Posts Tagged rehabilitation robots

[Abstract] Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor

Abstract

It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist ?exion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training; two types of visual guidance, namely looking at the hand motion shown on a video and looking at the user’s own hand, had no significant performance difference; a general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.

via Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor – IEEE Journals & Magazine

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[Abstract] Model Predictive Control for Upper Limb Rehabilitation Robotic System Under Noisy Condition

Abstract

Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements, ensuring with element of enjoyment patients’ safety. Nonlinear controllers make good option to this end as they adapt to handling the system uncertainties and parametric changes. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under disturbed conditions. From results maximum overshoot of 1.4 and 1.0 and steady state error of 0.99 is found under disturbed and noisy condition respectively. Hence MPC proves to be a robust controller of external disturbances rejection and noise filtration.

via Model Predictive Control for Upper Limb Rehabilitation Robotic System Under Noisy Condition – IEEE Conference Publication

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[Abstract + References] Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation

Abstract

Rehabilitation robots play an important role in rehabilitation treatment. Unlike conventional rehabilitation approach, the rehabilitation robotics provides an intensive rehabilitation motion with different modes (passive, active and active-assisted) based on the ability of the exoskeleton robot to perform assistive motion for a long period. However, this technology is still an emerging field. In this chapter, we present a Cartesian adaptive control based on a robust proportional sliding mode combined with time delay estimation for controlling a redundant exoskeleton robot called ETS-MARSE subject to uncertain nonlinear dynamics and external forces. The main objective of this research is to allow the exoskeleton robot to perform both rehabilitation modes, passive and active assistive motions with real subjects. The stability of the closed loop system is solved systematically, ensuring asymptotic convergence of the output tracking errors. Experimental results confirm the efficiency of the proposed control to provide an excellent performance despite the presence of dynamic uncertainties and external disturbances.

