Posts Tagged Rehabilitation robotics

[REVIEW ARTICLE] Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach – Full Text

Robot-mediated therapy is an innovative form of rehabilitation that enables highly repetitive, intensive, adaptive, and quantifiable physical training. It has been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis. Multiple studies collated in a growing number of review articles showed the positive effects on motor impairment, less clearly on functional limitations. After describing the current status of robotic therapy after upper limb paresis due to stroke, this overview addresses basic principles related to robotic therapy applied to upper limb paresis. We demonstrate how this innovation is an evidence-based approach in that it meets both the improved clinical and more fundamental knowledge-base about regaining effective motor function after stroke and the need of more objective, flexible and controlled therapeutic paradigms.


Robot-mediated rehabilitation is an innovative exercise-based therapy using robotic devices that enable the implementation of highly repetitive, intensive, adaptive, and quantifiable physical training. Since the first clinical studies with the MIT-Manus robot (1), robotic applications have been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis but also in cerebral palsy (2), multiple sclerosis (3), spinal cord injury (4), and other disease types. Thus, multiple studies suggested that robot-assisted training, integrated into a multidisciplinary program, resulted in an additional reduction of motor impairments in comparison to usual care alone in different stages of stroke recovery: namely, acute (57), subacute (18), and chronic phases after the stroke onset (911). Typically, patients engaged in the robotic therapy showed an impairment reduction of 5 points or more in the Fugl-Meyer assessment as compared to usual care. Of notice, rehabilitation studies conducted during the chronic stroke phase suggest that a 5-point differential represents the minimum clinically important difference (MCID), i.e., the magnitude of change that is necessary to produce real-world benefits for patients (12). These results were collated in multiple review articles and meta-analyses (1317). In contrast, the advantage of robotic training over usual care in terms of functional benefit is less clear, but there are recent results that suggest how best to organize training to achieve superior results in terms of both impairment and function (18). Indeed, the use of the robotic tool has allowed us the parse and study the ingredients that should form an efficacious and efficient rehabilitation program. The aim of this paper is to provide a general overview of the current state of robotic training in upper limb rehabilitation after stroke, to analyze the rationale behind its use, and to discuss our working model on how to more effectively employ robotics to promote motor recovery after stroke.

Upper Extremity Robotic Therapy: Current Status

Robotic systems used in the field of neurorehabilitation can be organized under two basic categories: exoskeleton and end-effector type robots. Exoskeleton robotic systems allow us to accurately determine the kinematic configuration of human joints, while end-effector type robots exert forces only in the most distal part of the affected limb. A growing number of commercial robotic devices have been developed employing either configuration. Examples of exoskeleton type include the Armeo®Spring, Armeo®Power, and Myomo® and of end-effector type include the InMotion™, Burt®, Kinarm™ and REAplan®. Both categories enable the implementation of intensive training and there are many other devices in different stages of development or commercialization (1920).

The last decade has seen an exponential growth in both the number of devices as well as clinical trials. The results coalesced in a set of systematic reviews, meta-analyses (1317) and guidelines such as those published by the American Heart Association and the Veterans Administration (AHA and VA) (21). There is a clear consensus that upper limb therapy using robotic devices over 30–60-min sessions, is safe despite the larger number of movement repetitions (14).

This technic is feasible and showed a high rate of eligibility; in the VA ROBOTICS (911) study, nearly two thirds of interviewed stroke survivors were enrolled in the study. As a comparison the EXCITE cohort of constraint-induced movement therapy enrolled only 6% of the screened patients participated (22). On that issue, it is relevant to notice the admission criteria of both chronic stroke studies. ROBOTICS enrolled subjects with Fugl-Meyer assessment (FMA) of 38 or lower (out of 66) while EXCITE typically enrolled subjects with an FMA of 42 or higher. Duret and colleagues demonstrated that the target population, based on motor impairments, seems to be broader in the robotic intervention which includes patients with severe motor impairments, a group that typically has not seen much benefit from usual care (23). Indeed, Duret found that more severely impaired patients benefited more from robot-assisted training and that co-factors such as age, aphasia, and neglect had no impact on the amount of repetitive movements performed and were not contraindicated. Furthermore, all patients enrolled in robotic training were satisfied with the intervention. This result is consistent with the literature (24).

The main outcome result is that robotic therapy led to significantly more improvement in impairment as compared to conventional usual care, but only slightly more on motor function of the limb segments targeted by the robotic device (16). For example, Bertani et al. (15) and Zhang et al. (17) found that robotic training was more effective in reducing motor impairment than conventional usual care therapy in patients with chronic stroke, and further meta-analyses suggested that using robotic therapy as an adjunct to conventional usual care treatment is more effective than robotic training alone (1317). Other examples of disproven beliefs: many rehabilitation professionals mistakenly expected significant increase of muscle hyperactivity and shoulder pain due to the intensive training. Most studies showed just the opposite, i.e., that intensive robotic training was associated with tone reduction as compared to the usual care groups (92526). These results are shattering the resistance to the widespread adoption of robotic therapy as a therapeutic modality post-stroke.

That said, not all is rosy. Superior changes in functional outcomes were more controversial until the very last years as most studies and reviews concluded that robotic therapy did not improve activities of daily living beyond traditional care. One first step was reached in 2015 with Mehrholz et al. (14), who found that robotic therapy can provide more functional benefits when compared to other interventions however with a quality of evidence low to very low. 2018 may have seen a decisive step in favor of robotic as the latest meta-analysis conducted by Mehrholz et al. (27) concluded that robot-assisted arm training may improve activities of daily living in the acute phase after stroke with a high quality of evidence However, the results must be interpreted with caution because of the high variability in trial designs as evidenced by the multicenter study (28) in which robotic rehabilitation using the Armeo®Spring, a non-motorized device, was compared to self-management with negative results on motor impairments and potential functional benefits in the robotic group.

