Posts Tagged robots
[Abstract] A Review: Hand Exoskeleton Systems, Clinical Rehabilitation Practices, and Future Prospects
Spinal cord injury (SCI) and stroke are pathologies that often result in the loss of/decrease in hand functionality. Hand function is a critical component of everyday life and therefore, a primary focus of clinical SCI/stroke rehabilitation is hand function recovery/improvement. In recent years, there has been a surge in hand exoskeleton research due to the potential for exoskeletons to improve clinical rehabilitation efficiency through automation. However, there is a disconnect between current clinical practice and exoskeleton research, resulting in a minority of hand exoskeletons being tested on individuals with SCI and/or stroke. This review article provides a comprehensive analysis and review of hand exoskeleton studies based on clinical rehabilitation practices to bridge the knowledge gap between clinical application and laboratory research. The key findings from this paper are: 1) current hand exoskeletons can successfully complete simple ADL tasks but lack the precision for fine motor control, 2) most hand exoskeletons exhibit a low number of degrees-of-freedom compared to the human hand, which may limit movement capability, 3) the majority of hand exoskeletons lack sensing capabilities, restricting viable control methods and user interfaces, and 4) inconsistent evaluation methods across studies do not allow for accurate performance assessment for different exoskeletons. The comparative assessments performed by this survey article show that there remain deficits between clinical hand rehabilitation practices and the current state of hand exoskeletons. By delineating these shortcomings, the information presented in this work can help inform future developments in the field of assistive and rehabilitative hand exoskeletons such that the gap between research and application may be closed.
Published in: IEEE Transactions on Medical Robotics and Bionics ( Early Access )
Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.
[ARTICLE] An innovative equivalent kinematic model of the human upper limb to improve the trajectory planning of exoskeleton rehabilitation robots – Full Text
Upper limb exoskeleton rehabilitation robots have been attracting significant attention by researchers due to their adaptive training, highly repetitive motion, and ability to enhance the self-care capabilities of patients with disabilities. It is a key problem that the existing upper limb exoskeletons cannot stay in line with the corresponding human arm during exercise. The aim is to evaluate whether the existing upper limb exoskeleton movement is in line with the human movement and to provide a design basis for the future exoskeleton. This paper proposes a new equivalent kinematic model for human upper limb, including the shoulder joint, elbow joint, and wrist joint, according to the human anatomical structure and sports biomechanical characteristics. And this paper analyzes the motion space according to the normal range of motion of joints for building the workspace of the proposed model. Then, the trajectory planning for an upper limb exoskeleton is evaluated and improved based on the proposed model. The evaluation results show that there were obvious differences between the exoskeleton prototype and human arm. The deviation between the human body and the exoskeleton of the improved trajectory is decreased to 41.64 %. In conclusion, the new equivalent kinematics model for the human upper limb proposed in this paper can effectively evaluate the existing upper limb exoskeleton and provide suggestions for structural improvements in line with human motion.
Upper limb exoskeleton rehabilitation robots have become more popular because they can not only provide adaptive training and highly repetitive motion but also enhance the self-care capabilities of patients with a loss of motor function (Jarrasse et al., 2010; Côté-Allard et al., 2018; Zhang et al., 2018). The design of upper limb exoskeletons should be especially considered because they interact directly with the human body (Esmaeili et al., 2011; Gopura et al., 2011). The number of degrees of freedom (DOF), range of motion (ROM) of joints, safety, comfort, low inertia, and adaptability to the human body should be especially considered in the design of these exoskeletons (Meng et al., 2018; Maciejasz et al., 2014). In particular, it is necessary that the movement of the exoskeleton should stay in line with the human arm. Misalignment may cause many problems, such as the external force between the exoskeleton and the arm, the inaccurate control output caused by the error of the measurement position, and decreased safety (Lo and Xie, 2012; Schiele and Helm, 2006; Rocon et al., 2007).