References

  1. Baek, J., Jin, M., & Han, S. (2016). A new adaptive sliding-mode control scheme for application to robot manipulators. IEEE Transactions on Industrial Electronics, 63(6), 3628–3637.CrossRefGoogle Scholar
  2. Brahim, B., Maarouf, S., Luna, C. O., Abdelkrim, B., & Rahman, M. (2016a). Adaptive iterative observer based on integral backstepping control for upper extremity exoskelton robot. In 8th International Conference on Modelling, Identification and Control (ICMIC) (pp. 886–891). Piscataway: IEEE.Google Scholar
  3. Brahim, B., Rahman, M. H., Saad, M., & Luna, C. O. (2016b). Iterative estimator-based nonlinear backstepping control of a robotic exoskeleton. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 10(8), 1313–1319.Google Scholar
  4. Brahmi, B., Saad, M., Lam, J. T. A. T., Luna, C. O., Archambault, P. S., & Rahman, M. H. (2018a). Adaptive control of a 7-DOF exoskeleton robot with uncertainties on kinematics and dynamics. European Journal of Control, 42, 77–87.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Brahmi, B., Saad, M., Luna, C. O., Rahman, M. H., & Brahmi, A. (2018b). Adaptive tracking control of an exoskeleton robot with uncertain dynamics based on estimated time delay control. IEEE/ASME Transactions on Mechatronics, 23, 575–585.CrossRefGoogle Scholar
  6. Chen, P., Chen, C.-W., & Chiang, W.-L. (2009). GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems. Expert Systems with Applications, 36(3), 5872–5879.CrossRefGoogle Scholar
  7. Craig, J. J. (2005). Introduction to robotics: Mechanics and control. Upper Saddle River: Pearson/Prentice Hall.Google Scholar
  8. De Morand, A. (2014). Pratique de la rééducation neurologique. Issy-les-Moulineaux: Elsevier Masson.Google Scholar
  9. Fridman, L. (1999). The problem of chattering: An averaging approach. In Variable structure systems, sliding mode and nonlinear control(pp. 363–386). London: Springer.CrossRefGoogle Scholar
  10. Khalil, H. K., & Grizzle, J. (1996). Nonlinear systems. New Jersey: Prentice Hall.Google Scholar
  11. Khan, A. M., Yun, D.-W., Ali, M. A., Zuhaib, K. M., Yuan, C., & Iqbal, J. (2016). Passivity based adaptive control for upper extremity assist exoskeleton. International Journal of Control, Automation and Systems, 14(1), 291–300.CrossRefGoogle Scholar
  12. Lewis, F. L., Dawson, D. M., & Abdallah, C. T. (2003). Robot manipulator control: Theory and practice. Boca Raton: CRC Press.CrossRefGoogle Scholar
  13. Nakamura, Y., & Hanafusa, H. (1986). Inverse kinematic solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 108(3), 163–171.zbMATHCrossRefGoogle Scholar
  14. Ochoa-Luna, C., Habibur Rahman, M., Saad, M., Archambault, P. S., & Bruce Ferrer, S. (2015). Admittance-based upper limb robotic active and active-assistive movements. International Journal of Advanced Robotic Systems, 12(9), 117.CrossRefGoogle Scholar
  15. Park, C.-W., & Cho, Y.-W. (2007). Robust fuzzy feedback linearisation controllers for Takagi-Sugeno fuzzy models with parametric uncertainties. IET Control Theory & Applications, 1(5), 1242–1254.MathSciNetCrossRefGoogle Scholar
  16. Rahman, M. H., Saad, M., Kenné, J.-P., & Archambault, P. S. (2013). Control of an exoskeleton robot arm with sliding mode exponential reaching law. International Journal of Control, Automation and Systems, 11(1), 92–104.CrossRefGoogle Scholar
  17. Rahman, M. H., Rahman, M. J., Cristobal, O., Saad, M., Kenné, J.-P., & Archambault, P. S. (2015). Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements. Robotica, 33(1), 19–39.CrossRefGoogle Scholar
  18. Reyes-Sierra, M., & Coello, C. A. C. (2005). A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization. In IEEE Congress on Evolutionary Computation, Edinburgh (pp. 65–72).Google Scholar
  19. Shieh, H.-J., & Hsu, C.-H. (2008). An adaptive approximator-based backstepping control approach for piezoactuator-driven stages. IEEE Transactions on Industrial Electronics, 55(4), 1729–1738.CrossRefGoogle Scholar
  20. Slotine, J.-J. E., & Li, W. (1991). Applied nonlinear control. Englewood Cliffs: Prentice-Hall.zbMATHGoogle Scholar
  21. Wampler, C. W. (1986). Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods. IEEE Transactions on Systems, Man, and Cybernetics, 16(1), 93–101.zbMATHCrossRefGoogle Scholar
  22. Wang, L., Chai, T., & Zhai, L. (2009). Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Transactions on Industrial Electronics, 56(9), 3296–3304.CrossRefGoogle Scholar
  23. Xie, S. (2016). Advanced robotics for medical rehabilitation. Cham: Springer.CrossRefGoogle Scholar
  24. Yim, J., & Park, J. H. (1999). Nonlinear H  control of robotic manipulator. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, SMC’99. Proceedings 1999 (pp. 866–871). Piscataway: IEEE.Google Scholar
  25. Youcef-Toumi, K., & Ito, O. (1990). A time delay controller for systems with unknown dynamics. Journal of Dynamic Systems, Measurement, and Control, 112(1), 133–142.zbMATHCrossRefGoogle Scholar
  26. Young, K. D., Utkin, V. I., & Ozguner, U. (1999). A control engineer’s guide to sliding mode control. IEEE Transactions on Control Systems Technology, 7(3), 328–342.CrossRefGoogle Scholar

via Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation | SpringerLink

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[THESIS] Model-based Control of Upper Extremity Human-Robot Rehabilitation Systems – Full Text PDF

Abstract

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to:

1) deliver repetitive and stimulating practice to post-stroke patients,

2) minimize therapist interventions, and

3) increase the number of patients per therapist, thereby decreasing
the associated cost.

The control of rehabilitation robots is often limited to black or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues.
This thesis presents advanced impedance- and model-based controllers for an endeffector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment.

In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is
developed. The rehabilitation robot is a 2 Degree-of-Freedom (DOF) parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Humanrobot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy.

In robotic rehabilitation, because of physical interaction of the patient with a mechanical
device, safety is a fundamental element in the design of a controller. Thus, impedance based assistance is commonly used for robotic rehabilitation. One of our objectives is
to achieve a reliable and real-time implementable controller. In our definition, a reliable
controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation.

Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed.