The Robot Assisted Training for the Upper Limb after Stroke (RATULS) study (29) might clarify things and put everyone in agreement on the topic. Of notice, RATULS goes beyond the Veterans Administration ROBOTICS with chronic stroke or the French REM_AVC study with subacute stroke. RATULS included 770 stroke patients and covered all stroke phases, from acute to chronic, and it included a positive meaningful control in addition to usual care.[…]


Continue —->  Frontiers | Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach | Neurology

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[Abstract] Configuration Optimization of a Dual-Arm Rehabilitation Robot – IEEE Conference Publication


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.

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[Abstract] Desktop upper limb rehabilitation robot using omnidirectional drive gear – IEEE Conference Publication


Research and development efforts into small upper limb rehabilitation robots for home-based rehabilitation have been made in order to reduce the patient burden associated with making visits to the hospital. However, currently, there are only a few small upper limb rehabilitation robots capable of providing training that is tailored to account for the differences in individual patients. This is because many robots use omni wheels for their movement mechanism, thus causing problems when measuring patient motor function because it is not possible to accurately estimate the position. To solve this problem, in this study, we propose a new small upper limb rehabilitation robot that switches the driving unit from an omni wheel to an omnidirectional drive gear mechanism, as a mechanism that does not cause slips. Although an omnidirectional drive gear poses problems in terms of machining difficulty and weight, these problems can be solved by using a 3D printer. We show that position errors in small upper limb rehabilitation robots are greatly reduced by introducing a gear mechanism.

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[Abstract + References] Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication


The paper presents a user study to compare the performance of two rehabilitation robotic systems, called HomeRehab and PupArm. The first one is a novel tele-rehabilitation system for delivering therapy to stroke patients at home and the second one has been designed and developed to provide rehabilitation therapy to patients in clinical settings. Nine patients with different neurological disorders participated in the study. The patients performed 16 movements with each robotic platform and after that they filled a usability survey. Moreover, to evaluate the patient’s performance with each robotic device, 8 movement parameters were computed from each trial and for the two robotic devices. Based on the analysis of subjective assessments of usability and the data acquired objectively by the robotic devices, we can conclude that the performance and user experience with both systems are very similar. This finding will be the base of more extensive studies to demonstrate that home-therapy with HomeRehab could be as efficient as therapy in clinical settings assisted by PupArm robot.


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2. J. Mackay, G. A. Mensah, The Atlas of Heart Disease and Stroke, Geneva, Switzerland:World Health Organization, 2004.

3. D. S. Nichols-Larsen, P. C. Clark, A. Zeringue, A. Greenspan, S. Blanton, “Factors Influencing Stroke Survivors Quality of Life during Subacute Recovery”, Stroke, vol. 36, pp. 14801484, 2005.

4. P. Langhorne, F. Coupar, A. Pollock, “Motor Recovery after Stroke: a Systematic Review”, The Lancet Neurology, vol. 8, no. 8, pp. 741754, 2009.

5. C. R. Carnigan, H. I. Krebs, “Telerehabilitation Robotics: Bright Lights Big Future?”, Journal of Rehabilitation Research and Development, vol. 43, no. 5, pp. 695-710, 2006.

6. K. J. Ottenbacher, P. M. Smith, S. B. Illig, R. T. Linn, G. V. Ostir, C. V. Granger, “Trends in Length of Stay Living Setting Functional Outcome and Mortality following Medical Reha-bilitation”, JAMA, vol. 292, no. 14, pp. 1687-1695, 2004.

7. L. Richards, C. Hanson, M. Wellborn, A. Sethi, “Driving Motor Recovery after Stroke”, Topics in Stroke Rehabilitation, vol. 15, no. 5, pp. 397411, 2008.

8. S. M. Linder, A. B. Rosenfeldt, A. Reiss, S. Buchanan, K. Sahu, C. R. Bay, S. L. Wolf, J. L. Alberts, “The Home Stroke Rehabilitation and Monitoring System Trial: A Randomized Controlled Trial”, International Journal of Stroke, vol. 8, no. 1, pp. 1747-4949, 2013.

9. T. Larsen, T. S. Olsen, J. Sorensen, “Early Home-Supported Discharge of Stroke Patients: A Health Technology Assessment”, International Journal of Technology Assessment in Health Care, vol. 22, no. 3, pp. 313-320, 2006.

10. Ifiaki Díaz, José María Catalan, Francisco Javier Badesa, Xabier Justo, Luis Daniel Lledo, Axier Ugartemendia, Jorge juan Gil, Jorge Díez, Nicolás García-Aracil, Development of a robotic device for post-stroke home tele-rehabilitation. Advances in Mechanical Engineering, vol. 10, no. 1, pp. 1-8, 2018.

11. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, J. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

12. R. Likert, G. M. Maranell, “A method of constructing an attitude scale” in Scaling: A Sourcebook for Behavioral Scientists, Chicago, IL:Aldine Publishing, pp. 233243, 1974.

13. H. J. Krebs, N. Hogan, M. L. Aisen, B. T. Volpe, “Robot-aided neurorehabilitation”, IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 1, pp. 75-87, Mar 1998.

14. Franciso J Badesa, Ana Llinares, Ricardo Morales, Nicolas Garcia-Aracil, Jose M Sabater, Carlos Perez-Vidal, “Pneumatic planar rehabilitation robot for post-stroke patients”, Biomedical Engineering: Applications Basis and Communications, vol. 26, no. 2, pp. 1450025, 2014.

15. D. Lledo Luis, A. Diez Jorge, Bertomeu-Motos Arturo, Ezquerro Santiago, J. Badesa Francisco, M. Sabater-Navarro Jose, Garca-Aracil Nicolas, “A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients”, Frontiers in Aging Neuroscience, vol. 8, pp. 205, 2016.

16. A. Llinares, F. J. Badesa, R. Morales, N. Garcia-Aracil, J. Sabater, E. Fernandez, “Robotic assessment of the influence of age on upper-limb sensorimotor function”, Clin. Interv. Aging, vol. 8, pp. 879, 2013.

