Posts Tagged exoskeletons

[Abstract + References] Auto-LEE: A Novel Autonomous Lower Extremity Exoskeleton for Walking Assistance – IEEE Conference Publication


Wearable exoskeletons have been proven to be efficacious in aiding walking for individuals suffering from lower limb mobility disorder. However, the application of most existing devices is limited to inconvenience of usage, e.g., complicated training and unnatural gait. This paper presents a novel autonomous lower extremity exoskeleton, Auto-LEE, for the purpose of improving the practicality of walking assistive devices as well as simplifying their application process. The developed exoskeleton consists of two robotic legs, and each of them has 5 active degrees-of-freedom (DOFs) to independently control the rotations of hip, knee and ankle joints in the sagittal and coronal planes, which enables the device to possess self-balancing ability and flexible gait. The modular design concept is introduced into the structure and hardware development of Auto-LEE, making it more convenient to be assembled and maintained. In order to validate the self-balancing walking ability, virtual prototype simulation and preliminary experiment on flat terrain are implemented.
1. “Spinal cord injury facts and figures at a glance”, 2018, [online] Available:

2. J. W. Mcdonald, C. Sadowsky, “Spinal-cord injury”, Lancet, vol. 359, no. 9304, pp. 417-425, 2002.

3. D. L. Brown-Triolo, M. J. Roach, K. Nelson, R. J. Triolo, “Consumer perspectives on mobility: implications for neuroprosthesis design”, Journal of Rehabilitation Research & Development, vol. 39, no. 6, pp. 659-669, 2002.

4. A. Tsukahara, Y. Hasegawa, K. Eguchi, Y. Sankai, “Restoration of gait for spinal cord injury patients using hal with intention estimator for preferable swing speed”, IEEE Transactions on Neural Systems & Rehabilitation Engineering, vol. 23, no. 2, pp. 308-318, 2015.

5. A. D. Gardner, J. Potgieter, F. K. Noble, “A review of commercially available exoskeletons’ capabilities”, International Conference on Mechatronics and Machine Vision in Practice, pp. 1-5, 2017.

6. M. Talaty, A. Esquenazi, J. E. Briceno, “Differentiating ability in users of the rewalk(tm) powered exoskeleton: an analysis of walking kinematics”, IEEE International Conference on Rehabilitation Robotics, pp. 6650469, 2013.

7. “Indego”, 2018, [online] Available:

8. “Ekso”, 2018, [online] Available:

9. K. A. Strausser, T. A. Swift, A. B. Zoss, H. Kazerooni, “Prototype medical exoskeleton for paraplegic mobility: First experimental results”, ASME 2010 Dynamic Systems and Control Conference, pp. 453-458, 2010.

10. Y. Mori, T. Taniguchi, K. Inoue, Y. Fukuoka, N. Shiroma, “Development of a standing style transfer system able with novel crutches for a person with disabled lower limbs”, Jsdd, vol. 5, no. 5, pp. 83-93, 2011.

11. K. Kong, D. Jeon, “Design and control of an exoskeleton for the elderly and patients”, IEEE/ASME Transactions on Mechatronics, vol. 11, no. 4, pp. 428-432, 2006.

12. K. Kong, H. Moon, B. Hwang, D. Jeon, M. Tomizuka, “Impedance compensation of subar for back-drivable force-mode actuation”, IEEE Transactions on Robotics, vol. 25, no. 3, pp. 512-521, 2009.

13. Y. Fang, Y. Yu, F. Chen, Y. Ge, “Dynamic analysis and control strategy of the wearable power assist leg”, IEEE International Conference on Automation and Logistics, pp. 1060-1065, 2008.

14. S. Zhang, C. Wang, X. Wu, Y. Liao, X. Hu, C. Wu, “Real time gait planning for a mobile medical exoskeleton with crutche”, IEEE International Conference on Robotics and Biomimetics, pp. 2301-2306, 2016.

15. D. Zanotto, P. Stegall, S. K. Agrawal, “Alex iii: A novel robotic platform with 12 dofs for human gait training”, IEEE International Conference on Robotics and Automation, pp. 3914-3919, 2013.

16. M. Cenciarini, A. M. Dollar, “Biomechanical considerations in the design of lower limb exoskeletons”, IEEE International Conference on Rehabilitation Robotics, pp. 5975366, 2011.

17. B. Protection, M. Labour, “Gb10000-88 human dimensions of chinese adults”.

18. S. Gao, Practical anatomical atlas: lower limbs volume, 2004.

19. S. Kajita, F. Kanehiro, K. Kaneko, K. Yokoi, “The 3d linear inverted pendulum mode: a simple modeling for a biped walking pattern generation”, Ieee/rsj International Conference on Intelligent Robots and Systems 2001. Proceedings, vol. 1, pp. 239-246, 2001.

via Auto-LEE: A Novel Autonomous Lower Extremity Exoskeleton for Walking Assistance – IEEE Conference Publication

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[Abstract + References] Do powered over-ground lower limb robotic exoskeletons affect outcomes in the rehabilitation of people with acquired brain injury?


Purpose: To assess the effects of lower limb robotic exoskeletons on outcomes in the rehabilitation of people with acquired brain injury.

Materials and methods: A systematic review of seven electronic databases was conducted. The primary outcome of interest was neuromuscular function. Secondary outcomes included quality of life, mood, acceptability and safety. Studies were assessed for methodological quality and recommendations were made using the GRADE system.

