Posts Tagged rehabilitation training

[Abstract + References] Wrist Motor Function Rehabilitation Training and Evaluation System Based on Human-Computer Interaction – Conference paper


Based on human-computer interaction, a wrist motor function rehabilitation training and evaluation system is developed for the treatment or improvement of wrist motor dysfunction. Specifically, the joint angle sensor and the MYO wristband are used to realize the perception of the wrist motion on the ROS, the wrist motor function rehabilitation training game with information feedback is designed, and the quantitative evaluation on the wrist motor function is realized. The experimental results demonstrate that in the rehabilitation training session, the online accuracy of wrist motion recognition is 95.2%, and in the evaluation session, the root mean square error of the measured and actual values of the wrist joint angle is less than 5°. The paper works provide the basis for further clinical experiments of the wrist motor function rehabilitation training and evaluation.


  1. 1.
    Pandian, S., Arya, K.N., Davidson, E.W.R.: Comparison of Brunnstrom movement therapy and motor relearning program in rehabilitation of post-stroke hemiparetic hand: a randomized trial. J. Bodywork Mov. Ther. 16(03), 330–337 (2012)CrossRefGoogle Scholar
  2. 2.
    Serrien, D.J., Strens, L.H., Cassidy, M.J., et al.: Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke. Exp. Neurol. 190(02), 425–432 (2004)CrossRefGoogle Scholar
  3. 3.
    Tsoupikova, D., Stoykov, N.S., Corrigan, M., et al.: Virtual immersion for post-stroke hand rehabilitation therapy. Ann. Biomed. Eng. 43(02), 467–477 (2015)CrossRefGoogle Scholar
  4. 4.
    Hasani, F.N., MacDermid, J.C., Tang, A., Kho, M.E.: Cross-cultural adaptation and psychometric testing of the Arabic version of the Patient-Rated Wrist Hand Evaluation (PRWHE-A) in Saudi Arabia. J. Hand Ther. 28(4), 412–420 (2015)CrossRefGoogle Scholar
  5. 5.
    Kennedy, S.A., Stoll, L.E., Lauder, A.S.: Human and other mammalian bite injuries of the hand: evaluation and management. J. Am. Acad. Orthop. Surg. 23(1), 47–57 (2015)CrossRefGoogle Scholar
  6. 6.
    Thielbar, K.O., Lord, T.J., Fischer, H.C., et al.: Training finger individuation with a mechatronic-virtual reality system leads to improved fine motor control post-stroke. J. Neuroengineering Rehabil. 11(01), 171 (2014)CrossRefGoogle Scholar
  7. 7.
    Rivas, J.J., Heyer, P., et al.: Towards incorporating affective computing to virtual rehabilitation; surrogating attributed attention from posture for boosting therapy adaptation. In: International Symposium on Medical Information Processing and Analysis, vol. 92(87), 58–63 (2015)Google Scholar
  8. 8.
    Heuser, A., Kourtev, H., Hentz, V., et al.: Tele-rehabilitation using the Rutgers Master II glove following Carpal Tunnel Release surgery. In: International Workshop on Virtual Rehabilitation, vol. 15(01), pp. 88–93 (2007)Google Scholar
  9. 9.
    Sucar, L.E., Orihuela, E.F., Velazquez, R.L., et al.: Gesture therapy: an upper limb virtual reality-based motor rehabilitation platform. IEEE Trans. Neural Syst. Rehabil. Eng. 22(03), 634–643 (2014)CrossRefGoogle Scholar

via Wrist Motor Function Rehabilitation Training and Evaluation System Based on Human-Computer Interaction | SpringerLink

, , , ,

Leave a comment

[Abstract + References] A Soft Robotic Glove for Hand Rehabilitation Using Pneumatic Actuators with Variable Stiffness – Conference paper


Traditional rigid robots exist many problems in rehabilitation training. Soft robotics is conducive to breaking the limitations of rigid robots. This paper presents a soft Rehabilitation training, Soft robot, Pneumatic actuator device for the rehabilitation of hands, including soft pneumatic actuators that are embedded in the device for motion assistance. The key feature of this design is the stiffness of each actuator at different positions is different, which results in the bending posture of the actuator is more accordant with the bending figure of human hand. In addition, another key point is the use of a fabric sleeves allow actuators to gain greater bending force when pressurized, which gives the hand greater bending force. We verified the feasibility of actuator through simulation, the performance of soft actuator and the device also are evaluated through experiments. Finally, the results show that this device can finish some of the hand rehabilitation tasks.