17. D. S. Dunn, Statistics and data analysis for the behavioral sciences, New York, NY, US:McGraw-Hill, 2001.

18. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, I. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

19. AM Coderre, AA Zeid, SP Dukelow et al., “Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching”, Neurorehabil Neural Repair., vol. 24, no. 6, pp. 528541, 2010.

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[Abstract + References] A Tendon-driven Upper-limb Rehabilitation Robot – IEEE Conference Publication


Rehabilitation robots are playing an increasingly important role in daily rehabilitation of patients. In recent years, exoskeleton rehabilitation robots have become a research hotspot. However, the existing exoskeleton rehabilitation robots are mainly rigid exoskeletons. During rehabilitation training using such exoskeletons, the patient’s joint rotation center is fixed, which cannot adapt to the actual joint movements, resulting in secondary damage to the patients. Therefore, in this paper, a tendon-driven flexible upper-limb rehabilitation robot is proposed; the structure and connectors of the rehabilitation robot are designed considering the physiological structure of human upper limbs; we also built the prototype and performed experiments to validate the designed robot. The experimental results show that the proposed upper-limb rehabilitation robot can assist the human subject to conduct upper-limb rehabilitation training.

I. Introduction

Central nervous system diseases, such as stroke, spinal cord injury and traumatic brain injury, tend to cause movement disorder [1]. Clinical studies have shown that intensive rehabilitation training after cerebral injury help patients recover motoric functions because of the brain plasticity [1], [2]. Traditional movement therapy is highly dependent on physiotherapists and the efficacy is limited by professional knowledge and skill levels of physiotherapists [3]. Upper-limbs recover more slowly than lower limbs because of the complex function of neurons. Meanwhile, the rehabilitation therapies are unaffordable for most patients. Robotic rehabilitation opened another way of rehabilitation training and its efficacy has been validated in clinical trials [3], [4]. Many upper-limb robot devices have been developed for rehabilitation or assistance in various forms. One of the famous devices was MIT-MANUS developed by MIT. This kind of devices are stationary external system where the patient inserts their hand or arm and is robotically assisted or resisted in completing predetermined tasks [3], [5]. Other examples of this type of devices include Lum et al.^{\prime}s MIME [6], Kahn et al.’s ARM Guide [7] and a 2-DOF upper-limb rehabilitation robot developed by Tsinghua



1. M. Hallett, “Plasticity of the human motor cortex and recovery from stroke”, Brain Research Reviews, vol. 36, pp. 169-174, 2001.

2. J. D. Schaechter, “Motor rehabilitation and brain plasticity after hemiparetic stroke”, Progress in Neurobiology, vol. 73, pp. 61-72, 2004.

3. Q. Yang, D. Cao, J. Zhao, “Analysis on State of the Art of upper-limb Rehabilitation Robots”, Jiqiren/robot, vol. 35, pp. 630, 2013.

4. P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, S. Leonhardt, “A survey on robotic devices for upper-limb rehabilitation”, Journal of Neuroengineering & Rehabilitation, vol. 11, pp. 3, 2014.

5. C. J. Nycz, M. A. Delph, G. S. Fischer, “Modeling and design of a tendon actuated flexible robotic exoskeleton for hemiparetic upper-limb rehabilitation”, International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3889-3892, 2015.

6. P. S. Lum, C. G. Burgar, P. C. Shor, “Use of the MIME robotic system to retrain multijoint reaching in post-stroke hemiparesis: why some movement patterns work better than others”, Engineering in Medicine and Biology Society 2003. Proceedings of the International Conference of the IEEE, vol. 2, pp. 1475-1478, 2003.

7. D. J. Reinkensmeyer, L. E. Kahn, M. Averbuch, A. Mckenna-Cole, B. D. Schmit, W. Z. Rymer, “Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide”, Journal of Rehabilitation Research & Development, vol. 37, pp. 653-662.

8. Y. Zhang, Z. Wang, L. Ji, S. Bi, “The clinical application of the upper extremity compound movements rehabilitation training robot”, International Conference on Rehabilitation Robotics, pp. 91-94, 2005.

9. H. Fukushima, “Health and wellbeing in the 21st century (No. 4): Early rehabilitation and conditions for which it is appropriate [J]” in Social-human environmentology, pp. 6, 2004.

10. T. G. Sugar, J. He, E. J. Koeneman, J. B. Koeneman, R. Herman, H. Huang et al., “Design and control of RUPERT: a device for robotic upper extremity repetitive therapy”, IEEE Transactions on Neural Systems & Rehabilitation Engineering a Publication of the IEEE Engineering in Medicine & Biology Society, vol. 15, no. 3, pp. 336-46, 2007.

11. J. C Perry, J. Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design”, Mechatronics IEEE/ASME Transactions on, vol. 12, pp. 408-417, 2007.

12. A. U. Pehlivan, O. Celik, M. K. O’Malley, “Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation”, IEEE International Conference on Rehabilitation Robotics IEEE Int Conf Rehabil Robot, pp. 5975428, 2011.

13. S Koo, T. P. Andriacchi, “The Knee Joint Center of Rotation is Predominantly on the Lateral Side during Normal Walking[J]”, Journal of Biomechanics, vol. 41, no. 6, pp. 1269, 2008.

14. Y. Mao, S. K. Agrawal, “Transition from mechanical arm to human arm with CAREX: A cable driven ARm EXoskeleton (CAREX) for neural rehabilitation”, Proc. IEEE Int. Conf. Robot. Autom., pp. 2457-2462, 2012.

15. Y. Mao, X. Jin, G. G. Dutta, J. P. Scholz, S. K. Agrawal, “Human movement training with a cable driven ARm EXoskeleton (CAREX)”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 1, pp. 84-92, Jan. 2015.

16. DJ Reinkensmeyer, JL Emken, SC. Cramer, “Robotics motor learning and neurologic recovery”, Annual Review of Biomedical Engineering, vol. 6, no. 1, pp. 497-525, 2004.