The design of the upper limb exoskeleton is generally based on the movement of the human arm. The number of DOF is defined according to the shoulder joint, elbow joint, and wrist joint as usual, which are 3 DOF for the shoulder joint (flexion/extension, abduction/adduction, and internal/external rotation), 2 DOF for the elbow joint (flexion/extension and pronation/supination), and 2 DOF for the wrist joint (radial/ulnar deviation and flexion/extension). Other existing models of exoskeletons for the human arm include, for instance, ETS-MARSE (Rahman et al., 2015), CADEN-7 (Perry et al., 2007; Perry and Rosen, 2006), and SUEFUL-7 (Gopura et al., 2009). In addition, there are also some researchers that decreased the number of DOF at the elbow joint and wrist joint, such as RETRAINER (Ambrosini et al., 2017), HAMEXO-I (Huang et al., 2014), and some other exoskeletons (Mahdavian et al., 2015; Wong and Mir-Nasiri, 2012; Wu et al., 2014), in order to simplify the design. The DOF at the shoulder joint are retained to ensure the moveability in these designs. However, these articulated exoskeletons still cannot stay in line with human movement. The main reason, as shown in Fig. 1, is that the shoulder abduction of 180∘ is added to the 60∘ scapulothoracic (SH) joint upward rotation and the 120∘ glenohumeral (GH) joint abduction. In addition, the 60∘ SH upward rotation is depicted as being the summation of the 25∘ of sternoclavicular (SC) joint elevation and the 35∘ of acromioclavicular (AC) joint upward rotation. The red arrow in Fig. 1 indicates the change in the axis of the GH joint when the shoulder joint is abducted from 0 to 180∘. It is the change in the rotation center of the shoulder complex during the movement that causes the misalignment between the exoskeleton and human arm (Neumann, 2013). Therefore, the shoulder joint is a compound joint, and its movement should consider the roles of the clavicle and the scapula in addition to the humerus.
There are a large number of researchers who have proposed the equivalent kinematic model of the upper limb to find the kinematic characteristics of human arm. Bertomeu-Motos et al. (2018) and Fang et al. (2019) simplified the human arm into a 7 DOF model, connected through two links, namely the upper arm and forearm. However, the model did not consider the contribution of the AC, SH, and SC joints. Eduardo et al. (2018) proposed a biomimetic kinematics model for upper extremity exoskeletons to simulate the contribution of the clavicle movement to the shoulder complex in the coronal plane. The proposed exoskeleton design based on the upper limb kinematic model shows a 17.1 % increase in the motion workspace on the coronal plane with the clavicle compared to non-clavicle designs. The kinematic characteristics in the sagittal plane and horizontal plane were not analyzed. Klopcar and Lenarcic (2006) researched kinematic shoulder complex characteristics on healthy subjects and proposed a model composed of an inner and outer shoulder joint. The inner shoulder joint has two rotations, with the center in the origin of the reference coordinate, and the outer has three rotations, with axes intersecting in the center of the GH joint. The advantage of the model is the inclusion of the shoulder girdle kinematics obtained as functions of the humeral elevation angle. Klopcar and Lenarcic (2005) reported an improved kinematic model of the human arm including the shoulder complex and elbow complex. The kinematic model is appropriate for computing and visualizing the human arm’s reachable workspace. However, the kinematic model did not contain the wrist joint and simplified AC, SH, and SC joints into an universal joint and one slider, which ignored the motion characteristics of human arm too much, such as the scapula extension/retraction (Neumann, 2013). The kinematic model for workspace determination also did not contain the internal/external rotation of the elbow joint. In addition, Laitenberger et al. (2014) refined the upper limb model by means of a forearm closed-loop kinematic chain and personalized joint parameters to quantify kinematics and dynamics of the forearm joint. The wrist joint was simplified into a universal joint (Duprey et al., 2016). In order to understand the kinematic characteristics of the upper limb, the equivalent kinematic model should include not only the GH, elbow, and wrist joints but also the AC, SH, and SC joints.