  1. 1.
    Yap, H.K., Lim, J.H., Goh, J.C.H., et al.: Design of a soft robotic glove for hand rehabilitation of stroke patients with clenched fist deformity using inflatable plastic actuators. J. Med. Devices 10(4), 044504 (2016)CrossRefGoogle Scholar
  2. 2.
    Kemna, S., Culmer, P.R., Jackson, A.E., et al.: Developing a user interface for the iPAM stroke rehabilitation system. In: IEEE International Conference on Rehabilitation Robotics, pp. 879–884. IEEE (2009)Google Scholar
  3. 3.
    Cai, Z., Tong, D., Meadmore, K.L., et al.: Design & control of a 3D stroke rehabilitation platform. In: IEEE International Conference on Rehabilitation Robotics (2011). 5975412Google Scholar
  4. 4.
    Lum, P.S., Burgar, C.G., Van der Loos, M.: MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: a follow-up study. J. Rehabil. Res. Dev. 43(5), 631 (2006)CrossRefGoogle Scholar
  5. 5.
    Pehlivan, A.U., Celik, O., O’Malley, M.K.: Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation. In: IEEE International Conference on Rehabilitation Robotics. IEEE (2011). 5975428Google Scholar
  6. 6.
    Polygerinos, P., Wang, Z., Galloway, K.C., et al.: Soft robotic glove for combined assistance and at-home rehabilitation. Robot. Auton. Syst. 73(C), 135–143 (2015)CrossRefGoogle Scholar
  7. 7.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: MRC-glove: A fMRI compatible soft robotic glove for hand rehabilitation application. In: IEEE International Conference on Rehabilitation Robotics, pp. 735–740. IEEE (2015)Google Scholar
  8. 8.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: IEEE International Conference on Robotics and Automation, pp. 4967–4972. IEEE (2015)Google Scholar
  9. 9.
    Yap, H.K., Khin, P.M., Koh, T.H., et al.: A fully fabric-based bidirectional soft robotic glove for assistance and rehabilitation of hand impaired patients. IEEE Robot. Autom. Lett. PP(99), 1 (2017)Google Scholar
  10. 10.
    Mosadegh, B., Polygerinos, P., Keplinger, C., et al.: Soft robotics: pneumatic networks for soft robotics that actuate rapidly. Adv. Funct. Mater. 24(15), 2109 (2014)CrossRefGoogle Scholar
  11. 11.
    Galloway, K.C., Polygerinos, P., Walsh, C.J., et al.: Mechanically programmable bend radius for fiber-reinforced soft actuators. In: International Conference on Advanced Robotics. IEEE (2014)Google Scholar
  12. 12.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Front. Neurosci. 11, 547 (2017)CrossRefGoogle Scholar
  13. 13.
    Yap, H.K., Ang, B.W., Lim, J.H., et al.: A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. In: IEEE International Conference on Robotics and Automation, pp. 3537–3542. IEEE (2016)Google Scholar
  14. 14.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Characterisation and evaluation of soft elastomeric actuators for hand assistive and rehabilitation applications. J. Med. Eng. Technol. 40, 1–11 (2016)CrossRefGoogle Scholar
  15. 15.
    Tong, M.: Design, Modeling and Fabrication of a Massage Neck Support Using Soft Robot Mechanis. The Ohio State University (2014)Google Scholar
  16. 16.
    Aubin, P.M., Sallum, H., Walsh, C., et al.: A pediatric robotic thumb exoskeleton for at-home rehabilitation: the Isolated Orthosis for Thumb Actuation (IOTA). In: Proceedings of IEEE International Conference on Rehabilitation Robotics, pp. 1–6 (2013)Google Scholar

via A Soft Robotic Glove for Hand Rehabilitation Using Pneumatic Actuators with Variable Stiffness | SpringerLink

, , , , , , ,

Leave a comment

[ARTICLE] Development of a Novel Home Based Multiscene Upper Limb Rehabilitation Training and Evaluation System for Post-stroke Patients – Full Text PDF