17. QZ Yang, CF Cao, JH. Zhao, “Analysis of the status of the research of the upper-limb rehabilitative robot”, Robot, vol. 35, no. 5, pp. 630-640, 2013.

18. XZ Jiang, XH Huang, CH Xiong et al., “Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control”, Journal of Computational & Nonlinear Dynamics, vol. 7, no. 2, pp. 024502, 2012.

19. ZC Chen, Z. Huang, “Motor relearning in the application of the rehabilitation therapy for stroke”, Chinese Journal of Rehabilitation Medicine, vol. 22, no. 11, pp. 1053-1056, 2007.

20. JC Perry, J Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design[J]”, IEEE/ASME Transactions on Mechatronics, vol. 12, no. 4, pp. 408-417, 2007.

21. C LV, Research on rehabilitation robot for upper-limb hemiplegia, Shanghai China:, 2011.

22. Y K Woo, G H Cho, E Y. Yoo, Effect of PNF Applied to the Unaffected Side on Muscle Tone of Affected Side in Patients with Hemiplegia[J], vol. 9, no. 2, 2002.

23. JH Liang, JP Tong, X. Li, “Observation of the curative effect of continuous passive movement of joints in the treatment of lower limb spasticity”, Theory and practice of rehabilitation in China, vol. 14, no. 11, pp. 1067-1067, 2008.


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[Abstract] Design and development of an upper limb rehabilitation robotic system – IEEE Conference Publication


This paper presents ongoing research activities for the designing and development of an upper limb rehabilitation robotic system needed by the Prokinetic Rehabilitation Clinic. Prokinetic therapists identified the need of a passive rehabilitation robotic system (RRS) with 3 degrees of freedom for upper limb. The medical and technical requirements analysis for an upper limb rehabilitation robotic system, the video motion analysis were carried out, and the mechanical, actuation, control systems and human-machine interface of the RRS were designed and developed. The results of designing and developing an upper limb rehabilitation robotic system are presented here.
Date of Conference: 28-31 May 2018


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[Abstract] Kinematic analysis and control for upper limb robotic rehabilitation system – IEEE Conference Publication


Present physical rehabilitation practice implies one-to-one therapist — patient interactions. This leads to shortage of therapists and high costs for patient or healthcare insurance systems. Along with Prokinetic Rehabilitation Clinic, we proposed a new intelligent, adaptive robotic system (RAPMES), which can provide the rehabilitation protocols, defined by a therapist, for the wrist and elbow of upper limb, considering the patient reactions and based on real-time feedback. RAPMES is a passive rehabilitation robotic system (RRS) with 3 degrees of freedom, and assists the rehabilitation process for elbow, forearm and wrist movements. Computation of the kinematic model for the RAPMES robotic device is required in order to determine the parameters associated with the mechanical joints, so that the experimental model executes certain trajectories in space. In this paper, we will present both forward and inverse kinematics determined for the experimental model. The kinematic model was implemented in Matlab environment, and we present a series of simulations, conducted in order to validate the proposed kinematic model. Then, we impose the functional movements (determined using the real-time video motion analysis system, as polynomial movement functions) as input to the kinematic model, and we present a series of simulations and results. The RAPMES control algorithm includes the kinematic model, and uses the polynomial movement functions as control input.
Date of Conference: 28-31 May 2018


I. Introduction

Statistics shows that, at European Union level, the upper limb is second common body part injured, as a result of unintentional physical injury [1]. Also, one can note the shortage of therapists and high costs for patient or healthcare insurance systems. Current development in robotics may offer a solution for this problem [2], allowing the creation of robotic devices to support the rehabilitation process, in a supervised or unsupervised way, in physiotherapy clinics or at home. In this context, we proposed RAPMES, a new intelligent, adaptive robotic system, which can provide the rehabilitation protocols, defined by a therapist, for the wrist and elbow of upper limb, considering the patient reactions and based on real-time feedback. RAPMES robotic system is designed on an ongoing research project, which implies several stages of development. In a first stage, we conducted a study involving therapists, the personnel and devices existent in a physiotherapy clinic. The role of this study was to determine the requirements for the robotic device, and to reveal the specific therapeutic needs of patients with rehabilitation indications at wrist and elbow level. On a second stage, we used a real-time video motion analysis system, to determine and understand specific functional movements frequently made with the dominant upper limb, by healthy persons. One of our research objectives is to include these movements as a part of RAPMES control algorithm, as they may offer a better rehabilitation of the upper limb, for specific moves. Next, we designed the robotic device, based on findings described above, and realized an experimental model of the robotic device.

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[Abstract] Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton


    Adaptive integral sliding mode control design for exoskeletons.

    Finite time convergence of the closed-loop system.

    Robustness of the control law with respect to parametric variations and disturbances.

    No requirement of the knowledge of the system bounds.

    Real experiments using an upper limb exoskeleton with and without human subjects.


A robust adaptive integral terminal sliding mode control strategy is proposed in this paper to deal with unknown but bounded dynamic uncertainties of a nonlinear system. This method is applied for the control of upper limb exoskeleton in order to achieve passive rehabilitation movements. Indeed, exoskeletons are in direct interaction with the human limb and even if it is possible to identify the nominal dynamics of the exoskeleton, the subject’s limb dynamics remain typically unknown and defer from a person to another. The proposed approach uses only the exoskeleton nominal model while the system upper bounds are adjusted adaptively. No prior knowledge of the exact dynamic model and upper bounds of uncertainties is required. Finite time stability and convergence are proven using Lyapunov theory. Experiments were performed with healthy subjects to evaluate the performance and the efficiency of the proposed controller in tracking trajectories that correspond to passive arm movements.


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[BOOK] Rehabilitation Robotics: Technology and Applications – Rehabilitation Robotics

Cover image The last decades have seen major advances in interventions for neuromotor rehabilitation. Forms of treatment based on repetitive exercise of coordinated motor activities have been proved effective in improving gait and arm functions and ultimately the patients’ quality of life. Exercise-based treatments constitute a significant burden for therapists and are heavy consumers of health-care resources. Technologies such as robotics and virtual reality can make them more affordable.

