Posts Tagged robots

[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential


Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a two-degrees-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied rms torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz, and an impedance of less than 0.03 Nm/° at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/°. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

via An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential – IEEE Journals & Magazine

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[Abstract] Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation


A key approach for reducing motor impairment and regaining independence after spinal cord injuries or strokes is frequent and repetitive functional training. A compatible exoskeleton (Co-Exos II) is proposed for the upper-limb rehabilitation. A compatible configuration was selected according to optimum configuration principles. Four passive translational joints were introduced into the connecting interfaces to adapt the glenohumeral joint (GH) movements and improve the compatibility of the exoskeleton. This configuration of the passive joints could reduce the influence of gravity of the exoskeleton device and the upper extremities. A Co-Exos II prototype was developed and still owned a compact volume. A new approach was presented to compensate the vertical GH movements. The shoulder closed-loop was simplified as a guide-bar mechanism. The compatible models of this loop were established based on the kinematic model of GH. The compatible experiments were completed to verify the kinematic models and analyze the human-machine compatibility of Co-Exos II. The theoretical displacements of the translational joints were calculated by the kinematic model of the shoulder loop. The passive joints exhibited good compensations for the GH movements through comparing the theoretical and measured results, especially vertical GH movements. Co-Exos II showed good human-machine compatibility for upper limbs.

via Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation* – IEEE Conference Publication

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation


Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available:

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from [Online]. Available:

via Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation – IEEE Conference Publication

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[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation


In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.


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[Abstract] The wearable hand robot: supporting impaired hand function in activities of daily living and rehabilitation


Our hands are very important in our daily life. They are used for non-verbal communication and sensory feedback, but are also important to perform both fine (e.g. picking up paperclips) and gross (e.g. lifting heavy boxes) motor tasks. Decline of hand function in older adults as a result of age-related loss of muscle mass (i.e. sarcopenia) and/or age-related diseases such as stroke, rheumatoid arthritis or osteoarthritis, is a common problem worldwide. The decline in hand function, in particular grip strength, often results in increased difficulties in performing activities of daily living (ADL), such as carrying heavy objects, doing housework, (un)dressing, preparing food and eating.
New developments, based on the concept of wearable soft-robotic devices, make it possible to support impaired hand function during the performance of daily activities and intensive task-specific training. The ironHand and HandinMind systems are examples of such novel wearable soft-robotic systems that have been developed in the ironHand and HandinMind projects. Both systems are developed to provide grip support during a wide range of daily activities. The ironHand system consists of a 3-finger wearable soft-robotic glove, tailored to older adults with a variety of physical age-related hand function limitations. The HandinMind system consists of a 5-finger wearable soft-robotic glove, dedicated towards application in stroke. In both cases, the wearable soft-robotic system could be connected to a computer with custom software to train specific aspects of hand function in a motivating game-like environment with multiple levels of difficulty. By adding the game environment, an assistive device is transformed into a dedicated training device.
The aim of the current thesis is to define user requirements, to investigate feasibility and to evaluate the direct and clinical effects of a wearable soft-robotic system that is developed to support impaired hand function of older adults and stroke patients in a wide range of daily activities and in exercise training at home.

via The wearable hand robot: supporting impaired hand function in activities of daily living and rehabilitation — University of Twente Research Information

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[WEB SITE] These Friendly Helpful Robots Will Likely Be Your Future Rehabilitation Partners

A new study has revealed that socially assistive robots (SARs), though already in use, will continue to see a rise as they become more suited to human relations.

By  August, 20th 2018

From K5s who patrols our local streets and parking areas to a host of bots which serve as personal assistants at home and on the go, programmable machines are increasingly entering our lives in new and dynamic ways. Still, the challenge of integrating robotics into heavily human-dependent labor such as retail and medical assistance remains a challenge.

Rehabilitation robots

A multidisciplinarian team of researchers at Freiburg University assessed the potential impact of robots in the area of physical rehabilitation in the future. The study, led by Dr. Philipp Kellmeyer, a neuroscientist in the University’s Medical Center, and Prof. Dr. Oliver Müller, a professor from the philosophy department found that socially assistive robots (SARs), though already in use, by all indications will be used increasingly more.

As the world’s population continues to grow, and with improved medical procedures improving post-op recovery rates and extending people’s average lifespan, SAR demand will inevitably increase.

Beyond continuing the research and development process to improve the technical capabilities of these helpful bots, much attention, the team concluded, should be given to developing strategies for how to create a relationship between SARs and patients. Few of us, especially those who have gone through the pain and frustration involved in physical rehabilitation, would disagree that the rapport with a health services professional becomes the main factor in maintaining the patient’s motivation.