Upper limb rehabilitation requires long-term, repetitive rehabilitation training and assessment, and many patients cannot pay for expensive medical fees in the hospital for so long time. It is necessary to design an effective, low cost, and reasonable home rehabilitation and evaluation system. In this paper, we developed a novel home based multi-scene upper limb rehabilitation training and evaluation system (HomeRehabMaster) for post-stroke patients. Based on the Kinect sensor and posture sensor, multi-sensors fusion method was used to track the motion of the patients. Multiple virtual scenes were designed to encourage rehabilitation training of upper limbs and trunk. A rehabilitation evaluation method integrating Fugl-meyer assessment (FMA) scale and upper limb reachable workspace relative surface area (RSA) was
proposed, and a FMA-RSA assessment model was established to assess upper limb motor function.
Correlation based dynamic time warping (CBDTW) was used to solve the problem of inconsistent upper limb movement path in different patients. Two clinical trials were conducted. The experimental results show that the system is very friendly to the subjects, the rehabilitation assessment results by this system are highly correlated with the therapist’s (the highest forecast accuracy was 92.7% in the 13th item), and longterm rehabilitation training can improve the upper limb motor function of the patients statistically significant (p=0.02<0.05). The system has the potential to become an effective home rehabilitation training and evaluation system.[…]
Full Text PDF —>  IEEE Xplore Full-Text PDF:

, , , , , , , , , , ,

Leave a comment

[ARTICLE] Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training – Full Text


Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

1. Introduction

Over the past decade, the increasing stroke patient population has brought great economic and medical pressures to the whole society. Surviving stroke patients usually have a lower quality of life dues to physical disability and cognitive impairment. Studies on clinical stroke treatment indicate that appropriate rehabilitation training has positive therapeutic effects for avoiding muscle atrophy and recovering musculoskeletal motor functions. However, the conventional one-on-one manual-assisted movement training conducted by physiotherapists suffers from many inherent limitations, such as high labor intensity, high cost, long time consumption, lack of repeatability, low participation levels of patient, and high dependence on personnel with specialized skills [1,2]. In recent years, robot-assisted rehabilitation therapies have gained growing interest from academic researchers and the healthcare industry around the world due to their unique advantages and promising application perspectives. Compared with the traditional manual rehabilitation treatment, the combination of robotic technologies and clinical experience can significantly improve the performance and quality of training. Robot-assisted therapy is capable of delivering high-intensity, long-endurance, goal-directed, and low-cost rehabilitation treatment. Moreover, the functional motivations of patient can be activated to enhance active participation and recover cognitive functions. The physical parameters and therapy data can be recorded and analyzed via sensing system, and that can provide objective basis to optimize training strategy and accelerate recovery process [3,4].
Many therapeutic robot system have been developed to assist stroke patients with motor dysfunctions perform the desired rehabilitation training. The existing rehabilitation robotic devices can be categorized into two types, i.e., end-effector-based robots and exoskeleton-based robots. End-effector-based robot has only a connection between its distal end and the impaired extremity of patient. However, the movement of end-effector cannot uniquely identify the configuration of human limb due to the kinematic redundancy. Miller et al. developed a lightweight and potable end-effector-based therapeutic robot, which is integrated with a wrist and finger force sensor module named WFES, for the upper limb rehabilitation training of hemiplegic stroke patients [5]. Pedro et al. developed a parallel kinematic mechanism (PKM) with two translational and two rotational degrees of freedom (DOFs) for knee diagnosis and rehabilitation tasks [6]. Kang et al. proposed a modular and reconfigurable wrist robot called CR2-Haptic for post-stroke subjects to train forearm and wrist movements [7]. Besides, many other end-effector-based therapeutic robot have been investigated and can be referred to [8,9,10,11,12,13]. Comparatively, the exoskeleton-based rehabilitation robots are developed with more complex structures imitating the anatomical human skeleton and guaranteeing the alignment between the joints axis of robot and impaired limb. ChARMin is a powered exoskeleton integrated with audiovisual game-like interface. It can provide intensive pediatric arm rehabilitation training for the children and adolescents with affected motor functions [14]. Simon et al. proposed a spherical shoulder exoskeleton with a double parallelogram linkage to eliminate singularities and achieve good manipulability properties [15]. Crea et al. developed a semi-autonomous whole-arm exoskeleton for the stroke patients performing activities of daily living (ADL) utilizing hybrid electroencephalography and electrooculography feedback signals [16]. Many other representative exoskeleton-based therapeutic robot have also been designed, such as CAREX-7 [17], RUPERT [18], ULEL [19], ArmeoPower [20], Indego [21], and ETS-MARSE [22].
The effectiveness of robot-assisted rehabilitation training depends on the control strategies applied in the therapeutic robot system. Currently, many kinds of control strategies have been developed according to the requirements of patients with various impairment severities in different therapy periods. The existing control schemes can be basically divided into two categories based on the interaction between therapeutic robots and patients, i.e., patient-passive training control and patient-cooperative training control. During the acute period of hemiplegia, the impaired extremity is fully paralyzed without any muscle contraction. The patient-passive training can imitate the manual therapeutic actions of a physiotherapist. It is especially well suited for the patients with severe paralysis to passively execute repetitive reaching missions along predefined training trajectories. However, it is a challenge to guarantee the position control accuracy during rehabilitation training due to the highly nonlinear properties and unexpected uncertainties of human-robot interaction. Different kinds of control algorithms have been developed to improve control performance of patient-passive training, including neural proportional-integral-derivative (PID) control [23], neural proportional-integral (PI) control [24], adaptive nonsingular terminal sliding mode control (SMC) [25], disturbance observer-based fuzzy control [26], neural-fuzzy adaptive control [27], adaptive backlash compensation control [28], and so on. Comparatively, the patient-cooperation training is applicable for the patients at the comparative recovery period, who have regained parts of motor functions. Clinical studies show that integrating the voluntary efforts of patients into rehabilitation training benefits to accelerate recovery progress and promote psychological confidence. Thus, patient-cooperation training should be able to regulate the human-robot interaction in accordance with the motion intentions and hemiplegia degrees of patients. Many patient-cooperation control strategies have been proposed, such as minimal assist-as-needed controller [29], myoelectric pattern recognition controller [30], electromyography (EMG)-based model predictive controllers [31], subject-adaptive controller [32], and fuzzy adaptive admittance controller [33].
Taking the above into consideration, the contribution of this paper is to develop an upper limb exoskeleton to assist the patient with motor disabilities perform multi-modal rehabilitation training. Firstly, the overall mechanical structure and the MATLAB/xPC-based real-time control system of the proposed therapeutic robot are introduced. Secondly, the dynamic modeling of the human-robot system is researched, and the dynamics parameters are obtained via virtual prototype and calibration experiments. After that, a multi-modal control strategy integrated with an adaptive sliding mode controller and a cascade-proportional-integral-derivative (CPID)-based impedance controller is proposed. The controller is combined with an audiovisual therapy interface and is able to realize patient-passive and patient-cooperation training based on the motor ability of patient. Finally, the effectiveness and feasibility of the developed rehabilitation exoskeleton system and control scheme are verified through three experiments conducted by several volunteers.[…]