Rehabilitation robotics specifically focuses on systems—devices, exercise scenarios, and control strategies—aimed at facilitating the recovery of impaired sensory, motor, and cognitive skills. The field has a relatively long history, dating back to the early 1990s. Early attempts were part of the general trend toward automating heavy tasks by using “intelligent” machines, with minimal human intervention. The notion of “artificial therapist” was common in early scientific papers and patent applications. However, the most distinctive feature of these devices is not their ability to “automate” treatment but, rather, that of precisely quantifying sensorimotor performance during exercise, in terms of movement kinematics and exchanged forces. This resulted in a gradual shift toward more evidence-based and data-driven forms of treatment. Present-generation rehabilitation robots are designed as complements, rather than substitutes, of the therapist’s work. They support the recovery of functions by efficiently exploiting structure and adaptive properties of the human sensorimotor systems and provide rich information on sensorimotor performance and their evolution. Their design, implementation, and modalities of intervention incorporate findings from behavioral studies on sensorimotor adaptation and motor skill learning and their neural substrates.

Rehabilitation robotics is therefore characterized by highly specific design approaches and technical solutions, with roots in both engineering and neurophysiology.

This book addresses both technology and application aspects of Rehabilitation Robotics. Part I focuses on the state of the art and representative advancements in the design, control, analysis, and implementation of rehabilitation robots and the underlying neurophysiological principles. Part II addresses the existing applications and the clinical validation of these systems, with a special emphasis on therapy robots, which support exercise-based treatments aimed at recovering sensorimotor or cognitive functions.

PART I: Background and Technology


Planning and execution of movements results from the coordinated activity of multiple interconnected sensory and motor areas in the cerebral cortex. When an area in this specialized motor network is damaged—for example, through a traumatic brain injury or an ischemic event—the activity of the motor networks can be disrupted, thus leading to functional deficits. How the surviving motor networks reorganize to compensate for the injury depends on the location and extent of the lesion but may be affected by sensorimotor exercise.

Chapter 1 summarizes how neuroplasticity modifies motor networks in response to injury, by focusing on the changes after a cerebrovascular accident in the primary motor cortex. Neuroanatomical and neurophysiological evidence in animal models and human stroke survivors is reviewed to demonstrate how injuries functionally impair motor networks, how motor networks compensate for the lesion to improve motor function, and how selected therapies may facilitate recovery.

Chapter 2 focuses on the hierarchical architecture and synergistic functioning of the motor system. These aspects are crucial for the development of successful robotics applications with rehabilitation purposes. The same framework is used to discuss the mechanisms underlying rehabilitation interventions with a potential to facilitate the recovery process.

Technology and Design Concepts

Devices for rehabilitation benefit from advances in robot technologies, including sensors and actuators, mechanical architectures, and the corresponding control architectures. These devices are characterized by a continuous interaction with the human body, which poses specific design constraints.

Chapter 3 summarizes the notion of “biomechatronic” design for systems for robot-mediated rehabilitation, encompassing robot structure, musculoskeletal biomechanics, and neural control. Robots for rehabilitation are typically conceived to constantly work in constrained motion with the human body, which represents a challenge for designers. This requires a top-down design approach, in which a model of the human agent guides a concurrent, iterative design cycle of the robot’s mechanical, electronic, and multilayered control subsystems. Criteria for the identification of functional and technical specifications and the selection of key components of the robotic system are also derived. Two design case studies demonstrate how these design principles are translated into practice.

Chapter 4 addresses how actuators play a critical role in defining the characteristics of the robot-patient interaction. The different options for actuating and controlling a rehabilitation device are discussed, considering the complex flow of information between the user and the robot during a rehabilitation task. Strategies for both high- and low-level control are presented. Impedance and admittance control modalities are discussed as means of decoding human intention and/or modulating the assistive forces delivered by the robot. Mathematical tools for model-based compensation of nonlinear phenomena (backlash and friction) are also presented.

The way robots are used to facilitate training is crucial for their application to therapy and has important implications for their mechanical and control design. Intensity and frequency of practice are major determinants of the recovery process, but different exercise modalities are possible. Robots may be used for haptic rendering in virtual environments, to provide forces that facilitate task performance or task completion, and/or to make a task more difficult and challenging.

Chapter 5 reviews the control strategies for robotic therapy devices and summarizes the techniques for implementing assistive strategies, including counterbalance techniques and adaptive controllers that modify control parameters based on the patient’s ongoing performance.

Personalized treatment is becoming increasingly popular in neurorehabilitation. Two chapters discuss how new design techniques such as exoskeletons or wearable robots are applied to the design of modern therapy robots, for either upper or lower limb rehabilitation.

Chapter 6 specifically addresses the design of exoskeletons for upper-limb rehabilitation. After an introduction of the rationale behind the selection of this robot architecture and a review of the available solutions for actuation, the chapter discusses the state of the art and the most commonly adopted solutions. An overview of clinical evidences of upper-limb rehabilitation with exoskeletons is then provided, discussing evidences in favor of training with exoskeleton devices.

Chapter 7 reviews the current state and clinical effectiveness, safety, and usability of exoskeletons for gait rehabilitation. It provides an overview of the actuation technologies, including compliant and lightweight solutions. Control strategies aimed at guiding the patient according to his/her needs and encouraging his/her active participation are also discussed. Novel perspectives for “symbiotic” human-exoskeleton interaction based on interfaces with neural structures are also introduced.

Computational Neurorehabilitation

One important feature of therapy robots is that they integrate both therapeutic and measuring functionalities. Therapy robots have built-in technology and sensors that measure movement kinematics and kinetics, thus providing an accurate assessment of motor function by which it is possible to diagnose the patient state and to evaluate patient performance and their progress during treatment. The availability of quantitative information has triggered an entirely new paradigm for neurorehabilitation, unifying clinical assessment, and exercise. Computational neurorehabilitation is a new and emerging field, which uses modern data analysis and modeling techniques to understand the mechanisms of neural plasticity and motor learning, and incorporates this knowledge into personalized, data- and model-driven forms of treatment.

