehabilitation engineering, vol. 5, pp. 158-169, Jun 1997.
13. D. A. Winter, “Biomechanics and motor control of human movement” in John Wiley & Sons, 2009.
via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication
Rehabilitation is an important process to restore muscle strength and joint’s range of motion. This paper proposes a biomechatronic design of a robotic arm that is able to mimic the natural movement of the human shoulder, elbow and wrist joint. In a preliminary experiment, a subject was asked to perform four different arm movements using the developed robotic arm for a period of two weeks. The experimental results were recorded and can be plotted into graphical results using Matlab. Based on the results, the robotic arm shows encouraging effect by increasing the performance of rehabilitation process. This is proven when the result in degree value are accurate when being compared with the flexion of both shoulder and elbow joints. This project can give advantages on research if the input parameter needed in the flexion of elbow and wrist.
According to the United Nations (UN), by 2030 the number of people over 60 years will increase by 56 per cent, from 901 million to more than 1.4 billion worldwide . As the number of older persons is expected to grow, it is imperative that government and private health care providers prepare adequate and modern facilities that can provide quality services for the needs of older persons especially in rehabilitation centers. Implementation of robotic technology in rehabilitation process is a modern method and definitely can contribute in this policy and capable in promoting early recovery and motor learning . Furthermore, systematic application of robotic technology can produce significant clinical results in motor recovery of post-traumatic central nervous system injury by assisting in physical exercise based on voluntary movement in rehabilitation .
via Biomechatronics design of a robotic arm for rehabilitation – IEEE Conference Publication
Robot-assisted training (RT) is a novel technique with promising results for stroke rehabilitation. However, benefits of RT on individuals with long-term chronic stroke have not been well studied. For this case study, we developed an arm-based RT protocol for reaching practice in physical and virtual environments and tracked the outcomes in an individual with a long-term chronic stroke (20+ years) over 10 half-hour sessions. We analyzed the performance of the reaching movement with kinematic measures and the arm motor function using the Fugl-Meyer Assessment-Upper Extremity scale (FMA-UE). The results showed significant improvements in the subject’s reaching performance accompanied by a small increase in FMA-UE score from 18 to 21. The improvements were also transferred into real life activities, as reported by the subject. This case study shows that even in long-term chronic stroke, improvements in motor function are still possible with RT, while the underlying mechanisms of motor learning capacity or neuroplastic changes need to be further investigated.
Source: Robot-assisted arm training in physical and virtual environments: A case study of long-term chronic stroke – IEEE Xplore Document
Stroke is one of the leading causes of disability worldwide. Consequently, many stroke survivors exhibit difficulties undergoing voluntary movement in their affected upper limb, compromising their functional performance and level of independence. To minimize the negative impact of stroke disabilities, exercises are recognized as a key element in post-stroke rehabilitation.
In order to provide the practice of exercises in a uniform and controlled manner as well as increasing the efficiency of therapists’ interventions, robotic training has been found, and continues to prove itself, as an innovative intervention for post-stroke rehabilitation. However, the complexity as well as the limited degrees of freedom and workspace of currently commercially available robots can limit their use in clinical settings. Up to now, user-friendly robots covering a sufficiently large workspace for training of the upper limb in its full range of motion are lacking.
This paper presents the design and implementation of ERA, an upper-limb 3-DOF force-controlled exerciser robot, which presents a workspace covering the entire range of motion of the upper limb. The ERA robot provides 3D reaching movements in a haptic virtual environment. A description of the hardware and software components of the ERA robot is also presented along with a demonstration of its capabilities in one of the three operational modes that were developed.
Source: IEEE Xplore Document – Exerciser for rehabilitation of the Arm (ERA): Development and unique features of a 3D end-effector robot