The challenges of exoskeleton design include motion control and posture determination of its multiple DOF robotic components. In addition, the highly complex mechanical and redundant structures of human joints represent current research objects (Eduardo et al., 2018). The upper limb equivalent kinematic model is a common ground in these two fields, which can provide a reference for the structural design, posture determination, and motion trajectory of the upper limb exoskeleton. This key application promotes the further development of the upper extremity exoskeleton to provide more effective rehabilitation training and motion assistance for stroke patients. Various equivalent kinematic models of the upper limb have been proposed. However, most of the models simplify the DOF of human upper limbs and cannot fully describe the movement of upper limbs, and there are few studies on the application of these models in exoskeleton design. Therefore, the goal of this paper is to propose a new equivalent kinematic model for the human upper limb that will describe the movement characteristics of the human upper limbs as fully as possible and explain how the model can be used to evaluate and improve the design of upper limb exoskeleton rehabilitation robots.
The remainder of this paper is organized as follows. We present, in the next two sections, the proposed method for the equivalent kinematic model of human arm, kinematic analysis, motion space, and evaluation method. In Sect. 4, the results are presented. The discussion is conducted in Sect. 5, and we summarize and conclude this paper in Sect. 6.[…]
[Abstract] Robotic Assisted Passive Wrist and Forearm Rehabilitation: Design of an Exoskeleton and Implementation
An exoskeleton for human wrist and forearm rehabilitation has been designed and manufactured. Considering the torque values required for daily life activities, a structural analysis study has been presented. It has three degrees of freedom (DOF) which must be fitted to real human wrist and forearm. Anatomical motion ranges of human limbs have been taken into account during design. IMU has been used in order to get the kinematic values of the limbs and to evaluate the performance level of the therapy. Adapting a six DOF Denso robot to rehabilitation has been completed and experiments have been performed.
[Abstract + References] Automated Voluntary Finger Lifting Rehabilitation Support Device for Hemiplegic Patients to Use at Home
We have been proposing a robotic finger rehabilitation support device for hemiplegic patients that can be used at home. This device instructs a patient to lift a finger voluntarily and provides assistance when the patient is impossible to lift. In previous studies, we have shown an automated evaluation method which monitors the level of involuntary finger movement. However, the detailed procedure of finger rehabilitation has not been clarified. In this paper, we show a practical procedure of finger rehabilitation as well as a hardware design of the device. We also discuss safety issues during finger lift assistance. Our design limits the speed of finger lift so that it avoids unwanted contraction of finger muscles by stretch reflex. Also, the angle of finger lift is limited in our design so that it will not exceed the maximum excursion of an MP joint.
1.J. D. Schaechter, “Motor rehabilitation and brain plasticity after hemiparetic stroke”, Progress in Neurobiology, vol. 73, no. 1, pp. 61-72, May 2004.Show Context CrossRef Google Scholar 2.J. W. Krakauer, “Motor learning: its relevance to stroke recovery and neurorehabilitation”, Current Opinion in Neurology, vol. 19, no. 1, pp. 84, Feb. 2006.Show Context CrossRef Google Scholar 3.S. Balasubramanian, J. Klein and E. Burdet, “Robot-assisted rehabilitation of hand function”, Current Opinion in Neurology, vol. 23, no. 6, pp. 661-670, Dec. 2010.Show Context CrossRef Google Scholar 4.L. Bishop, A. M. Gordon and