Chapter 8 reviews the quantitative measures—encompassing kinematic, kinetic, timing, sensory, and neuromechanical aspects of performance—which are most frequently used to describe motor behavior during robot-assisted rehabilitation of the upper limb. The chapter also analyzes how these indicators are used to monitor motor recovery during exercise, to understand the evolution of performance, and to precisely plan and, if necessary, modify the rehabilitation strategies. The relationship between robot-derived measures and their clinical counterparts is also discussed.

Chapter 9 addresses computational models for neuromotor recovery, with a focus on state-space models that describe the development of functional behaviors through exercise and the relation between neuromotor recovery and motor learning. The chapter first reviews models of the dynamics of sensorimotor adaptation and motor skill learning and then elaborates on similarities and differences with neuromotor recovery. Finally, it discusses how these models can be used to achieve a better understanding of the role of robots to promote recovery and to develop personalized forms of treatment.

Chapter 10 proposes a general framework to model the interaction between robot and patient during robot-assisted training. Human and robot are modeled as two agents, whose respective tasks are described by two cost functions. Optimal interaction strategies are then derived in terms of differential game theory. This approach allows to describe different forms of human-robot interaction. A specific prediction is that optimal interaction requires that the robot maintains a model of the behavior of its human partner. In this case, simulations and empirical studies exhibit more stable, reactive, and adaptive interaction. This form of “symbiotic” interaction is a step toward defining what it takes for robots to behave as “optimal” trainers.

Chapter 11 addresses the strategies implemented in rehabilitation robots to promote patient motivation, which is a major determinant of recovery through exercise. Motivation may be measured with self-report questionnaires or with indirect, more objective measures, such as exercise duration. Motivation may be promoted through interaction with virtual environments, which may consist of activities of daily living, which emphasize relatability, or games, which emphasize enjoyment. The design of these environments must take the hardware, the patients’ characteristics, and goal-related feedback into account. Motivation during exercise must be maintained by regulating task difficulty, thus ensuring an appropriate “challenge level.”

Software Environments for Rehabilitation Robotics

As a natural conclusion of this methodological section,  Chapter 12 reviews the software development environments that can be used to implement the different levels of control of a modern rehabilitation robot. The robotic field suffers from a lack of standardization in programming environments. Hence, it is not surprising that even in the specific context of rehabilitation robotics, there is currently no consensus on specific software and hardware platforms. The chapter surveys different solutions used for combining robots (and, more in general, haptic interfaces) and virtual environments. Advantages and disadvantages of each of these environments are discussed, together with typical applications, with a focus on upper-limb rehabilitation.

PART II: Applications

The second part of the book addresses the application of rehabilitation robots in different pathologies for training of diverse districts (upper and lower limb) and using different training strategies.

High Intensity, Assist-As-Needed Therapy to Improve Motor Functions

Chapter 13 provides an overview of 28 + years of efforts at MIT’s Newman Laboratory for Biomechanics and Human Rehabilitation for the developments of robotic tools to assist in the neurorecovery process. After a definition of the basic principles that are core for successful rehabilitation robotics technology, the chapter presents a snapshot of few of MIT’s rehabilitation robots, discusses the results of metaanalyses for upper extremity robotics, and finally presents two exciting examples for acute and chronic stroke. Overall, the above material points out that robotic therapy for the upper extremity that involves an interactive high-intensity, intention-driven therapy based on motor learning principles and assist-as-needed leads to better outcomes than usual care in both acute/subacute and chronic stroke.

The above principles have been extended to training in a three-dimensional workspace, using robots with an exoskeleton structure.  Chapter 14 describes the application of one of the first architectures developed with the purpose of mirroring the anatomical structure of the human arm and of enabling task-oriented training in the 3D space, mimicking activities of daily living.

Hand and finger functions are of critical importance for independence in everyday activities, but their recovery is often limited following neurological injury. This has motivated the development of novel therapeutic and assistive tools.  Chapter 15 provides a comprehensive overview of robotic approaches for the rehabilitation of hand function and underlines their potential to complement conventional rehabilitation. First, the design concepts of existing hand exoskeletons and end-effector devices are presented. Then, clinical evidence that underlines the feasibility of robot-assisted rehabilitation of hand function is presented. Finally, promising research directions are discussed to further exploit the potential of robot-assisted rehabilitation of hand function in neurological patients.

Robot-assisted gait training typically involves body-weight support and physical guidance to move the legs into the correct pattern. Gait rehabilitation robots allow greater exercise duration and movement repetitions; improve patient safety and motivation; reduce the therapists’ burden; and, eventually, improve the therapeutic outcome.  Chapter 16 introduces the rationale for robot-assisted gait training. In particular, existing gait-rehabilitation robots and their control strategies are presented. The available clinical trials are also summarized, showing that training with robotic rehabilitation devices is at least as effective as conventional physiotherapy. Further clinical studies are required in order to define the most appropriate robotic technical features based on the task, patients’ type, and degree of impairment.

Wearable systems open new perspectives for rehabilitation in individuals with disabilities, which can lead to difficulty in walking or making arm movements since they could be used to facilitate independent training in the clinic or at home. Wearable systems range from complex rigid exoskeleton structures for the assistance of joints or limbs to hybrid, soft, and interactive systems. The existing solutions are not yet widely used in clinical environments. The aim of  Chapter 17 is to review the scientific challenges and the current developments of wearable systems and to discuss their clinical potential.

Robots Not Only for Stroke Rehabilitation

Although most applications of robot rehabilitation focus on stroke and traumatic brain injury, these devices may find application in the treatment of other pathologies.