Continue —> Sensors | Free Full-Text | Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training | HTML

Sensors 18 03611 g001

Figure 1. Architecture and major components of the upper extremity rehabilitation exoskeleton. (a) Virtual prototype model. (b) Real-life picture of exoskeleton.

, , , , , , , , , ,

Leave a comment

[ARTICLE] Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training – Full Text

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.


Stroke is a severe neurological disease caused by the blockages or rupture of cerebral blood vessels, leading to significant physical disability and cognitive impairment (12). The recent statistics from the World Health Organization indicate that worldwide 15 million people annually suffer from the effect of stroke, and more than 5 million stroke patients survive and, however, require a prolonged physical therapy to recover motor function. Recent trends predict increased stroke incidence at younger ages in the upcoming years (34). Approximately four-fifths of all survived stroke patients suffer from the problems of hemiparesis or hemiplegia and, as a result, have difficulties in performing activities of daily living (ADL). Stroke causes tremendous mental and economic pressure on the patients and their families (5). Medical research has proved that, owing to the neural plasticity of the human brain, appropriate rehabilitation trainings are beneficial for stroke survivors to recover musculoskeletal motor abilities. Repetitive and task-oriented functional activities have substantial positive effects on improving motor coordination and avoiding muscle atrophy (67). Traditional stroke rehabilitation therapy involves many medical disciplines, such as orthopedics, physical medicine, and neurophysiology (89). Physiotherapists and medical personnel are required to provide for months one-on-one interactions to patients that are labor intensive, time consuming, patient-passive, and costly. Besides, the effectiveness of traditional therapeutic trainings is limited by the personal experiences and skills of therapists (1011).

In recent decades, robot-assisted rehabilitation therapies have attracted increasing attention because of their unique advantages and promising applications (1213). Compared with the traditional manual repetitive therapy, the use of robotic technologies helps improve the performance and efficiency of therapeutic training (14). Robot-assisted therapy can deliver high-intensive, long-endurance, and goal-directed rehabilitation treatments and reduce expense. Besides, the physical parameters and the training performance of patients can be monitored and evaluated via built-in sensing systems that facilitate the improvement of the rehabilitation strategy (1516). Many therapeutic robots have been developed to improve the motor functions of the upper extremity of disabled stroke patients exhibiting permanent sensorimotor arm impairments (17). The existing robots used for upper limb training can be basically classified into two types: end-point robots and exoskeleton robots. End-point robots work by applying external forces to the distal end of impaired limbs, and some examples are MIME (18), HipBot (19), GENTLE/s (20), and TA-WREX (21). Comparatively, exoskeleton robots have complex structures similar to anatomy of the human skeleton; some examples of such robots are NMES (22), HES (23), NEUROExos (24), CAREX-7 (25), IntelliArm (26), BONES (27), and RUPERT (28). The joints of the exoskeleton need to be aligned with the human anatomical joints for effective transfer of interactive forces.