H. Kim, “Hand Robotic Therapy in Children with Hemiparesis: A Pilot Study”, American Journal of Physical Medicine & Rehabilitation, vol. 96, no. 1, pp. 1-7, Jan. 2017.Show Context CrossRef Google Scholar 5.O. Sandoval-Gonzalez et al., “Design and Development of a Hand Exoskeleton Robot for Active and Passive Rehabilitation”, International Journal of Advanced Robotic Systems, vol. 13, no. 66, pp. 1-12, 2016.Show Context CrossRef Google Scholar 6.P. Polygerinos, K. C. Galloway, E. Savage, M. Herman, K. O. Donnell and C. J. Walsh, “Soft robotic glove for hand rehabilitation and task specific training”, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2913-2919, 2015.Show Context View Article Full Text: PDF (3804KB) Google Scholar 7.D. Guilherme, N. DeSouza et al., “A pediatric robotic thumb exoskeleton for at-home rehabilitation: The isolated orthosis for thumb actuation (IOTA)”, International Journal of Intelligent Computing and Cybernetics, vol. 7, no. 3, pp. 233-252, 2014.Show Context Google Scholar 8.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Automated Evaluation of Hand Motor Function Recovery by Using Finger Pressure Sensing Device for Home Rehabilitation”, IEEE BIBE 2018, pp. 207-214, 2018.Show Context View Article Full Text: PDF (500KB) Google Scholar 9.K. Yamamoto, Y. Furudate, K. Chiba, Y. Ishida and S. Mikami, “Home Robotic Device for Rehabilitation of Finger Movement of Hemiplegia Patients”, 2017 IEEE/SICE International Symposium on System Integration, pp. TuE1.3:1-6, 2017.Show Context View Article Full Text: PDF (1305KB) Google Scholar 10.Y. Furudate, K. Yamamoto, K. Chiba, Y. Ishida and S. Mikami, “Quantification Method of Motor Function Recovery of Fingers by Using the Device for Home Rehabilitation”, 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 3872-3875, 2017.Show Context View Article Full Text: PDF (804KB) Google Scholar 11.K. Chiba et al., “Robotic Finger Rehabilitation Support Device for Home Use – An Analysis of the Effect of Finger Rehabilitation for the Upper Limb Function Recovery”, International Journal of Mechanical Engineering and Robotics Research, vol. 5, no. 4, pp. 288-294, Oct. 2016.Show Context CrossRef Google Scholar 12.“Joint motion: method of measuring and recording”, Churchill Livingstone, 1965.Show Context Google Scholar 13.S. Werle, J. Goldhahn, S. Drerup, B. R. Simmen, H. Sprott and D. B. Herren, “Age- and Gender-Specific Normative Data of Grip and Pinch Strength in a Healthy Adult Swiss Population”, J Hand Surg Eur Vol, vol. 34, no. 1, pp. 76-84, Feb. 2009.Show Context CrossRef Google Scholar 14.E. L. Altschuler et al., “Rehabilitation of hemiparesis after stroke with a mirror”, The Lancet, vol. 353, no. 9169, pp. 2035-2036, 1999.Show Context CrossRef Google Scholar
The field of wearable robotics has emerged as a leading-edge industry through a surge of development over the last 20 years. The increasing physical and cognitive interaction between robotics and their human creators has set the stage for a host of new robotic applications in medicine, aerospace, military, industry, and personal use. Military and medical applications have driven the development of advanced robotic technology, and their objectives will accelerate applications to neurological, orthopedic, and traumatic injuries that involve lost or degraded motor function of the lower extremities. Key enabling technologies are promoting wearable robotic systems that are effective, practical, affordable, and reliable. The field of wearable robotics is poised to change the way we view human abilities, disabilities, limitations, and potential. This chapter explores the current state of the art in wearable robotics, considering engineering and clinical perspectives. Key aspects of design are addressed and supported by examples to illustrate design features and traits. Finally, results are presented from recent pilot studies of lower extremity systems to support rehabilitation.