In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-Dimensional (3D) musculoskeletal model or internal 2-Dimensional (2D) musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2D musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project.[…]

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[ICRA 2017 Workshop] Advances and challenges on the development, testing and assessment of assistive and rehabilitation robots: Experiences from engineering and human science research – PDF

Abstract
Assistive robots for health and welfare applications are required to display perceptual, cognitive and
bodily-kinaesthetic capabilities that are natural and intuitive for older people and persons with disabilities
to interact with, communicate with, work with as partners, and learn to adapt to their needs. However, the
embodiment of such capabilities has been scarcely studied, so it is still required that the health and social
care staff and the user groups could explore and learn how to exploit the capabilities of the assistive
robots. Therefore, a multidisciplinary approach to promote the study from the engineering and human
science to introduce the next generation of assistive robots is desired. The goal of this workshop is to
provide a forum for sharing the experiences from the engineering and human science research on the
development, testing and assessment of assistive robots and present the most recent advances and
challenges in order to foreseen novel designing approaches and user based studies addressing
healthcare and social welfare applications in the ambient assisted living from a world-wide perspective
with point of departure in interdisciplinary research collaboration.

WORKSHOP PROCEEDINGS – PAPERS
Table of Contents
Invited Speakers and Poster Papers

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[REVIEW] Learning control in robot-assisted rehabilitation of motor skills – Full Text HTML/PDF

Abstract

The key idea in iterative learning control is captured by the intuition of ‘practice makes perfect’. The underlying learning is based on a gradient descent algorithm iteratively optimising an appropriate input–output measured criterion. How this paradigm is used to model quantitatively, at an input/output level, the learning that happens in the context of human motor skill learning is discussed in this note.
Experimental studies of human motor learning, in robotically controlled environments, indicate that a model consisting of a classical (iterative) learning control augmented with an appropriate kinematic model of human motor motion fits the observed human learning behaviour well. In the context of the rehabilitation of motor skills, such models promise better human–machine interfaces that extend the capability and capacity of rehabilitation clinicians by creating effective robot–patient–clinician feedback loops. The economic promise of robot-assisted rehabilitation is to greatly extend the intervention capacity above what presently can be achieved by rehabilitation systems: addressing the needs of more people, over longer periods of time and at a distance in the comfort of their own personal environment. Moreover, the robot platforms provide for a more rigorous and quantitative evaluation of the patient’s motor skill across the entire personal rehabilitation trajectory, which opens up opportunities for improved, more individually tuned rehabilitation regimes.

Source: Learning control in robot-assisted rehabilitation of motor skills – a review – Journal of Control and Decision –

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[Abstract] Application of robotics in medical fields: rehabilitation and surgery

Abstract

The applications of robotics in recent years have emerged beyond the field of manufacturing or industrial robots itself. Robotic applications are now widely used in medical, transport, underwater, entertainment and military sectors. In the medical field, these applications should be emphasised in view of the increasing challenges owing to the variety of findings in the field of medicine, which requires new inventions to ease work process. The objective of this review paper is to study and present the past and on-going research in medical robotics with emphasis on rehabilitation (assistive care) and surgery robotics which are certainly the two main practical fields where robot applications are commonly used presently. The study found that rehabilitation and surgery robotics applications grow extensively with the finding of new invention, as well as research that is being undertaken and to be undertaken.

Source: Application of robotics in medical fields: rehabilitation and surgery: International Journal of Computer Applications in Technology: Vol 52, No 4

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[REVIEW ARTICLE] Recent Development of Rehabilitation Robots – Full Text HTML

Abstract:

We have conducted a critical review on the development of rehabilitation robots to identify the limitations of existing studies and clarify some promising research directions in this field. This paper is presented to summarize our findings and understanding. The demands for assistive technologies for elderly and disabled population have been discussed, the advantages and disadvantages of rehabilitation robots as assistive technologies have been explored, the issues involved in the development of rehabilitation robots are investigated, some representative robots in this field by leading research institutes have been introduced, and a few of critical challenges in developing advanced rehabilitation robots have been identified. Finally to meet the challenges of developing practical rehabilitation robots, reconfigurable and modular systems have been proposed to meet the identified challenges, and a few of critical areas leading to the potential success of rehabilitation robots have been discussed.

1. Introduction

The progress on the studies of rehabilitating robots has been significantly lagged in contrast to the emerging society needs. On the one hand, the population who needs assistance and rehabilitation is consistently increasing; on the other hand, the existing rehabilitation robots have the limited capabilities of personalization and yet they are too expensive for the majority of patients. The performances of existing robots have been proven unsatisfactory [1, 2]. The innovations in the development of the next-generation rehabilitation robots can lead to significant benefits to human beings. In this paper, a critical literature review is conducted to identify the limitations of existing works and clarify the prosperous research directions in the development of assistive robots. In the next sections, the needs of assistive technologies in the healthcare industry are introduced…

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via Recent Development of Rehabilitation Robots.

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