The control strategies applied in therapeutic robots are important to ensure the effectiveness of rehabilitation training. So far, according to the training requirement of patients with different impairment severities, many control schemes have been developed to perform therapy and accelerate recovery. Early rehabilitation robot systems implemented patient-passive control algorithms to imitate the manual therapeutic actions of therapists. These training schemes are suitable for patients with severe paralysis to passively execute repetitive reaching tasks along predefined trajectories. Primary clinical results indicate that patient-passive training contributes to motivating muscle contraction and preventing deterioration of arm functions. The control of the human–robot interaction system is a great challenge due to its highly nonlinear characteristics. Many control algorithms have been proposed to enhance the tracking accuracy of passive training, such as the robust adaptive neural controller (29), fuzzy adaptive backstepping controller (30), neural proportional–integral–derivative (PID) controller (31), fuzzy sliding mode controller (32), and neuron PI controller (33).

The major disadvantage of patient-passive training is that the active participation of patients is neglected during therapeutic treatment (34). Several studies suggest that, for the patients who have regained parts of motor functions, the rehabilitation treatment integrated with the voluntary efforts of patients facilitates the recovery of lost motor ability (35). The patient-active control, normally referred as patient-cooperative control and assist-as-needed control, is capable of regulating the human–robot interaction depending on the motion intention and the disability level of patients. Keller et al. proposed an exoskeleton for pediatric arm rehabilitation. A multimodal patient-cooperative control strategy was developed to assist upper limb movements with an audiovisual game-like interface (36). Duschauwicke et al. proposed an impedance-based control approach for patient-cooperative robot-aided gait rehabilitation. The affected limb was constrained with a virtual tunnel around the desired spatial path (37). Ye et al. proposed an adaptive electromyography (EMG) signals-based control strategy for an exoskeleton to provide efficient motion guidance and training assistance (38). Oldewurtel et al. developed a hybrid admittance–impedance controller to maximize the contribution of patients during rehabilitation training (39). Banala et al. developed a force-field assist-as-need controller for intensive gait rehabilitation training (40). However, there are two limitations in the existing patient-cooperative control strategies. Firstly, the rehabilitation training process is not completely patient-active, as the patient needs to perform training tasks along a certain predefined trajectory. Secondly, existing control strategies are executed in self-designed virtual scenarios that are generally too simple, rough, and uninteresting. Besides, applying a certain control strategy to different virtual reality scenarios is difficult.

Taking the above issues into consideration, the main contribution of this paper is to develop a control strategy for an upper limb exoskeleton to assist disabled patients in performing active rehabilitation training in a virtual scenario based on their own active motion intentions. Firstly, the overall structure design and the real-time control system of the exoskeleton system are briefly introduced. A dynamic model of the human–robot interaction system is then established using the Lagrangian approach. After that, an admittance-based patient-active controller combined with an audiovisual therapy interface is proposed to induce the active participation of patients during training. Existing commercial virtual games without a specific predetermined training trajectory can be integrated into the controller via a virtual keyboard unit. Finally, three types of experiments, namely the position tracking experiment without interaction force, the free arm movement experiment, and the virtual airplane-game operation experiment, are conducted with healthy and disabled subjects. The experimental results demonstrate the feasibility of the proposed exoskeleton and control strategy.

Exoskeleton Robot Design

The architecture of the proposed exoskeleton is shown in Figure 1. This wearable force-feedback exoskeleton robot has seven actuated degrees of freedom (DOFs) and two passive DOFs covering the natural range of movement (ROM) of humans in ADL. The robot has been designed with an open-chain structure to mimic the anatomy of the human right arm and provide controllable assistance torque to each robot joint. There are three actuated DOFs at the shoulder for internal/external rotation, abduction/adduction, and flexion/extension; two DOFs at the elbow for flexion/extension and pronation/supination; and two DOFs at the wrist for flexion/extension and ulnal/radial deviation. Besides, since the center of rotation of the glenohumeral joint varies with the shoulder girdle movement, the robot is mounted on a self-aligning platform with two passive translational DOFs to compensate the human–robot misalignment and to guarantee interaction comfort. […]