Chapter 18 addresses robot-assisted rehabilitation in multiple sclerosis (MS). Robot-assisted training leads to improved movement quality on reaching tasks, but clinical effects on standard assessment have not been always observed after multiple-session training. An increasing number of studies report effects of a multiple-week training program, but the magnitude of the effect was often similar to conventional training programs. Overall, there is evidence supporting the beneficial effect of robot- and technology-supported training, but its superiority compared with other or conventional treatment programs is still debatable. Research investigating the impact of different technological settings and the motor learning strategies implemented in technology must be encouraged for MS patients.

Persons with cognitive deficits are a completely different target population that can be addressed by therapy robots. Cognitive rehabilitation therapy (CRT) is a set of interventions designed to enhance cognitive performance. Ideally, CRT engages the participant in a learning activity to enhance neurocognitive skills relevant to the overall recovery goals. There is ongoing research to identify the determinants of a positive response to treatment.  Chapter 19 addresses the use of rehabilitation robots, socially interactive robots (SIR), and socially assistive robots (SAR), both virtual and embodied, to enhance, restore, or prevent early deterioration of cognitive abilities related to neurodegenerative disease or injury.

Integrating Robot Therapy With Neuro- and Psychophysiological Techniques

All the techniques and devices described until now use robot technology alone. Integration of different approaches and different technologies may improve the outcome, for instance, by training and restoring different functions within the same training session or by using physiological signals to monitor and/or control the recovery process. The following chapters focus on the use of neuro- and psychophysiological signals to enhance or complement robot-assisted therapy.

Chapter 20 presents hybrid FES-robot devices for training of activities of daily living aiming at the parallel restoration of functions by the external activation of paralyzed muscles and external mechanical support of postural functions. The combination of two modalities within the same treatment may multiply their individual effects as the external activation of muscles eliminates the need for large mechanical actuators and reduces the number of degrees of freedom to a controllable domain and, on the other hand, robot guidance removes the need for prolonged, fatiguing stimulation of muscles.

In spite of the acknowledged importance of proprioception for motor control and neuromotor rehabilitation, no effective method for assessment and rehabilitation of proprioceptive deficits has emerged in clinical practice. While there are many clinical scales for assessing proprioception, they all have insufficient psychometric properties and cannot be used in closed-loop treatment paradigms wherein treatment parameters are monitored and adjusted online or with a trial-by-trial frequency.  Chapter 21 discusses how robots can simultaneously address two interrelated needs: to provide sensitive and repeatable assessments of proprioceptive integrity and to automate repetitive training procedures designed to enhance proprioception and its contributions to functional movement.

The outcome of a training program can be conditioned not only by the patient’s physical conditions but also by his/her psychophysiological state during the whole course of the rehabilitation program.  Chapter 22 reviews psychophysiological response modalities that, together with task performance parameters and biomechanical measurements, may be used in a biocooperative approach to rehabilitation. The chapter focuses in particular on electrocardiogram, skin conductance, respiration signal, and peripheral skin temperature. Each signal is described in terms of acquisition modalities, signal processing, and features extraction. The psychophysiological responses in the case of multimodal challenge and physical activity are also examined, with reference to the differentiation of arousal and valence.

Understanding the mechanisms underlying muscle coordination during daily motor activities is a fascinating challenge in neuroscience and may provide important information pertaining to the recovery strategies of the neuromuscular system. Muscle synergies have been hypothesized as a neural strategy to simplify the control of the redundant motor actuators leading human movement and as a method to study motor coordination in healthy and neurological subjects.  Chapter 23 presents the theoretical framework for the extraction and the description of muscle synergies. Moreover, it summarizes how neuropathologies impact on muscle synergies and their potential for neurorehabilitation. Finally, it discusses how muscle synergies can be used to assess the effectiveness of robot-aided rehabilitation and the design of innovative control strategies.

Robots and Information Technologies Advances Toward Long-Term Intervention

As the world’s population ages, the management of chronic diseases will become more important. This shift will put pressure on health-care systems that often focus on providing effective care while reducing costs. The use of technological advancements to augment health-care services provides a method to meet these demands. Telerehabilitation robotics, addressed in  Chapter 24, combines established features of robot-assisted rehabilitation and tele-health care to provide distance rehabilitation services. While there is a growing market of robotic devices used in traditional rehabilitation settings, home-based implementations provide a unique set of challenges (e.g., remote monitoring, deployment constraints, and data management) that has limited the number of successful solutions. Clinical and kinematic outcomes show promising results and support further investigation. Cost analyses have demonstrated that telerehabilitation robotics is a cost-effective alternative compared with clinic-based therapy. While telerehabilitation robotics is a promising addition to conventional care, numerous barriers that limit practical integration will need to be addressed to allow a more widespread acceptance and use of this approach in rehabilitation.

As a final remark, robot rehabilitation involving an interactive high-intensity, intention-driven therapy based on motor learning principles and assist-as-needed leads to better outcomes than usual care in acute/subacute, chronic stroke and other pathologies. For this reason, clinical guidelines recommend the application of these technologies for the recovery of the lost functions.

This book highlights the most important technical aspects and strategies for the design, development, and application of robot technologies for rehabilitation purposes. With their ability to adapt exercise parameters based on physiological signals, objective and sensitive metrics reflecting the state and performance of a patient, the unique possibility to combine motor and somatosensory training, and the perspective of simple and wearable tools for home rehabilitation, robot devices promise further potential for the rehabilitation of neurological patients aiming at an improved motor function, a reduction of their disability, and overall an improved quality of life.


via Rehabilitation Robotics: Technology and Applications – Rehabilitation Robotics


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[ARTICLE] Hand exoskeleton for rehabilitation therapies with integrated optical force sensor – Full Text

This article presents the design of a hand exoskeleton that features its modularity and the possibility of integrating a force sensor in its frame. The modularity is achieved by dividing the exoskeleton in separate units, each one driving a finger or pair of them. These units or “finger modules” have a single degree of freedom and may be easily attached or removed from the robot frame and human fingers by snap-in fixations. As for the force sensing capability, the device relies on a novel force sensor that uses optical elements to amplify and measure small elastic deformations in the robot structure. This sensor can be fully integrated as a structural element of the finger module. The proposed technology has been validated in two experimental sessions. A first study was performed in a clinical environment in order to check whether the hand exoskeleton (without the integrated force sensor) can successfully move an impaired hand in a “Mirror Therapy” environment. A second study was carried with healthy subjects to check the technical feasibility of using the integrated force sensor as a human–machine interface.