These Friendly Helpful Robots Will Likely Be Your Future Rehabilitation Partners
Source: RAPP

Are we setting the bar too high for SARs?

Though SARs still serve as assistants in the rehabilitation process, not the main role, it is still crucial to clearly define just what that role will be, and what it will look like throughout the rehabilitation process. This is key as SARs assist patients in three different areas: people with cognitive disabilities, people who require rehabilitation, and ageing or elderly patients.

In a previous study titled “The Grand Challenges in Socially Assistive Robotics”, a team of researchers classified the most important components for effective SAR design in six categories:

The robot’s physical embodiment (including physical, responsive and cultural aspects)

Personality, which is, in essence, the main factor in achieving successful human-robot interactions

Empathy, which is a relative concept, is central. The researchers shared from their observations: “Machines cannot feel empathy. However, it is possible to create robots that display overt signs of empathy. In order to emulate empathy, a robotic system should be capable of recognizing the user’s emotional state, communicating with people, displaying emotion, and conveying the ability of taking perspective.”

The relative level of engagement with patients, which includes verbal and non-verbal communication

Adaptation, which involves learning from an environment and quickly implementing lessons into the patient interaction.

Transfer, which focuses on long-term behavioral changes of the SAR.

Though by no means trying to build the perfect robots or a human replacement, due to the delicate nature of this work, it’s important for those involved in SAR design to continue to have discussions about small to significant ways to improve the patient experience.

With a title that truly gets to the heart of the matter, the study  “Social robots in rehabilitation: A question of trust” is published in the Science Robotics journal this month.

via These Friendly Helpful Robots Will Likely Be Your Future Rehabilitation Partners

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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.

via Kinematic analysis and control for upper limb robotic rehabilitation system – IEEE Conference Publication

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[Abstract] A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training


Objective: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address muscle strength deficits in a task-specific manner can assist in the recovery of arm function; however, current devices are typically large, bulky, and expensive to be routinely used in the clinic or at home. This study sought to address this issue by developing a portable planar passive rehabilitation robot, PaRRo. Methods: We designed PaRRo with a mechanical layout that incorporated kinematic redundancies to generate forces that directly oppose the user’s movement. Cost-efficient eddy current brakes were used to provide scalable resistances. The lengths of the robot’s linkages were optimized to have a reasonably large workspace for human planar reaching. We then performed theoretical analysis of the robot’s resistive force generating capacity and steerable workspace using MATLAB simulations. We also validated the device by having a subject move the end-effector along different paths at a set velocity using a metronome while simultaneously collecting surface electromyography (EMG) and end-effector forces felt by the user. Results: Results from simulation experiments indicated that the robot was capable of producing sufficient end-effector forces for functional resistance training. We also found the endpoint forces from the user were similar to the theoretical forces expected at any direction of motion. EMG results indicated that the device was capable of providing adjustable resistances based on subjects’ ability levels, as the muscle activation levels scaled with increasing magnet exposures. Conclusion: These results indicate that PaRRo is a feasible approach to provide functional resistance training to the muscles along the upper extremity. Significance: The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy.

via A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training – IEEE Journals & Magazine

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[Abstract] Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact


Recent technological developments regarding wearable soft-robotic devices extend beyond the current application of rehabilitation robotics and enable unobtrusive support of the arms and hands during daily activities. In this light, the HandinMind (HiM) system was developed, comprising a soft-robotic, grip supporting glove with an added computer gaming environment. The present study aims to gain first insight into the feasibility of clinical application of the HiM system and its potential impact. In order to do so, both the direct influence of the HiM system on hand function as assistive device and its therapeutic potential, of either assistive or therapeutic use, were explored. A pilot randomized clinical trial was combined with a cross-sectional measurement (comparing performance with and without glove) at baseline in 5 chronic stroke patients, to investigate both the direct assistive and potential therapeutic effects of the HiM system. Extended use of the soft-robotic glove as assistive device at home or with dedicated gaming exercises in a clinical setting was applicable and feasible. A positive assistive effect of the soft-robotic glove was proposed for pinch strength and functional task performance `lifting full cans’ in most of the five participants. A potential therapeutic impact was suggested with predominantly improved hand strength in both participants with assistive use, and faster functional task performance in both participants with therapeutic application.

I. Introduction

Neurorehabilitation research has shown that training programs for patients after stroke should ideally consist of high intensity, task-specific and functional exercises with active contribution of the patient, to have the best chance for improving arm/hand function [1], [2]. Conventional rehabilitation involves predominantly one-to-one attention of a therapist for each patient, which is a challenge when aiming to provide high intensity training and involves high costs [3], [4]. This is impeded further by an increased ageing of the population, associated with a higher prevalence of stroke patients and less healthcare professionals available to provide such intensive training.


via Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact – IEEE Conference Publication

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