- 1.Ambrose RO, Aldridge H, Askew RS, Burridge RR, Bluethmann W, Diftler M, Lovchik C, Magruder D, Rehnmark F. Robonaut: NASA’s space humanoid. IEEE Intell Syst. 2000;1(4):57–63.CrossRefGoogle Scholar
- 2.Afzal T, Kern M, Tseng S-C, Lincoln J, Chang S-H. Metabolic expenditures during exoskeleton-assisted walking in person with multiple sclerosis, 2016 AAP Annual Meeting. Sacramento: Association of Academic Physiatrists; 2016. http://journals.lww.com/ajpmr/Fulltext/2016/03001/Abstracts_of_Scientific_Papers_and_Posters.1.aspx.Google Scholar
- 3.Banala SK, Kim SH, Agrawal SK, Scholz JP. Robot assisted gait training with active leg exoskeleton (ALEX). Neural Syst Rehabil Eng, IEEE Trans. 2009;17(1):2–8.CrossRefGoogle Scholar
- 4.Blicher J, Nielsen J. Cortical and spinal excitability changes after robotic gait training. Neurorehabil Neural Repair. 2009;23:143–9.CrossRefPubMedGoogle Scholar
- 5.Bortole M, Venkatakrishnan A, Zhu F, Moreno JC, Francisco GE, Pons JL, Contreras-Vidal JL. The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study. J Neuroeng Rehabil. 2015;12(1):1.CrossRefGoogle Scholar
- 6.Chang SH, Kern M, Afzal T, Tseng SC, Lincoln J, Francisco G. Wearable exoskeleton assisted rehabilitation in multiple sclerosis: feasibility and experience. In: González-Vargas J, Ibáñez J, Contreras-Vidal J, van der Kooij H, Pons J, editors. Wearable robotics: challenges and trends. Biosystems & Biorobotics, vol 16. Cham: Springer; 2017. p. 15–9.Google Scholar
- 7.Daly JJ, Ruff RL. Construction of efficacious gait and upper limb functional interventions based on brainplasticity evidence and model-based measures for stroke patients. Sci World J. 2007;7:2031–45.CrossRefGoogle Scholar
- 8.Defense Advanced Research Projects Agency Website. [Internet] [cited 23 January 2017]. Available from: http://www.darpa.mil/program/warrior-web.
- 9.Del-Ama AJ, Gil-Agudo A, Pons JL, Moreno JC. Hybrid gait training with an overground robot for people with incomplete spinal cord injury: a pilot study. Front Hum Neurosci. 2014;8:298.CrossRefPubMedPubMedCentralGoogle Scholar
- 10.Esquenazi A, Talaty M, Packel A, Saulino M. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil. 2012;91(11):911–21.CrossRefPubMedGoogle Scholar
- 11.Hartigan C, Kandilakis C, Dalley S, Clausen M, Wilson E, Morrison S, Etheridge S, Farris R. Mobility outcomes following five training sessions with a powered exoskeleton. Topics Spinal Cord Inj Rehabil. 2015;21(2):93–9.CrossRefGoogle Scholar
- 12.Kazerooni H. Human-robot interaction via the transfer of power and information signals. Syst, Man Cybern, IEEE Trans. 1990;20(2):450–63.CrossRefGoogle Scholar
- 13.Luft AR, Macko RF, Forrester LW, et al. Treadmill exercise activates subcortical neural networks and improves walking after stroke. A randomized controlled trial. Stroke. 2008;39:3341–50.CrossRefPubMedPubMedCentralGoogle Scholar
- 14.Makinson BJ. Research and development prototype for machine augmentation of human strength and endurance. hardiman i project. General electric Co Schenectady Ny specialty materials handling products operation; 1971.Google Scholar
- 15.Pilleri M, Weis L, Zabeo L, Koutsikos K, Biundo R, Facchini S, Rossi S, Masiero S, Antonini A. Overground robot assisted gait trainer for the treatment of drug-resistant freezing of gait in Parkinson disease. J Neurol Sci. 2015;355(1–2):75–8.CrossRefPubMedGoogle Scholar
- 16.Pratt GA, Williamson MM. Series elastic actuators. Intelligent robots and systems 95.‘Human robot interaction and cooperative robots’, Proceedings. 1995 IEEE/RSJ International Conference on 1995 Aug 5. IEEE. Vol. 1, p. 399–406.Google Scholar
- 17.Pons JL. Wearable robots: biomechatronic exoskeletons. Hoboken: Wiley; 2008.CrossRefGoogle Scholar
- 18.Rea R, Beck C, Rovekamp R, Diftler M, Neuhaus P. X1: a robotic exoskeleton for in-space countermeasures and dynamometry. In: AIAA SPACE 2013 Conference and Exposition 2013 Sep. https://ntrs.nasa.gov/search.jsp?R=20140000694.