A wide diversity of robotic devices, which can actuate/assist the movements of the human hand, can be found in the current scientific literature.1 Depending on the application, a hand exoskeleton may require uneven features. For example, a rehabilitation-aimed exoskeleton needs to be fairly backdrivable and allows a wide range of movement, so it is flexible enough to perform different rehabilitation exercises.2 In contrast, an assistance exoskeleton must be stiff enough to ensure a firm grasping of objects present during activities of daily living and can sacrifice flexibility of movement in favor of predefined grasping patterns.

These different requirements result on diverse force transmission architectures:

  • Some devices use linkages in order to transmit the force from the actuator to the human joints.35 This is a stiff architecture that requires a proper alignment between kinematic centers of the linkage and human joints, but allows a good control of the hand pose. Due to the flexibility of the design, with the correct sizing, these mechanisms can achieve complex movement patterns with simple actuators.
  • Another extended architecture is the cable-driven glove.68 These are more flexible and simpler alternatives that rely on the own human joints to direct the movement, so they are less prone to uncomfortable poses. In contrast, they require pulleys to achieve high forces and are harder to control in intermediate positions. Additionally, this kind of exoskeletons need a pair of cables in antagonist configuration in order to assist both extension and flexion movements.
  • Finally, some devices use deformable actuators, like pneumatic muscles or shape-memory alloys, attached directly to the hand by means of a glove.9,10 They result in very light and simple devices, but actuators are not placed in the most advantageous place to achieve great forces.

Regarding the exoskeletons based on linkages, especially those which rely on electric actuators, having a measurement of the interaction force between user and device may result an interesting feature in order to ease control tasks and improve safety. In certain devices, different sensor technologies have been implemented, such as torque sensors,11 strain gauges,12 flexion sensors,13 and miniature load cells.14 These sensors may be effective in their respective applications but present some shortcomings for their integration in exoskeletons. In particular, torque sensors measure loads in the motor shaft so, in over-constrained mechanisms, they might not measure all the interaction forces. Strain gauges are complex to fix in the proper place and shorter ones may not perform correctly, so for being usable they require geometries with size comparable to human phalanxes. Another miniature sensors, like load cells or force-sensitive resistors, normally can measure force in only one sense (compression or extension) and those that can measure both directions are too big for the scale of the human hand.

Research background and objectives

In our previous paper,15 we studied the feasibility of using multimodal systems in order to assist post-stroke patients during the execution of rehabilitation therapies with real objects. In this context, we evaluated the suitability of using a hand exoskeleton device,16 such as the aforementioned ones, for assisting an impaired person during the grasping of objects present in activities of daily living. This device has experienced substantial improvements with respect to the previous design in order to be able to interact safely with disabled users.

In that previous experimentation, the electromyographic (EMG) signal of the forearm muscles was proposed as a method to estimate user’s intention and consequently trigger the open/close movement of the hand exoskeleton. This method proved to be effective, but it can be used only for users with a coherent and relatively strong EMG signal, which might not be the case for most patients.17 From these results, there is a need for additional technologies that can detect the movement intention of the subject in order to cope with a wider range of user profiles.

Despite that the presented device will also be used in assistive context, the objective of the exposed research is to show whether the proposed improvements of the hand exoskeleton, including a miniature optical force sensor, allow its use in a real rehabilitation environment. Special attention will be given to the development of a force sensing method in order to measure the human–robot interaction forces and therefore to estimate user’s intention in rehabilitation scenarios.

Hand exoskeleton

Among the different existing architectures, we have decided to implement an exoskeleton based on the linkage approximation, since we consider that this is the most flexible solution in order to achieve a good compromise between the requirements of both rehabilitation and assistance scenarios. The motion transmission is based on a bar mechanism that allows the possibility of coupling the motion of phalanxes, so a natural hand movement is achievable using only one active degree of freedom per finger. Additionally, bars can transmit both tensile and compressive loads so the same mechanism is able to perform extension (most demanding movement in rehabilitation) and flexion (mandatory for assistance) movement of the fingers.

In detail, the designed exoskeleton is composed by three identical finger modules that drive index, middle and the pair formed by ring and little fingers. Each finger module has a single degree of freedom actively driven by a linear actuator. Unlike many of the referenced exoskeletons, due to the inherent uncertainty introduced by the human–exoskeleton interface (modeled as a slide along the phalanx longitudinal axis in Figure 1), we have decided not to rely on the human finger as the element that closes the kinematic chain. Conversely, we have adopted an approach similar to the one adopted by Ho et al.5 This way, adding a pair of circular guides whose centers are coincident with the joints of a reference finger, the mechanism is kinematically determinate without needing the human finger. Ho’s device uses slots with flange bearings to implement the guides; this may result effective but requires precision machining and miniature elements to achieve a compact solution. In contrast, we have designed a double-edged guide that slides between four V-shaped bearings (Figure 2). These elements allow the optimization of the required space and may be easily manufactured by prototyping technologies or plastic molding. To make up for the additional constraints, we have decided to actuate only medial and proximal phalanxes.



Figure 1. Kinematics scheme of the finger linkage attached to the human finger. Metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints have been modeled as revolute joints. Additionally, the interface between the module and the phalanxes has been modeled by means of slide.



Figure 2. Left: Finger module represented in its extreme positions. Right: Detailed view of the designed circular guide to minimize mechanical clearances with minimum friction.


Continue —>  Hand exoskeleton for rehabilitation therapies with integrated optical force sensor – Jorge A Díez, Andrea Blanco, José María Catalán, Francisco J Badesa, Luis Daniel Lledó, Nicolas García-Aracil, 2018

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