Figure 1. Architecture of upper limb rehabilitation exoskeleton (1-Self-aligning platform; 2-AC servo motor; 3-Bowden cable components; 4-Support frame; 5-Wheelchair; 6-Elbow flexion/extension; 7-Proximal force/torque sensor; 8-Wrist flexion/extension; 9-Wrist ulnal/radial deviation; 10-Distal force/torque sensor; 11-Forearm pronation/supination; 12-Auxiliary links; 13-Shoulder flexion/extension; 14-Shoulder abduction/adduction; 15-Shoulder internal/external; 16-Free-length spring).


Continue —>  Frontiers | Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training | Neurology

, , , , , , , , ,

Leave a comment

[Abstract] Recent Patents on Wrist Rehabilitation Equipment


Background: Wrist activity is very frequent in our daily routine, and is under heavy load during the movement such as supporting and push-pull. So wrist can get damaged very easily in daily life. Based on the rehabilitation medicine and ergonomics, the particularity and complexity of human limbs should be considered in the design process. During the treatment process, the wrist joint rehabilitation equipment can provide stable, accurate, safe and comfortable repeated rehabilitation training for patients. The use of rehabilitation training equipment can greatly reduce the cost of treatment and improve the rehabilitation efficiency.

Objective: The related patents of rehabilitation training equipment for wrist joint will be reviewed, and the structure and working principle of these equipments will be illustrated. The results of the analysis provide some meaningful reference for the optimal design of the wrist joint.

Methods: Based on the comparative analysis of the latest patents related to wrist rehabilitation equipment, the key problems and future development of the rehabilitation equipment are put forward.

Results: The patents of the rehabilitation training equipment for the wrist are classified in the paper. Studies show that remarkable improvements have been achieved in the invention of the wrist rehabilitation equipment.

Conclusion: In the future, the mechanical design, control system and rehabilitation strategy of wrist rehabilitation equipment should be further studied.


via Recent Patents on Wrist Rehabilitation Equipment: Ingenta Connect

, , , , , ,

Leave a comment

[ARTICLE] Active rehabilitation training system for upper limb based on virtual reality – Full Text


In this article, an active rehabilitation training system based on the virtual reality technology is designed for patients with the upper-limb hemiparesis. The six-axis inertial measurement unit sensors are used to acquire the range of motion of both shoulder and elbow joints. In order to enhance the effect of rehabilitation training, several virtual rehabilitation training games based on the Unity3D engine are designed to complete different tasks from simple level to complicated level. The purpose is to increase the patients’ interest during the rehabilitation training. The basic functions of the virtual rehabilitation task scenes are tested and verified through the single-joint training and the multi-joint compounding training experiments. The experimental results show that the ranges of motion of both shoulder and elbow joints can reach the required ranges of a normal human in the rehabilitation training games. Therefore, the system which is easy to wear, low cost, wireless communication, real-time data acquisition, and interesting virtual rehabilitation task games can provide an effective rehabilitation training process for the upper-limb hemiparesis at home.

The upper limb has many degrees of freedom, and it is also a complex part of the human body by which people can accomplish fine movements during their activities in daily life. With the intensification of the aging problem in the world, the amount of stroke hemiparesis has shown a growing trend, especially in China, which has an enormous population.1 Approximately 30%–50% of these stroke survivors will suffer from chronic hemiparesis, especially involving their hands. In addition, spinal cord injury (SCI) and traffic accident survivors may also find limb movements’ disorder. Injury within the cervical region of the cord leads to tetraplegia, which leads to impairment of all four limbs. An estimated result shows that 55% of new cases will result in tetraplegia, while the other 45% will experience paraplegia due to injury below the cervical level.2Limb hemiparesis which is caused by stroke, SCI, or traffic accidents not only gives the patient’s daily life a great deal of inconvenience and even more makes the patient suffer from great mental pain but also brings a heavy stress and medical burden for the patient’s family and society. Technology has been developed in an effort to facilitate rehabilitation for the patient. Upper-limb rehabilitation is one of the fastest growing areas in modern neurorehabilitation. Quality of life can be improved with efficient therapy.3 At present, rehabilitation therapy of upper limb with traditional rehabilitation therapy is commonly used, that is, rehabilitation therapists perform rehabilitation trainings on individuals. Now with the development of robot technology, the rehabilitation of robot-assisted training is also rising up. The MIT-Manus4 is an example of end-effector-based and arm-rehabilitation robotic device, while the ARMin device5 is an example of arm-rehabilitation exoskeletons which also allows pronation/supination of the lower arm and wrist flexion/extension. It could be operated in three modes: passive mobilization, active game-supported arm therapy, and active training of activities of daily living (ADLs). The end-effector-based robots have practical advantages (usability, simplicity, and cost-effectiveness), and exoskeleton robots have biomechanical advantages (better guidance). Currently, the automatic rehabilitation devices on market as mentioned above are mostly complex and expensive, which are often used in the hospitals and clinics are not affordable to ordinary patients. Therefore, one of the research objectives aims to develop the upper-limb rehabilitation training system with minimal structure and low cost and can be used in patient’s home. But in China, it can be seen that patients with upper-limb orthosis in home is only for fixing the arm and just move autonomously according to the setting angle. The researches on intelligent domestic rehabilitation device just begins, most of which are in the experimental stage and not yet market oriented.6,7