- 19.Vallery H, Veneman J, Asseldonk EV, Ekkelenkamp R, Buss M, Kooij HV. Compliant actuation of rehabilitation robots. Robot Autom Mag, IEEE. 2008;15(3):60–9.CrossRefGoogle Scholar
- 20.Winchester P, McColl R, Querry R, et al. Changes in supra-spinal activation patterns following robotic locomotor therapy in motor-incomplete spinal cord injury. Neurorehabil Neural Repair. 2005;19:313–24.CrossRefPubMedGoogle Scholar
- 21.Yang A, Asselin P, Knezevic S, Kornfeld S, Spungen AM. Assessment of in-hospital walking velocity and level of assistance in a powered exoskeleton in persons with spinal cord injury. Top Spinal Cord Inj Rehabil. 2015;21(2):100–9.CrossRefPubMedPubMedCentralGoogle Scholar
- 22.Zeilig G, Weingarden H, Zwecker M, Dudkiewicz I, Bloch A, Esquenazi A. Safety and tolerance of the ReWalk™ exoskeleton suit for ambulation by people with complete spinal cord injury: a pilot study. J Spinal Cord Med. 2012;35(2):96–101.CrossRefPubMedPubMedCentralGoogle Scholar
- 23.Zoss AB, Kazerooni H, Chu A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mechatronics. 2006;11(2):128–38.CrossRefGoogle Scholar
Many people in the world are increasingly suffering from stroke issues. Survivors often tend to suffer from hemiplegia or related conditions, in which some portion of their body may be rendered useless. The wrist is one such part. But this injury can be recovered by conventional rehabilitation processes like physical therapy. In this paper, a device for robot-assisted physical therapy is presented for wrist rehabilitation. It can overcome the lack of availability of physical therapists and reduce the cost incurred in long-term therapy. Also, it can provide accurate regular exercises without missing any step even in the absence of the therapist. These two DOF robotic devices can learn the physical exercise (i.e. wrist-based movements) from the trained therapist through an electronic smart-band. It can also replicate these exercises when the patient wears this device over his/her wrist. Here, an accelerometer sensor and a magnetometer sensor-based smart-band are used for recognizing the wrist motions like flexion, extension, abduction, and adduction. The objective of this preliminary work is to drive accurately all the motor actuators which are attached to the robot and calibrate the feedback sensor to reflect the movement of the smart-band. In the future, this robot can be used as a teleoperated rehabilitation device through an IoT platform.
[ARTICLE] An Exoneuromusculoskeleton for Self-Help Upper Limb Rehabilitation After Stroke – Full Text
This article presents a novel electromyography (EMG)-driven exoneuromusculoskeleton that integrates the neuromuscular electrical stimulation (NMES), soft pneumatic muscle, and exoskeleton techniques, for self-help upper limb training after stroke. The developed system can assist the elbow, wrist, and fingers to perform sequential arm reaching and withdrawing tasks under voluntary effort control through EMG, with a lightweight, compact, and low-power requirement design. The pressure/torque transmission properties of the designed musculoskeletons were quantified, and the assistive capability of the developed system was evaluated on patients with chronic stroke (n = 10). The designed musculoskeletons exerted sufficient mechanical torque to support joint extension for stroke survivors. Compared with the limb performance when no assistance was provided, the limb performance (measured as the range of motion in joint extension) significantly improved when mechanical torque and NMES were provided (p < 0.05). A pilot trial was conducted on patients with chronic stroke (n = 15) to investigate the feasibility of using the developed system in self-help training and the rehabilitation effects of the system. All the participants completed the self-help device-assisted training with minimal professional assistance. After a 20-session training, significant improvements were noted in the voluntary motor function and release of muscle spasticity at the elbow, wrist, and fingers, as indicated by the clinical scores (p < 0.05). The EMG parameters (p < 0.05) indicated that the muscular coordination of the entire upper limb improved significantly after training. The results suggested that the developed system can effectively support self-help upper limb rehabilitation after stroke. ClinicalTrials.gov Register Number NCT03752775.