Results: Of 2469 identified studies, 13 (n = 322) were included in the review. Five contained data suitable for meta-analysis. When the data were pooled, there were no differences between exoskeleton and control for 6-Minute Walk Test, Timed Up and Go or 10-Meter Walk Test. Berg Balance Scale outcomes were significantly better in controls (MD = 2.74, CI = 1.12–4.36, p = 0.0009). There were no severe adverse events but drop-outs were 11.5% (n = 37). No studies reported the effect of robotic therapy on quality of life or mood. Methodological quality was on average fair (15.6/27 on Downs and Black Scale).

Conclusions: Only small numbers of people with acquired brain injury had data suitable for analysis. The available data suggests no more benefit for gait or balance with robotic therapy than conventional therapy. However, some important outcomes have not been studied and further well-conducted research is needed to determine whether such devices offer benefit over conventional therapy, in particular subgroups of those with acquired brain injury.

  • Implications for Rehabilitation
  • There is adequate evidence to recommend that powered over-ground lower limb robotic exoskeletons should not be used clinically in those with ABI, and that use should be restricted to research.
  • Further research (controlled trials) with dependent ambulators is recommended.
  • Research of other outcomes such as acceptability, spasticity, sitting posture, cardiorespiratory and psychological function, should be considered.



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[Abstract] A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation


Exoskeleton robots have been demonstrated to effectively assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeletons more accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeletons focus on assistance with coarse-motion of the upper arm while forearm fine-motion rehabilitation is often ignored. This paper presents an elbow-wrist exoskeleton with five degrees-of-freedom (DoFs). Using geared bearings, slider crank mechanisms, and a spherical mechanism for the wrist and elbow modules, this exoskeleton can provide 5-DoF rotary motion forearm assistance. The optimized exoskeleton dimensions allow sufficient rotation output while the motors are placed parallel to the forearm and elbow joint. Thus compactness and less inertia loading can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control interaction force and impedance between exoskeleton and forearm. The resulting 3-kg exoskeleton can be used alone or easily in combination with other exoskeleton robots to provide various robot-aided upper limb rehabilitation.

via A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation – IEEE Journals & Magazine

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[Abstract] Variable impedance control of finger exoskeleton for hand rehabilitation following stroke



The purpose of this paper is to propose a variable impedance control method of finger exoskeleton for hand rehabilitation using the contact forces between the finger and the exoskeleton, making the output trajectory of finger exoskeleton comply with the natural flexion-extension (NFE) trajectory accurately and adaptively.


This paper presents a variable impedance control method based on fuzzy neural network (FNN). The impedance control system sets the contact forces and joint angles collected by sensors as input. Then it uses the offline-trained FNN system to acquire the impedance parameters in real time, thus realizing tracking the NFE trajectory. K-means clustering method is applied to construct FNN, which can obtain the number of fuzzy rules automatically.


The results of simulations and experiments both show that the finger exoskeleton has an accurate output trajectory and an adaptive performance on three subjects with different physiological parameters. The variable impedance control system can drive the finger exoskeleton to comply with the NFE trajectory accurately and adaptively using the continuously changing contact forces.


The finger is regarded as a part of the control system to get the contact forces between finger and exoskeleton, and the impedance parameters can be updated in real time to make the output trajectory comply with the NFE trajectory accurately and adaptively during the rehabilitation.


via Variable impedance control of finger exoskeleton for hand rehabilitation following stroke | Emerald Insight

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[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] Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality


According to the present situation that the treatment means for apoplectic patients is lagging and weak, a set of long-distance exoskeleton rehabilitation training system with 5 DOF for upper limb was developed. First, the mechanical structure and control system of the training system were designed. Then a new kind of building method for virtual environment was proposed. The method created a complex model effectively with good portability. The new building method was used to design the virtual training scenes for patients’ rehabilitation in which the virtual human model can move following the trainer on real time, which can reflect the movement condition of arm of patient factually and increase the interest of rehabilitation training. Finally, the network communication technology was applied into the training system to realize the remote communication between the client-side of doctor and training system of patient, which makes it possible to product rehabilitation training at home.

via Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality – IEEE Conference Publication

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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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[ARTICLE] Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling – Full Text



Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.


We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.


We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.


Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.


The ability to walk directly relates to quality of life. Neurological lesions such as those underlying stroke and spinal cord injury (SCI) often result in severe motor impairments (i.e., paresis, spasticity, abnormal joint couplings) that compromise an individual’s motor capacity and health throughout the life span. For several decades, scientific effort in rehabilitation robotics has been directed towards exoskeletons that can help enhance motor capacity in neurologically impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had limited performance when tested in healthy individuals [1] and have achieved only modest clinical impact in neurologically impaired patients [2], e.g., stroke [34], SCI patients [5]. […]


Continue —>  Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

Fig. 1 Enter aSchematic representation of the real-time modeling framework and its communication with the robotic exoskeleton. The whole framework is operated by a Raspberry Pi 3 single-board computer. The framework consists of five main components: a The EMG plugin collects muscle bioelectric signals from wearable active electrodes and transfers them to the EMG-driven model. b The B-spline component computes musculotendon length (Lmt) and moment arm (MA) values from joint angles collected via robotic exoskeleton sensors. c The EMG-driven model uses input EMG, Lmt and MA data to compute the resulting mechanical forces in 12 lower-extremity musculotendon units (Table 1) and joint moment about the degrees of freedom of knee flexion-extension and ankle plantar-dorsiflexion. d The offline calibration procedure identifies internal parameters of the model that vary non-linearly across individuals. These include optimal fiber length and tendon slack length, muscle maximal isometric force, and excitation-to-activation shape factors. eThe exoskeleton plugin converts EMG-driven model-based joint moment estimates into exoskeleton control commands. Please refer to the Methods section for an in-depth description caption

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[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation


Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

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5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

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16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

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via eConHand: A Wearable Brain-Computer Interface System for Stroke 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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