Another problem is that the patients are treated with low initiative and dull training process which does not motivate them, while the treatment effect is not obvious.8,9 Computer games based on virtual reality (VR) are a good way to mobilize the patients’ initiative in the training, so the rehabilitation effect on a particular movement task will be greatly improved.10 VR environments provide an excellent method to manipulate task conditions in training. The effects and the intensity of training can be enhanced and designed more challenging, since the implementation of VR can build a channel both visual and haptic communication can be involved in. The research on VR system which is applied to rehabilitation training was initiated a few years ago. Mazzone et al.11 made a study on the effect of rehabilitation training for patients with shoulder joints training using VR technology. This study aimed to determine whether performance of shoulder exercises in virtual reality gaming (VRG) results in similar muscle activation as non-VRG exercise. The conclusion was drawn that exercise with VRG should be effective to reduce shoulder pain caused by spinal injury. Fischer et al.12 conducted a preliminary study claim that stroke patients could assist themselves in training their hands in the virtual environment. The purpose of this pilot study was to investigate the impact of assisted motor training in a virtual environment on hand function for the stroke survivors. Participants had 6 weeks of training in reach-to-grasp of both virtual and actual objects. After the training period, participants in all three groups demonstrated a decrease in time to perform some of the functional tasks. These designs based on VR have achieved some success and then the second research objective is to add the VR technology to the intelligent domestic rehabilitation device. These studies are mainly designed for the single joint of the upper-limb rehabilitation training. Therefore, it is necessary to carry out the research on multi-joint combined training device for patients who can just stay home by training with VR tasks of adjustable game levels.

Another important element which needs to be considered as an ultimate success using at home is its ease of use. Therefore, simple active rehabilitation device should be developed. The setup time of such device should be fast, besides measurement, treatment approaches, and incorporating gaming, and should provide intuitive interfaces that can be directly utilized by the individuals. This study will introduce an active rehabilitation training system for upper limb based on VR technology, which has some advantages such as simple structure, easy to manipulate, and portable for household. It also mobilizes patients’ initiative with adjustable difficulty level of VR tasks so that the individuals’ rehabilitation effect of the upper limb is obviously improved.

The active rehabilitation training system for upper limb based on VR is designed for the pronation/supination and flexion movement trainings of the elbow joint and the extension/flexion and abduction exercises of the shoulder joint. By adding the games in training processes, the patients may actively participate in rehabilitation trainings, while the efficiency will be greatly improved. The portable and easy-to-use design of this system can effectively reduce the problem of the medical resources shortage in the rehabilitation field.

Overall scheme of the system

The system is composed of two parts: the upper-limb posture detection system and the virtual rehabilitation training task scene, as shown in Figure 1.


Figure 1. Schematic diagram of an active rehabilitation training system for upper limb based on VR.


Continue —> Active rehabilitation training system for upper limb based on virtual realityAdvances in Mechanical Engineering – Jianhai Han, Shujun Lian, Bingjing Guo, Xiangpan Li, Aimin You, 2017

, , , , , , , ,

Leave a comment

[Abstract+References] Wearable Rehabilitation Training System for Upper Limbs Based on Virtual Reality – Conference paper


In this paper, wearable rehabilitation training system for the upper limb based on virtual reality is designed for patients with upper extremity hemiparesis. The six-axis IMU sensor is used to collect the joint training angles of the shoulder and elbow. In view of the patient’s shoulder and elbow joint active rehabilitation training, the virtual rehabilitation training games based on the Unity3D engine are designed to complete different tasks. Its purpose is to increase the interest of rehabilitation training. The data obtained from the experiment showed that the movement ranges of the shoulder and elbow joint reached the required ranges in the rehabilitation training game. The basic function of the system is verified by the experiments, which can provide effective rehabilitation training for patients with upper extremity hemiparesis.