Upper limb motor deficits are noted in >80% of stroke survivors,1,2 who require continuous long-term physical rehabilitation to reduce upper limb impairments.3,4 Restoration of poststroke limb function requires intensive repeated training of the paralyzed limb5,6 with maximized voluntary motor effort7,8 and minimized compensatory motions in close-to-normal muscular coordination.8,9 However, long-term poststroke rehabilitation is challenging because of the expanding stroke population and insufficiency of professional staff worldwide.10,11 Effective rehabilitation methods with potential for self-help training by stroke survivors are urgently required to improve the independency of stroke survivors and decrease the burden on the health care system. Suitable technologies for these methods are currently lacking.11,12
Various rehabilitation robots have been developed to assist the labor-intensive process of physical poststroke training, with main advantages of higher dosage and lower cost compared with traditional “one-to-one” manual physical therapy.13 However, these robots are large equipment powered by alternating current (AC) that require professional operation in a clinical environment with limited access to outpatients. Mobile exoskeletons are an emerging technology with wearable application. These exoskeletons are powered by portable batteries and have potential for user-independent self-help rehabilitation that can be accessed anytime, even in unconventional environments (e.g., at home).12,14,15 However, currently available upper limb exoskeletons, which are composed of rigid materials and actuated by electrical motors, are constrained by their heavy weight and low torque-to-weight ratio, which limit their user-independent applications. These exoskeletons require high-power consumption because their actuations must generate sufficient torque to support paralyzed limbs as well as the weight of the system worn on the body. Thus, most exoskeletons require AC supply,11,15,16 which triggers electrical safety concerns for user-independent usage.
Furthermore, the body/device integration is neither stable nor comfortable in current rigid exoskeletons, with misalignment or migration occurring during repeated practice mainly because of the non-negligible weights mounted onto the paretic limb.11,14 Misalignments with additional loads deteriorate abnormal muscular coordination in the paralyzed upper limb, which undermines the rehabilitative potential of the aforementioned systems.17,18 Therefore, most rigid exoskeletons for poststroke upper limb rehabilitation are still used under the close assistance of professionals in clinical environments, and their rehabilitation effects in user-independent operations are unclear.
With the introduction of soft materials in mechanical actuation, soft robotic equipment has been designed using easily deformable materials with light and flexible actuators that conform to human body contours19–22 so as to achieve superior body/device integration to that provided by rigid robotic equipment. Three main types of actuation systems, namely cable, hydraulic, and pneumatic systems, are used in current wearable soft robots.21 Cable systems used cables with desired tension attached to a target limb for flexion/extension.11,23 The cable-driven upper limb exoskeletons usually have a lightweight design with low inertia in the wearable part accommodating possible joint misalignment between the paretic limb and the exoskeleton.23 However, the cable is driven by electric motors with gears/pulleys, leading to an increment of complexity and overall weight of the whole assembly.23 Hydraulic systems are powered by hydraulic pressure, and able to produce greater torque compared with the actuators in cable and pneumatic systems.11,23,24 However, few hydraulic systems have been developed for upper limb, because they are relatively heavy and complex in the design, requiring additional space to accommodate the fluid and to prevent leakages under pressure.11,16,23
In contrast, pneumatic systems (pneumatic muscles) are the most commonly adopted actuation for the upper limb.21,23 Pneumatic exoskeletons have high torque-to-weight ratios because of the low weight of the wearable part actuated by air.21,25–29 However, pneumatic systems are usually bulky and slow in power transmission from pressure to torque during air inflation by compressors for needed air volume and pressure compared with electrical motor actuation in rigid exoskeleton to achieve equivalent mechanical outputs (e.g., joint torque).23,30 Large and high-power compressors connected to the pneumatic muscles constrain these devices for user-independent applications.21 Thus, a novel lightweight mechanical design is required to achieve optimized body/device integration with fast power transmission, high torque-to-weight ratios, and low-power consumption for user-independent self-help rehabilitation.
Neuromuscular electrical stimulation (NMES), proposed for upper limb rehabilitation,31,32 can activate the contraction of impaired muscles to generate limb movement31,32 and effectively enhance the muscle force and sensory feedback for motor relearning after stroke.33 However, controlling motion kinematics, such as the range of motion (ROM) and trajectory, by using NMES alone is difficult because of the limited stimulating precision in fine motor control.34 Recently, NMES has been combined with mechanical robots in poststroke training.35 The combined NMES-robot treatment is more effective than treatment involving the use of only NMES or only a robot in upper limb rehabilitation, particularly in improving muscular coordination by reducing muscular compensation.36 The integration of NMES into a robot can trigger the biological actuation of target muscles to reduce the demand of mechanical support from the robot part.11 However, little has been done on the integration of NMES with mobile exoskeletons or soft robots.
In this study, we designed a multi-integrated robotic system that combines the NMES, soft pneumatic muscle, and exoskeleton techniques, namely exoneuromusculoskeleton, for upper limb rehabilitation after stroke. Mechanical integration between rigid exoskeleton and pneumatic muscle (i.e., exomusculoskeleton) can enable high torque-to-weight ratios with a compact size and fast power transmission. By combining NMES with the exomusculoskeleton (i.e., exoneuromusculoskeleton), the mechanical scale and power requirement of the entire system can be reduced due to the evoked muscular effort. In addition, NMES and mechanical assistance enable the achievement of close-to-normal muscular coordination with minimized compensatory motions. To optimize therapeutic outcomes, electromyography (EMG) of the paralyzed limb has been used to indicate voluntary intentions37 to maximize voluntary motor effort during practice for better improvements in voluntary motor functions with longer sustainability compared with those with passive limb motions.38
In this study, we designed an EMG-driven exoneuromusculoskeleton to assist the upper limb physical practice at the elbow, wrist, and fingers. The assistive capability of the designed system was evaluated on patients with chronic stroke. The designed system’s feasibility of self-help operation and rehabilitation effects were also investigated through a pilot single-group trial.
The designed exoneuromusculoskeleton (Fig. 1) could be worn on the paretic upper limb of a stroke survivor. The designed system comprised two wearable parts: the elbow (158 g) and wrist/hand (50 g). Both parts were connected to a pump box (80 g) mounted on the upper limb. Moreover, a control box (358 g) that included system control circuits and a rechargeable 12-V battery could be carried on the waist. The developed system can assist a stroke survivor to perform sequential arm reaching and withdrawing tasks, namely (1) elbow extension, (2) wrist extension with the hand open, (3) wrist flexion with the hand closed, and (4) elbow flexion. Real-time control and wireless communication between the control box and a mobile application (app) were achieved on a smartphone through a microprocessor and Bluetooth module.
[Abstract] Attention Enhancement and Motion Assistance for Virtual Reality-Mediated Upper-limb Rehabilitation
Dysfunctions of upper limbs caused by diseases such as stroke result in difficulties in conducting day-to-day activities. Studies show that rehabilitation training using virtual reality games is helpful for patients to restore arm functions. It has been found that ensuring active patient participation and effort devoting in the process is very important to obtain better training results. This paper introduces a method to help patients increase their engagement and provide motion assistance in virtual reali-ty-mediated upper-limb rehabilitation training. Attention en-hancement and motion assistance is achieved through an illusion of virtual forces created by altering the drag speed between the cursor and the object presented on a screen to the patient as the only feedback. We present two game forms using the proposed method, including a target-approaching game and a maze-following game. The results of evaluation experiments with human participants showed that the proposed method could provide path guidance that significantly improved path-following performance of users and required more involvement of the users when compared to playing the game without attention enhance-ment and motion assistance.