Liang, M., Dou, Z.L., Wang, Q.H.: Application of virtual reality technique in rehabilitation of hemiplegic upper extremities function of stroke patients. Chin. J. Rehabil. Med. 02, 114–118 (2013)Google Scholar


Valencia, N., Cardoso, V., Frizera, A.: Serious Game for Post-stroke Upper Limb Rehabilitation. Converging Clinical and Engineering Research on Neurorehabilitation II. Springer, Berlin (2017)Google Scholar


Lei, Y., Yu, H.L., Wang, L.L., Wang, Z.P.: Research on virtual reality-based interactive upper-limb rehabilitation training system. Prog. Biomed. Eng. 36(1), 21–24 (2015)Google Scholar


Xu, B.G., Peng, S., Song, A.G.: Upper-limb rehabilitation robot based on motor imagery EEG. Robot 33(3), 307–313 (2011)CrossRefGoogle Scholar


Wang, H.T.: Status of Application of Virtual Reality Technique in Motor Rehabilitation in Stroke. Chin. J. Rehabil. Theory Pract. 10, 911–915 (2014)Google Scholar


Zhang, J.L.: Research of Finger Rehabilitation System Based on Virtual Reality Technology. Huazhong University of Science and Technology (2012)Google Scholar


Mei, Z., He, L.W., Wu, L., Jian, Z.: Design and test of a portable exoskeleton elbow rehabilitation training device. Chin. J. Rehabil. Med. 11, 1155–1157 (2015)Google Scholar


Mazzone, B., Haubert, L.L., Mulroy, S.: Intensity of shoulder muscle activation during resistive exercises performed with and without virtual reality games. In: International Conference on Virtual Rehabilitation, pp. 127–133. IEEE (2013)Google Scholar


Fischer, H.C., Stubblefield, K., Kline, T.: Hand rehabilitation following stroke: a pilot study of assisted finger extension training in a virtual environment. Topics Stroke Rehabil. 14(1), 1–12 (2014)CrossRefGoogle Scholar


Kapandji, A.I.: The Physiology of the Joints, 6th edn. People’s Military Medical Press, Beijing (2011)Google Scholar


Gu, Y., Tian, L.H., Chen, H.: Application of virtual reality training system and rehabilitation therapy in the treatment in hemiplegic patients with upper limb dysfunction. Chin. J. Rehabil. Med. 26(6), 579–581 (2011)Google Scholar

Source: Wearable Rehabilitation Training System for Upper Limbs Based on Virtual Reality | SpringerLink

, , , , , , , , ,

Leave a comment

[Abstract+References] A Review of Upper and Lower Limb Rehabilitation Training Robot – Conference paper


With the aging of society, the number of patients with limb disorders caused by stroke has increased year by year, it is necessary to introduce more advanced technology into the field of rehabilitation treatment. Rehabilitation training based on the brain plasticity has been proved by clinical medical practice as an effective treatment method, and because of the serious lack of professional rehabilitation therapists, a large number of rehabilitation training robot have been designed so far. This article analyzed and described the research status on upper and lower limbs rehabilitation training robot, and at last the paper forecasts the future development trend of rehabilitation robot.

Source: A Review of Upper and Lower Limb Rehabilitation Training Robot | SpringerLink

, , , , , , , ,

Leave a comment

[ARTICLE] Rehabilitation System for Stroke Patients using Mixed-Reality and Immersive User Interfaces – Full Text PDF


The work presented in this paper addresses stroke, a disease costing the healthcare in Europe and USA over 3% of their entire healthcare expenditure, including inpatient treatments, outpatient hospital visits and long-term rehabilitation and care.

The StrokeBack project is a response to those needs offering an effective long term care and rehabilitation strategy for stroke patients, which would actively involve patients in the rehabilitation process while minimizing costly human support. The game based training system has been proposed allowing physicians to supervise the rehabilitation of patents at home. The proposed approach empowers patients and their caretakers to execute effectively rehabilitation protocols in their home settings, while leading physicians are able to monitor the rehabilitation progress remotely via Personal Health Record (PHR) system.

The increased rehabilitation speed and ability to perform training at home directly improves quality of life of patients.

more –> Full Text PDF

, , , , , , , , , , , ,

Leave a comment

%d bloggers like this: