Posts Tagged Hand exoskeleton

[ARTICLE] Direct Drive Hand Exoskeleton for Robot-assisted Post Stroke Rehabilitation – Full Text PDF

Abstract

This article introduces novel rehabilitation hand module development for the
physiotherapy of the hand of patients suffering from spastic hemiplegia. Spasm is basically a muscle cramp, it practically involves the sudden, unintended and painful contraction of a muscle or muscle group, which is caused by nerve damage resulting from a stroke. Stroke is the main reason for permanent disability in adulthood, and so the social- and medical care systems require a huge amount of healthcare resources due to the inactivity of the patients concerned. The robotically facilitated rehabilitation assists the physicians in providing repeated therapies of great intensity, and so the patients may enjoy the benefits of rehabilitation, while the therapists may reduce their own workload at the same time. Furthermore, the robotic devices offer an objective and reliable opportunity for tracing and accurately assessing the improvement of the patients’ motor skills. This article introduces the electrical- and mechanical design of a therapeutic device and the inverse kinematic and dynamic modules which control this device. The rehabilitation device is capable of moving the thumb, the index-, the middle- and the ring fingers, and allows the rehabilitation of the left- and right hands as well. The device is a completely new design with direct
drive approach and several benefits. It has two components: a planar module with serial kinematics of rotational joints with three degrees of freedom (3DoF RRR), and another module with two degrees of freedom (2DoF). The modules integrated load cells, which are built in between each joint to measure the reaction forces. The 3DoF finger moves the index, the middle and the ring fingers, using a load distributor placed above the fingers. The finger orthoses are connected to the load distributor via magnets. The 2DoF finger moves the thumb performing the opening/closing along the plane tilted in two angles.[…]

Full Text PDF

, , , , , , , , , ,

Leave a comment

[Abstract] EMG based motion intention estimation to control a hand exoskeleton robot for rehabilitation

Abstract

Recently, robot assisted rehabilitation has drawn attention over the traditional rehabilitation techniques, owing to its intuitiveness, customizability and effectiveness. However, identification of the correct motion intention of the human is one of the key elements in robot assisted rehabilitation. Over the recent years, electromyography signals are used to identify human motion intention. However, during hand open/close motions both extensor and flexor muscle groups of the forearm show activation and it is necessary to separate the activation to identify the motion intention. In this study the effectiveness of different classification techniques in separating the muscle activation with different muscles configurations is evaluated. Results show with only two muscle sites, motion intention can be identified with a higher accuracy.

Source

, , , , , , , , ,

Leave a comment

[Abstract + References] A New Hand Exoskeleton Framework for Rehabilitation of Fingers

Abstract

Objective: This study investigates a new hand exoskeleton control framework for stroke patients using a brain-computer interface (BCI) technique.

Methods: Hand rehabilitation exoskeletons are required to improve critical features, including portability, low cost, safe human-robotic interaction, and intelligent control. The new hand exoskeleton works on brain-computer interface techniques to present a feasibility human-control framework, further to evaluate stroke patients in rehabilitation action research. A visualization design software is utilized to construct the mechatronic system of the hand exoskeleton. A subscript process in the software sets motion parameters of torque, angular displacement, and angular velocity to present a feasibility aim in extension and flexion motion.

Main results: The simulation results showed that the proposed hand exoskeleton could generate enough range in finger extension and flexion motion without any interruptions, and real motion experiments by the 3D product also support the feasibility result.

Significant: The hand exoskeleton can be used to explore stroke patients in cortex hemodynamic changes using a brain imaging modality, such as function near-infrared spectroscopy.

References

1.N. Spychala, S. Debener, E. Bongartz, H. H. O. Muller, J. D. Thorne, A. Philipsen, et al., “Exploring self-paced embodiable neurofeedback for post-stroke motor rehabilitation”, Front. Hum. Neurosci., vol. 13, 2020.Show Context CrossRef  Google Scholar 2.C.-H. Han, H.-J. Hwang, J.-H. Lim and C.-H. Im, “Assessment of user voluntary engagement during neurorehabilitation using functional near-infrared spectroscopy: a preliminary study”, J. NeuroEng. Rehab., vol. 15, no. 27, 2018.Show Context CrossRef  Google Scholar 3.R. A. Khan, N. Naseer, N. K. Qureshi, F. M. Noon, H. Nazeer and M. U. Khan, “fNIRS-based neurorobotic interface for gait rehabilitation”, J. NeuroEng. Rehab., vol. 15, no. 7, 2018.Show Context CrossRef  Google Scholar 4.C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala and S. C. Cramer, “Robot-based hand motor therapy after stroke”, Brain, vol. 131, pp. 425-437, 2008.Show Context CrossRef  Google Scholar 5.P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy and S. Leonhardt, “A survey on robotic devices for upper limb rehabilitation”, J. Neuroeng. Rehabil., vol. 11, no. 3, 2014.Show Context CrossRef  Google Scholar 6.E. Carmeli, S. Peleg, G. Bartur, E. Elbo and J. J. Vatine, “HandTutor enhanced hand rehabilitation after stroke – a pilot study”, Physiother. Res. Int., vol. 16, pp. 191-200, 2011.Show Context CrossRef  Google Scholar 7.S. L. Wolf, C. J. Winstein, J. P. Miller and D. Morris, “Effect of constraint induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial”, JAMA, vol. 296, pp. 2095-2104, 2006.Show Context CrossRef  Google Scholar 8.X. S. Hu, K.-S. Hong and S. S. Ge, “fNIRS-based online deception decoding”, J. Neural. Eng., vol. 9, no. 026012, 2012.Show Context CrossRef  Google Scholar 9.X. S. Hu, K.-S. Hong and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity”, J. Biomed. Opt., vol. 18, no. 017003, 2013.Show Context CrossRef  Google Scholar 10.N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right-and left-wrist motor imagery for development of a brain-computer interface”, Neurosci. Lett., vol. 553, pp. 84-89, 2013.Show Context CrossRef  Google Scholar 11.K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy”, Hear. Res., vol. 333, pp. 157-166, 2016.Show Context CrossRef  Google Scholar 12.N. Naseer, M. J. Hong and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface”, Exp. Brain Res., vol. 232, pp. 555-564, 2013.Show Context CrossRef  Google Scholar 13.W. P. Teo and E. Chew, “Is motor-imagery brain-computer interface feasible in stroke rehabilitation”, PM. R, vol. 6, pp. 723-728, 2014.Show Context CrossRef  Google Scholar 14.M. Li, G. Xu, J. Xie and C. Chen, “A review: motor rehabilitation after stroke with control based on human intent”, Proc. of the Inst. Mech. Eng. H, vol. 232, pp. 344-360, 2018.Show Context CrossRef  Google Scholar 15.S. Jiang, L. Chen, Z. Wang, J. Xu, C. Qi and H. Qi, “Application of BCI-FES system on stroke rehabilitation”, Proc. of the International IEEE/EMBS Conference on Neural Engineering, pp. 1112-1115, 2010.Show Context Google Scholar 16.F. Pichiorri, G. Morone, M. Petti, J. Toppi, I. Pisotta and M. Molinari, “Brain-computer interface boosts motor imagery practice during stroke recovery”, Ann. Neurol., vol. 77, pp. 851-865, 2015.Show Context CrossRef  Google Scholar 17.A. Vourvopoulos, S. Bermúdez and I Badia, “Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain computer interaction: A within-subject analysis”, J. Neuroeng. Rehabil., vol. 13, no. 69, 2016.Show Context CrossRef  Google Scholar 18.R. Xu, N. Jiang, N. Mrachacz-Kersting, C. Lin, G. Asin Prieto and J. C. Moreno, “A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity”, IEEE Trans. Biomed. Eng., vol. 61, pp. 2092-2101, 2014.Show Context Google Scholar 19.N. A. Bhagat, A. Venkatakrishnan, B. Abibullaev, E. J. Artz, N. Yozbatiran and A. A. Blank, “Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors”, Front. Neurosci., vol. 10, 2016.Show Context CrossRef  Google Scholar 20.X. Zhang, G. Xu, J. Xie, M. Li, W. Pei and J. Zhang, “An EEG-driven Lower limb rehabilitation training system for active and passive co-stimulation”, Proc. of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4582-4585, 2015.Show Context View Article Full Text: PDF (765KB) Google Scholar 21.M. J. Khan and K.-S. Hong, “Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control”, Front. Neurorobot., vol. 11, 2017.Show Context CrossRef  Google Scholar 22.P. Heo, G. M. Gu, S.-J. Lee, K. Rhee and J. K. Kim, “Current hand exoskeleton technologies for rehabilitation and assistive engineering”, Int. J. Precis. Eng. Manuf., vol. 13, pp. 807-824, 2012.Show Context CrossRef  Google Scholar 23.J. Arata, K. Ohmoto, R. Gassert, O. Lambercy, H. Fujimoto and I. Wada, “A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism”, Proc. of the 2013 IEEE International Conference on Robotics and Automation, pp. 3902-3907, 2013.Show Context View Article Full Text: PDF (1226KB) Google Scholar 24.P. Polygerinos, Z. Wang, K. C. Galloway, R. J. Wood and C. J. Walsh, “Soft robotic glove for combined assistance and at-home rehabilitation”, Rob. Auton. Syst., vol. 73, pp. 135-143, 2015.Show Context CrossRef  Google Scholar 25.K. Y. Tong, S. K. Ho, P. M. K. Pang, X. L. Hu, W. K. Tam and K. L. Fung, “An intention driven hand functions task training robotic system”, Proc. of the IEEE Eng. Med. Biol. Soc. 2010, pp. 3406-3409, 2010.Show Context View Article Full Text: PDF (813KB) Google Scholar 26.S. Ito, H. Kawasaki, Y. Ishigure, M. Natsume, T. Mouri and Y. Nishimoto, “A design of fine motion assist equipment for disabled hand in robotic rehabilitation system”, J. Franklin Inst., vol. 348, pp. 79-89, 2011.Show Context CrossRef  Google Scholar 27.J. Arata, K. Ohmoto, R. Gassert, O. Lambercy, H. Fujimoto and I. Wada, “A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism”, Proc. of the 2013 IEEE International Conference on Robotics and Automation, pp. 3902-3907, 2013.Show Context View Article Full Text: PDF (1226KB) Google Scholar 28.L. Cui, A. Phan and G. Allison, “Design and fabrication of a three dimensional printable non-assembly articulated hand exoskeleton for rehabilitation”, Proc. of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4627-4630, 2015.Show Context View Article Full Text: PDF (706KB) Google Scholar 29.S. Nakagawara, H. Kajimoto, N. Kawakami, S. Tachi and I. Kawabuchi, “An encounter-type multi-fingered master hand using circuitous joints”, Proc. of the IEEE International Conference on Robotics and Automation, pp. 2667-2672, 2005.Show Context View Article Full Text: PDF (979KB) Google Scholar 30.H. Fang, Z. Xie and H. Liu, “An exoskeleton master hand for controlling DLR/HIT hand”, Proc. of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 3703-3708, 2009.Show Context View Article Full Text: PDF (939KB) Google Scholar 31.N. S. K. Ho, K. Y. Tong, X. L. Hu, K. L. Fung, X. J. Wei and W. Rong, “An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation”, Proc. of the IEEE Int. Conf. Rehabil. Robot., no. 5975340, 2011.Show Context View Article Full Text: PDF (848KB) Google Scholar 32.C. J. Nycz, T. Butzer, O. Lambercy, J. Arata, G. S. Fischer and R. Gassert, “Design and characterization of a lightweight and fully portable remote actuation system for use with a hand exoskeleton”, Proc. of the IEEE Robot. Autom. Lett., vol. 1, pp. 976-983, 2016.Show Context View Article Full Text: PDF (1044KB) Google Scholar 33.M. A. Delph, S. A. Fischer, P. W. Gauthier, C. H. M. Luna, E. A. Clancy and G. S. Fischer, “A soft robotic exomusculature glove with integrated sEMG sensing for hand rehabilitation”, Proc. of the 2013 IEEE 13th International Conference on Rehabilitation Robotics, pp. 1-7, 2013.Show Context View Article Full Text: PDF (2920KB) Google Scholar 34.H. In, B. K. Kang, M. Sin and K. Cho, “Exo-Glove: a soft wearable robot for the hand using soft tendon routing system”, IEEE Robot. Autom. Magaz., vol. 22, pp. 97-105, 2015.Show Context View Article Full Text: PDF (2529KB) Google Scholar 35.M. Rahmani, M. H. Rahman and J. Ghommam, “A 7-dof upper limb exoskeleton robot control using a new robust hybrid controller”, Int. J. Cont. Auto. Syst., vol. 17, no. 4, pp. 986-994, 2019.Show Context CrossRef  Google Scholar 36.H. D. Lee, J. I. Moon and T. H. Kang, “Design of a series elastic tendon actuator based on gait analysis for a walking assistance exosuit”, Int. J. Cont. Auto. Syst., vol. 17, no. 11, pp. 2940-2947, 2019.Show Context CrossRef  Google Scholar 37.M. Rahmani and M. H. Rahman, “Adaptive neural network fast fractional sliding mode control of a 7-dof exoskeleton robot”, Int. J. Cont. Auto. Syst., vol. 18, no. 1, pp. 124-133.Show Context CrossRef  Google Scholar 38.E. Bae, S. E. Park, Y. Moon, I. T. Chun, M. H. Chun and J. Choi, “A robotic gait training system with stair-climbing mode based on a unique exoskeleton structure with active foot plates”, Int. J. Cont. Auto. Syst., vol. 18, no. 1, pp. 196-205, 2020.Show Context CrossRef  Google Scholar 39.M. Jeong, H. Woo and K. Kong, “A study on weight support and balance control method for assisting squat movement with a wearable robot angel-suit”, Int. J. Cont. Auto. Syst., vol. 18, no. 1, pp. 114-123, 2020.Show Context CrossRef  Google Scholar 40.A. Borboni, M. Mor and R. Faglia, “Gloreha-hand robotic rehabilitation: Design mechanical model and experiments”, J. Dyn. Syst. Meas. Control, vol. 138, no. 111003, 2016.Show Context CrossRef  Google Scholar 41.N. Smaby, M. E. Johanson, B. Baker, D. E. Kenney, W. M. Murray and V. R. Hentz, “Identification of key pinch forces required to complete functional tasks”, J. Rehabil. Res. Dev., vol. 41, pp. 215-224, 2004.Show Context CrossRef  Google Scholar 42.D. L. Yang, K.-S. Hong, S.-H. Yoo and C.-S. Kim, “Evaluation of neural degeneration biomarkers in the prefrontal cortex for early identification of patients with mild cognitive impairment: an fNIRS study”, Fron. Hum. Neuro., vol. 13, 2019.Show Context CrossRef  Google Scholar 43.K.-S. Hong and M. A. Yaqub, “Application of functional near-infrared spectroscopy in the health industry: A review”, J. Innov. Optic. Heal. Sci., vol. 12, no. 6, 2019.Show Context Google Scholar 44.M. A. Tanveer, M. J. Khan, M. J. Qureshi, N. Naseer and K.-S. Hong, “Enhance drowsiness detection using deep learning: an fNIRS study”, IEEE Access, vol. 7, no. 1, 2019.Show Context Google Scholar 

Source

, , , , ,

Leave a comment

[Abstract] A Review on Surface Electromyography-Controlled Hand Robotic Devices Used for Rehabilitation and Assistance in Activities of Daily Living

Abstract

Introduction

Spinal cord injuries, traumas, natural aging, and strokes are the main causes of arm impairment or even a chronic disability for an increasing part of the population. Therefore, robotic devices can be essential tools to help individuals afflicted with hand deficit with the activities of daily living in addition to the possibility of restoring hand functions by rehabilitation. Because the surface electromyography (sEMG) control paradigm has recently emerged as an interesting intention control method in devices applied to rehabilitation, the concentration in this study has been devoted to sEMG-controlled hand robotic devices, including gloves and exoskeletons that are used for rehabilitation and for assistance in daily activities.

Materials and Methods

A brief description is given to the previous reviews and studies that have surveyed the robotic devices used for rehabilitation; a comparison is conducted among these studies with respect to the targeted part of the body and the device’s control method. Important issues about controlling by sEMG signal are accentuated, and a review of sEMG-controlled hand robotic devices is presented with an abbreviated description for each endeavor. Some criteria related to sEMG control are specifically emphasized, for instance, the muscles used for control, the number of sEMG channels, and the type of sEMG sensor used.

Discussion

It is noted that most of the sEMG-based controls for the devices included in this study have used the nonpattern recognition scheme due to the weak sEMG signals and abnormal pattern of muscle activation for stroke patients. In addition to sEMG-based control, additional control paradigms have been used in many of the listed robotic devices to increase the efficacy of the system; this cooperation is required because of the difficulty in dealing with the sEMG signals of stroke patients. Most of the listed studies have conducted the experiments on a healthy subject to evaluate the efficacy of the systems, whereas the studies that have recruited stroke patients for system assessment were predominately using additional control schemes.

Conclusions

This article highlights the important issues about the sEMG control method and accentuates the weaknesses associated with this type of control to assist researchers in overcoming problems that impede sEMG-controlled robotic devices to be feasible and practical tools for people afflicted with hand impairment.

via A Review on Surface Electromyography-Controlled Hand Robotic… : JPO: Journal of Prosthetics and Orthotics

, , , , , , ,

Leave a comment

[ARTICLE] Evaluation of a 1-DOF Hand Exoskeleton for Neuromuscular Rehabilitation – Full Text PDF

Abstract

A low-cost 1-DOF hand exoskeleton for neuromuscular rehabilitation has been designed and assembled. It consists of a base equipped with a servo motor, an index finger part, and a thumb part, connected through three gears. The index part has a tri-axial load cell and an attached ring to measure the finger force. An admittance control scheme was designed to provide intuitive control and positive force amplification to assist the user’s finger movement. To evaluate the effects of different control parameters on neuromuscular response of the fingers, we created an integrated exoskeleton-hand musculoskeletal model to virtually simulate and optimize the control loop. The exoskeleton is controlled by a proportional derivative controller that computes the motor torque to follow a desired joint angle of the index part, which is obtained from inverse kinematics of a virtual end-effector mass driven by the finger force. We conducted parametric simulations of the exoskeleton in action, driven by the user’s
closing and opening finger motion, with different proportional gains, endeffector masses, and other coefficients. We compared the interaction forces between the index finger and the ring in both passive and active modes. The best performing assistive controller can reduce the force from around 1.45N (in passive mode) to only around 0.52N, more than 64% of reduction. As a result, the muscle activations of the flexors and extensors were reduced significantly. We also noted the admittance control scheme is versatile and can also provide resistance (e.g. for strength training) by simply increasing the virtual end-effect mass.

1 Introduction

Stroke, one of the leading causes of adult disability, affects approximately 800,000 individuals each year in the United States [1]. Nearly 80% of stroke survivors suffer
from hemiparesis of the upper arm and thus impaired hand function, which is integral
to most activities of daily living. It is well established that highly repetitive training
can aid in the recovery of motor function of the hand however this can be labor intensive for the providing physical therapist in addition to the cost. In the past decade,
more robotic hand rehabilitation devices have been introduced to help patients recover
hand function through assistance during repetitive training of the hand [2-4].
In a comprehensive review by Heo et al. [2], hand exoskeleton technologies for rehabilitation and assistive engineering, from basic hand biomechanics, neurophysiology, sensors and actuators, physical human-robot interactions and ergonomics, are summarized. Different types of actuators and control schemes have been used for hand exoskeletons. In some control schemes, the robotic device will move the user passively through a preprogrammed trajectory for continuous passive movement (CPM) therapy. These devices can be beneficial for severely impaired individuals who may not have the ability to generate the forces required for specific finger or hand movement or for individuals who have abnormal muscle synergies preventing continuous movement. A few devices such as the Kinetic Maestra and Vector 1 are commercially available devices that are used for CPM [5, 6]. These devices allow for
passive movement through the range of motion for individual fingers. However, as
there is no active participation by the user, this device on its own may not promote
neurorehabilitation. These devices can be combined with other simulations or control
schemes that require active participation by the user. One commercially available
hand exoskeleton that has been used extensively by our lab to provide haptics to virtual simulations is the CyberGrasp [7]. The CyberGrasp is a cable driven exoskeleton
that weighs 450 grams and can provide up to 12 N of force on each finger and can be
used to provide assistance for extension of the user’s fingers. In one study, this was
used in combination with a virtual reality simulation to train finger individuation as
the user played a virtual piano [8]. The CyberGrasp was used to resist finger flexion
of the inactive fingers, promoting movement of the active independent finger. Similarly, the eXtension Glove (X-Glove) was developed to be used for cyclical stretching
in addition to active movement training [9-11]. This cable driven design is actuated
using linear servos allowing for individual finger movement in both extension and
flexion. In addition to this, each cable is integrated with a tension sensor which allows
the force of each digit to be monitored. This device has two modes that can be used
for rehabilitation, the first mode cyclically extends and flexes the fingers. The second
mode is an active training mode in which the glove provides constant extension assistance so that the user can complete flexion tasks as long as they overcome the force
required to keep the finger extended. In a further attempt to integrate user control with
the exoskeleton, an external input from the user such as force or electromyography
has been incorporated into some designs such as the Helping Hand [12]. This soft
robotic device allows for active assistance for each finger individually, in addition to
the ability to follow control states triggered by EMG.

In this paper, we introduce a low cost 1-DOF hand exoskeleton for neuromuscular
rehabilitation of individual fingers. This exoskeleton consists of a base equipped with
a servo motor, an index finger component and a thumb component connected with
gears. The exoskeleton’s control system was designed to generate suitable actuation
torques based on the interaction force between the user’s finger and the exoskeleton’s
index component. The goal of this study is to model the exoskeleton interacting with a
neuromuscular hand model in order to evaluate the effectiveness of an intuitive admittance control algorithm on providing different levels of assistance or resistance during hand rehabilitation.

2 Methods

2.1 The 1-DOF Exoskeleton and Hand Model

This exoskeleton consists of a base stationed with a servo motor (Dynamixel
XM430), an index finger part and a thumb part, which are connected through 3 gears
of equal sizes as shown in Fig. 1. The motor drives the top gear which in turn rotates
the gear attached to the index part and then the gear attached the thumb part. The
index and thumb parts both have rings for the fingers, and an OptoForce tri-axial load
cell or force sensor (OnRobot, Denmark) is attached to the index ring. All parts are
3D printed with a carbon fiber reinforced nylon material called Onyx (Markforged,
USA). The total weight of this exoskeleton is 0.158kg and the mass and inertia properties of its components, which were either measured or computed based on material
and part geometry, are listed in Table 1.

Related image

Fig. 1. The design of the 1-DOF hand exoskeleton

Full Text PDF

 

, , , , , , , , , , ,

Leave a comment

[ARTICLE] An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism – Full Text

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Introduction

Hand function is essential for our daily life (Heo et al., 2012). In fact, only partial loss of the ability to move our fingers can inhibit activities of daily living (ADL), and even reduce our quality of life (Takahashi et al., 2008). Research on robotic training of the wrist and hand has shown that improvements in finger or wrist level function can be generalized across the arm (Lambercy et al., 2011). Finger muscle weakness is believed to be the main cause of loss of hand function after strokes, especially for finger extension (Cruz et al., 2005Kamper et al., 2006). Hand rehabilitation requires repetitive task exercises, where a task is divided into several movements and patients are asked to practice those movements to improve their hand strength, range of motion, and motion accuracy (Takahashi et al., 2008Ueki et al., 2012). High costs of traditional treatments often prevent patients from spending enough time on the necessary rehabilitation (Maciejasz et al., 2014). In recent years, robotic technologies have been applied in motion rehabilitation to provide training assistance and quantitative assessments of recovery. Studies show that intense repetitive movements with robotic assistance can significantly improve the hand motor functions of patients (Takahashi et al., 2008Ueki et al., 2008Kutner et al., 2010Carmeli et al., 2011Wolf et al., 2006).

Patients should be actively involved in training to achieve better rehabilitation results (Teo and Chew, 2014Li et al., 2018). Motor rehabilitation has implemented Brain Computer Interface (BCI) methods as one of the means to detect human movement intent and get patients to be actively involved in the motor training process (Teo and Chew, 2014Li et al., 2018). Motor imagery-based BCIs (Jiang et al., 2015Pichiorri et al., 2015Kraus et al., 2016Vourvopoulos and Bermúdez I Badia, 2016), movement-related cortical potentials-based BCIs (Xu et al., 2014Bhagat et al., 2016), and steady-state motion visual evoked potential-based BCIs (Zhang et al., 2015) have been used to control rehabilitation robots. However, the high cost and complexity of the preparation in utilizing these methods mean that most current BCI devices are more suitable for research purposes than clinical practices. This is attributable to the fact that the ease of use and device cost are two main factors to consider during the selection of human movement intent detection based on BCIs for practical use (van Dokkum et al., 2015Li et al., 2018). Therefore, non-invasive, easy-to-install BCIs that are convenient to use with acceptable accuracy should be introduced to hand rehabilitation robot control.

Owing to the versatility and complexity of human hands, developing hand exoskeleton robots for rehabilitation assistance in hand movements is challenging (Heo et al., 2012Arata et al., 2013). In recent years, hand exoskeleton devices have drawn much research attention, and the results of current research look promising (Heo et al., 2012). Hand exoskeleton devices mainly use linkage, wire, or hydraulically/pneumatically driven mechanisms (Polygerinos et al., 2015a). The rigid mechanical design of linkage-based mechanisms provides robustness and reliability of power transmission, and has been widely applied in hand exoskeletons (Tong et al., 2010Ito et al., 2011Arata et al., 2013Cui et al., 2015Polygerinos et al., 2015a). However, the safety problem of misalignment between the human finger joints and the exoskeleton joints may occur during rehabilitation movements (Heo et al., 2012Cui et al., 2015). Compensation approaches used in current studies make the mechanism more complicated (Nakagawara et al., 2005Fang et al., 2009Ho et al., 2011). Pneumatic and hydraulic soft hand exoskeletons, which are made of flexible materials, are proposed to assist hand opening or closing (Ang and Yeow, 2017Polygerinos et al., 2015aYap et al., 2015b). However, despite bi-directional assistance—namely finger flexion and extension—being essential for hand rehabilitation (Iqbal et al., 2014), a large group of current soft hand exoskeleton devices only provide finger flexion assistance (Connelly et al., 2010Polygerinos et al., 20132015aYap et al., 2015ab). Wire-driven mechanisms can also be complex to transmit bi-directional movements since wires can only transmit forces along one direction (In et al., 2015Borboni et al., 2016). In order to transmit bi-directional movements, a tendon-driven hand exoskeleton was proposed, where the tendon works as a tendon during the extension movement and as compressed flexible beam constrained into rectilinear slides mounted on the distal sections of the glove during flexion (Borboni et al., 2016). Arata et al. (2013) attempted to avoid wire extension and other associated issues by proposing a hand exoskeleton with a three-layered sliding spring mechanism. Hand rehabilitation exoskeleton devices are still seeking to achieve key features such as low complexity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control.

In this article, we describe the design and characterization of a novel multi-segment mechanism driven by one layer of a steel spring that can assist both extension and flexion of the finger. Thanks to the inherent features of this multi-segment mechanism, joint misalignment between the device and the human finger is no longer a problem, enhancing the simplicity and flexibility of the device. Moreover, its compliance makes the hand exoskeleton safe for human-robotic interaction. This mechanism can generate enough range of motion with a single input by distributing an actuated linear motion to the rotational motions of finger joints. Active rehabilitation training is realized by using a threshold of the attention value measured by a commercialized electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. Features of this hand exoskeleton include active involvement of patients, low complexity, compactness, bi-directional actuation, low cost, portability, and safe human-robotic interaction. The main contributions of this article include: (1) prototyping and evaluation of a hand exoskeleton with a rigid-soft combined multi-segment mechanism driven by one layer of a steel spring with a sufficient output force capacity; (2) using attention-based BCI control to increase patients’ participation in exoskeleton-assisted hand rehabilitation; and (3) determining the threshold of attention value for our attention-based hand rehabilitation robot control.

Exoskeleton Design

Design Requirements

The target users are stroke survivors during flaccid paralysis period who need continuous passive motion training of their hands. They should also be able to focus their attention on motion rehabilitation training for at least a short period of time. For the purpose of hand rehabilitation, an exoskeleton should have minimal ADL interference and have the ability to generate adequate forces to perform hand flexion and extension with a range of motion that is similar or slightly lower than the motion range of a natural finger.

To achieve minimal ADL interference, the device is to be confined to the back of the finger and the width of the device should not exceed the finger width. Here, the width and height constraints of the exoskeleton on the back of the finger are both 20 mm. Low weight of the rehabilitation systems is a key requirement to allow practical use by a wide stroke population (Nycz et al., 2016). Therefore, the target weight of the exoskeleton should be as light as possible to make the patient feel more comfortable to wear it. The typical weight of other hand exoskeletons is in the range of 0.7 kg–5 kg (CyberGlove Systems Inc., 2016Delph et al., 2013Polygerinos et al., 2015aRehab-Robotics Company Ltd., 2019). In this article, the target weight of the exoskeleton is less than 0.5 kg.

There are 15 joints in the human hand. The thumb joint consists of an interphalangeal joint (IPJ), a metacarpophalangeal joint (MPJ), and a carpometacarpal joint (CMJ). Each of the other four fingers has three joints including a metacarpophalangeal joint (MCPJ), a proximal interphalangeal joint (PIPJ), and a distal interphalangeal joint (DIPJ). The hand exoskeleton must have three bending degrees of freedom (DOF) to exercise the three joints of the finger. For some rehabilitation applications, it is unnecessary for each of the MCPJ, PIPJ, and DIPJ of the human finger to have independent motion as long as the whole range of motion of the finger is covered. Tripod grasping requires the MPJ and IPJ of the thumb to bend around 51° and 27°; MCPJ, PIPJ, and DIPJ of the index finger to bend around 46°, 48°, and 12°; and for the middle finger to bend around 46°, 54°, and 12° (In et al., 2015). For the execution speed of rehabilitation exercises, physiotherapists suggest a lower speed than 20 s for a flexion/extension cycle of a finger joint (Borboni et al., 2016). It has to be stressed that hyperextension of all these joints should always be carefully avoided.

The exerted force to the finger should be able to enable continuous passive motion training. In addition, the output force should help the patient to generate grasping forces required to manipulate objects in ADL. Pinch forces required to complete functional tasks are typically below 20 N (Smaby et al., 2004). Polygerinos et al. (2015b) estimated each robot finger should exert a distal tip force of about 7.3 N to achieve a palmar grasp—namely four fingers against the palm of the hand—to pick up objects less than 1.5 kg. Existing devices can provide a maximum transmission output force between 7 N and 35 N (Kokubun et al., 2013In et al., 2015Polygerinos et al., 2015bBorboni et al., 2016Nycz et al., 2016).

The design should allow some customization to hand size and adaptability to different patient statuses and different stages of rehabilitation.

Rigid-Soft Combined Mechanism

Based on our established design requirements, a hand exoskeleton was designed and constructed (see Figure 1). In our design, each finger was driven by one actuator for finger extension and flexion, resulting in a highly compact device. A multi-segment mechanism with a spring layer was proposed. It has respectable adaptability, thus avoiding joint misalignment problems. A three-dimensional model of a single finger actuator is shown in Figure 1A. This finger actuator contained a linear motor, a steel strap, and a multi-segment mechanism. As shown in Figure 1B, the spring layer bended and slid because of the linear motion input provided by the linear actuator. The structure then became like a circular sector. When the structure was attached to a finger, it supported the finger flexion/extension motion. Five finger actuators were attached to a fabric glove via Velcro straps and five linear motors were attached to a rigid part which was fixed to the forearm by a Velcro strap. Each steel strap was attached to a motor by a small rigid 3D-printed part. It should be noted that the current structure is not applicable to thumb adduction/abduction.

Figure 1. Design of the hand exoskeleton: (A) CAD drawing of the index finger acuator; (B) bending motion generated by the proposed mutli-segment mechanism with a spring layer; (C) segment thicknesses (unit: mm); and (D) overview of the hand exoskeleton prototype.

[…]

 

Continue —>  Frontiers | An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism | Frontiers in Neurorobotics

 

, , , , , ,

Leave a comment

[ARTICLE] Design and Preliminary Feasibility Study of a Soft Robotic Glove for Hand Function Assistance in Stroke Survivors – Full Text

Various robotic exoskeletons have been proposed for hand function assistance during activities of daily living (ADL) of stroke survivors. However, traditional exoskeletons involve the use of complex rigid systems that impede the natural movement of joints, and thus reduce the wearability and cause discomfort to the user. The objective of this paper is to design and evaluate a soft robotic glove that is able to provide hand function assistance using fabric-reinforced soft pneumatic actuators. These actuators are made of silicone rubber which has an elastic modulus similar to human tissues. Thus, they are intrinsically soft and compliant. Upon air pressurization, they are able to support finger range of motion (ROM) and generate the desired actuation of the finger joints. In this work, the soft actuators were characterized in terms of their blocked tip force, normal and frictional grip force outputs. Combining the soft actuators and flexible textile materials, a soft robotic glove was developed for grasping assistance during ADL for stroke survivors. The glove was evaluated on five healthy participants for its assisted ROM and grip strength. Pilot test was performed in two stroke survivors to evaluate the efficacy of the glove in assisting functional grasping activities. Our results demonstrated that the actuators designed in this study could generate desired force output at a low air pressure. The glove had a high kinematic transparency and did not affect the active ROM of the finger joints when it was being worn by the participants. With the assistance of the glove, the participants were able to perform grasping actions with sufficient assisted ROM and grip strength, without any voluntary effort. Additionally, pilot test on stroke survivors demonstrated that the patient’s grasping performance improved with the presence and assistance of the glove. Patient feedback questionnaires also showed high level of patient satisfaction and comfort. In conclusion, this paper has demonstrated the possibility of using soft wearable exoskeletons that are more wearable, lightweight, and suitable to be used on a daily basis for hand function assistance of stroke survivors during activities of daily living.

Introduction

The ability to perform basic activities of daily living (ADL) impacts a person’s quality of life and independence (Katz, 1983Andersen et al., 2004). However, an individual’s independence to perform ADLs is jeopardized due to hand motor impairments, which can be observed in patients with neurological disorders such as stroke. In order to improve hand motor functions in terms of strength and range of motion (ROM) (Kutner et al., 2010), stroke survivors undergo rehabilitation programs comprising repetitive practice of simulated ADL tasks (Michaelsen et al., 2006). Normally, patients undergo rehabilitation exercises in a specialized rehabilitation center under the guidance of physiotherapists or occupational therapists. However, due to increasing patient population, it is foreseen that there will be a shortage of physiotherapists to assist in the rehabilitative process. Thus, there will be comparatively less therapy time, which will eventually lead to a slower recovery process for the patients. Over the past decade, technological developments in robotics have facilitated the rehabilitative process and have shown potential to assist patients in their daily life (Maciejasz et al., 2014). One example of such a device is the hand exoskeleton, which is secured around the hand to guide and assist the movement of the encompassed joints. However, due to the complexity of the hand, designing a hand exoskeleton remains a challenging task.

Traditional hand exoskeletons involve the use of rigid linkage-based mechanisms. In this kind of mechanism, rigid components, such as linear actuators, rotary motors, racks, and pinions as well as rigid linkages are normally involved (Worsnopp et al., 2007Rotella et al., 2009Martinez et al., 2010). To assist hand movements that have high degrees of freedom (DOFs), traditional exoskeletons can be incorporated with a substantial number of actuators to achieve the requirement. However, this means that their application is limited due to the increasing bulkiness for higher DOFs. Therefore, these devices are normally restricted in clinical settings and not suitable for performing home therapy. Additionally, their rigidity, weight and constraint on the non-actuated DOFs of the joints pose complications. As a result, the level of comfort and safety of patients is reduced. In view of this, there is an apparent need for the development of exoskeletons that may be used in both clinical and home settings. A lightweight and wearable exoskeleton may allow patients to bring back home to continue daily therapy or to serve as an assistive device for the ADLs.

The development of wearable robotic exoskeletons serves to provide an alternative approach toward addressing this need. Instead of using rigid linkage as an interface between the hand and the actuators, wearable exoskeletons typically utilize flexible materials such as fabric (Sasaki et al., 2004Yap et al., 2016a) and polymer (Kang et al., 2016), driven by compliant actuators such as cables (Sangwook et al., 2014Xiloyannis et al., 2016) and soft inflatable actuators (Polygerinos et al., 2015dYap et al., 2016c). Therefore, they are more compliant and lightweight compared to the rigid linkage-based mechanism. Cable-driven based exoskeletons involve the use of cables that are connected to actuators in the form of electrical motors situated away from the hand (Nilsson et al., 2012Ying and Agrawal, 2012Sangwook et al., 2014Varalta et al., 2014). By providing actuations on both dorsal and palmar sides of the hand, bi-directional cable-driven movements are possible (Kang et al., 2016). These cables mimic the capability of the tendons of the human hand and they are able to transmit the required pulling force to induce finger flexion and extension. However, the friction of the cable, derailment of the tendon, and inaccurate routing of the cable due to different hand dimensions can affect the efficiency of force transmission in the system.

On the other hand, examples of the soft inflatable actuators are McKibben type muscles (Feifei et al., 2006Tadano et al., 2010), sheet-like rubber muscles (Sasaki et al., 2004Kadowaki et al., 2011), and soft elastomeric actuators (Polygerinos et al., 2015b,cYap et al., 2015); amongst which, soft elastomeric actuators have drawn increasing research interest due to their high compliance (Martinez et al., 2013). This approach typically embeds pneumatic chamber networks in elastomeric constructs to achieve different desired motions with pressurized air or water (Martinez et al., 2012). Soft elastomeric actuators are highly customizable. They are able to achieve multiple DOFs and complex motions with a single input, such as fluid pressurization. The design of a wearable hand exoskeleton that utilizes soft elastomeric actuators is usually simple and does not require precise routing for actuation, compared to the cable-driven mechanism. Thus, the design reduces the possibility of misalignment and the setup time. These properties allow the development of hand exoskeletons that are more compliant and wearable, with the ability to provide safe human-robot interaction. Additionally, several studies have demonstrated that compactness and ease of use of an assistive device critically affect its user acceptance (Scherer et al., 20052007). Thus, these exoskeletons provide a greater chance of user acceptance.

Table 1 summarizes the-state-of-art of soft robotic assistive glove driven by inflatable actuators. Several pioneer studies on inflatable assistive glove have been conducted by Sasaki et al. (2004)Kadowaki et al. (2011) and Polygerinos et al. (2015a,b,c). Sasaki et al. have developed a pneumatically actuated power assist glove that utilizes sheet-like curved rubber muscle for hand grasping applications. Polygerinos et al. have designed a hydraulically actuated grip glove that utilizes fiber-reinforced elastomeric actuators that can be mechanically programmed to generate complex motion paths similar to the kinematics of the human finger and thumb. Fiber reinforcement has been proved to be an effective method to constrain the undesired radial expansion of the actuators that does not contribute to effective motion during pressurization. However, this method limits the bending capability of the actuators (Figure S1); as a result, higher pressure is needed to achieve desired bending.

Table 1. Hand assistive exoskeletons driven by inflatable actuators.

This paper presents the design and preliminary feasibility study of a soft robotic glove that utilizes fabric-reinforced soft pneumatic actuators. The intended use of the device is to support the functional tasks during ADLs, such as grasping, for stroke survivors. The objectives of this study were to characterize the soft actuators in terms of their force output and to evaluate the performance of the glove with healthy participants and stroke survivors. The glove was evaluated on five healthy participants in order to determine the ROM of individual finger joints and grip strength achieved with the assistance of the glove. Pilot testing with two stroke survivors was conducted to evaluate the feasibility of the glove in providing grasping assistance for ADL tasks. We hypothesized that with the assistance of the glove, the grasping performance of stroke patients improved.

Specific contributions of this work are listed as follows:

(a) Presented fabric-reinforcement as an alternative method to reinforce soft actuators, which enhanced the bending capability and reduced the required operating pressure of the actuators,

(b) Utilized the inherence compliance of soft actuators and allowed the actuators to achieve multiple motions to support ROM of the human fingers,

(c) Integrated elastic fabric with soft actuators to enhance the extension force for finger extension,

(d) Designed and characterized a soft robotic glove using fabric-reinforced soft actuators with the combination of textile materials, and

(e) Conducted pilot tests with stroke survivors to evaluate the feasibility of the glove in providing functional assistance for ADL tasks.

Design Requirements and Rationale

The design requirements of the glove presented in this paper are similar to those presented by Polygerinos et al. (2015a,b,c) in terms of design considerations, force requirements, and control requirements. For design considerations, weight is the most important design criterion when designing a hand exoskeleton. Previous studies have identified the threshold for acceptable weight of device on the hand, which is in the range of 400–500 g (Aubin et al., 2013Gasser and Goldfarb, 2015). Cable-driven, hydraulic, and pneumatic driven mechanisms are found to be suitable options to meet the criteria. To develop a fully portable system for practical use in home setting, reduction in the weight of the glove as well as the control system is required. The total weight of the control system should not exceed 3 kg (Polygerinos et al., 2015a,b,c). In this work, the criteria for the weight of the glove and control system are defined as: (a) the weight of the glove should be <200 g, and (b) the weight of the control system should be <1.5 kg.

Considering the weight requirement, hydraulic systems are not ideal for this application, as the requirement of a water reservoir for hydraulic control systems and actuation of the actuators with pressurized water will add extra weight to the hand. The second consideration is that the hand exoskeleton should allow fast setup time. Therefore, it is preferable for the hand exoskeleton to fit the hand anatomy rapidly without precise joint alignment. Compared to cable-driven mechanisms, soft pneumatic actuators are found to be more suitable as they allow rapid customization to different finger length. Additionally, they do not require precise joint alignment and cable routing for actuation as the attachment of the soft pneumatic actuators on the glove is usually simple. Therefore, in this work, pneumatic mechanisms were selected. Using pneumatic mechanism, Connelly et al. and Thielbar et al. have developed a pneumatically actuated glove, PneuGlove that is able to provide active extension assistance to each finger while allowing the wearer to flex the finger voluntarily (Connelly et al., 2010Thielbar et al., 2014). The device consists of five air bladders on the palmar side of the glove. Inflation of the air bladders due to air pressurization created an extension force that extends the fingers. However, due to the placement of the air bladders on the palmar side, grasping activities such as palmar and pincer grasps were more difficult. Additionally, this device is limited to stroke survivors who are able to flex their fingers voluntarily.

In this work, the soft robotic glove is designed to provide functional grasping assistance for stroke survivors with muscle weakness and impairments in grasping by promoting finger flexion. While the stroke survivors still preserve the ability to modulate grip force within their limited force range, the grip release (i.e., hand opening) is normally prolonged (Lindberg et al., 2012). Therefore, the glove should assist with grip release by allowing passive finger extension via reinforced elastic components, similar to Saeboflex (Farrell et al., 2007) and HandSOME (Brokaw et al., 2011). The elastic components of these devices pull the fingers to the open hand state due to increased tension during finger flexion. Additionally, the glove should generate the grasping force required to manipulate and counteract the weight of the objects of daily living, which are typically below 1.07 kg (Smaby et al., 2004). Additionally, the actuators in the glove should be controlled individually in order to achieve different grasping configurations required in simulated ADL tasks, such as palmar grasp, pincer grasp, and tripod pinch. For the speed of actuation, the glove should reach full grasping motion in <4 s during simulated ADL tasks and rehabilitation training.

For the actuators, we have recently developed a new type of soft fabric-reinforced pneumatic actuator with a corrugated top fabric layer (Yap et al., 2016a) that could minimize the excessive budging and provide better bending capability compared to fiber-reinforced soft actuators developed in previous studies (Polygerinos et al., 2015c,d). This corrugated top fabric layer allows a small initial radial expansion to initiate bending and then constrains further undesired radial expansion (Figure 1). The detailed comparison of the fiber-reinforced actuators and fabric-reinforced actuators can be found in the Supplementary Material.

 

 

Figure 1. (A) A fabric-reinforced soft actuators with a corrugated fabric layer and an elastic fabric later [Actuator thickness, T= 12 mm, and length, L = 160 mm (Thumb), 170 mm (Little Finger), 180 mm (Index & Ring Fingers), 185 mm (Middle Finger)]. (B) Upon air pressurization, the corrugated fabric layer unfolds and expands due to the inflation of the embedded pneumatic chamber. Radial budging is constrained when the corrugated fabric layer unfolds fully. The elastic fabric elongates during air pressurization and stores elastic energy. The actuator achieves bending and extending motions at the same time. (C) A bending motion is preferred at the finger joints (II, IV, VI). An extending motion is preferred over the bending motion at the finger segments (I, III, V) and the opisthenar (VII).

Continue —>  Frontiers | Design and Preliminary Feasibility Study of a Soft Robotic Glove for Hand Function Assistance in Stroke Survivors | Neuroscience

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

Leave a comment

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

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

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

These different requirements result on diverse force transmission architectures:

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

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

Research background and objectives

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

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

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

Hand exoskeleton

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

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

 

figure

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

 

figure

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

 

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

, , , , , , , ,

Leave a comment

[Abstract] A Dual-cable Hand Exoskeleton System for Virtual Reality

Abstract

In this paper, a hand exoskeleton system for virtual reality is proposed. As a virtual reality interface for the hand, a wearable system should be able to measure the finger joint angles and apply force feedback to the fingers at the same time with a simple and light structure. In the proposed system, two different cable mechanisms are applied to achieve such requirements; three finger joint angles in the direction of the flexion/extension (F/E) motion are measured by a tendon-inspired cable mechanism and another cable is used for force feedback to the finger for one degree of freedom (DOF) actuation per finger. As two different types of cables are used, the system is termed a dual-cable hand exoskeleton system. Using the measured finger joint angles and motor current, the cable-driven actuation system applies the desired force to the fingers. That is, when the desired force is zero, the motor position is controlled to follow the finger posture while maintaining the appropriate cable slack; when the desired force needs to be applied, the motor current is controlled to generate the desired force. To achieve a smooth transition between the two control strategies, the control inputs were linearly integrated; and the desired motor position was generated to prevent a sudden motor rotation. A prototype of the proposed system was manufactured with a weight of 320g, a volume of 13 × 23 × 8cm3, maximum force up to 5 N. The proposed control algorithms were verified by experiments with virtual reality applications.

 

via A Dual-cable Hand Exoskeleton System for Virtual Reality

, , , , , , ,

Leave a comment

[Conference paper] FEX a Fingers Extending eXoskeleton for Rehabilitation and Regaining Mobility – Abstract+References

 

Abstract

This paper presents the design process of an exoskeleton for executing human fingers’ extension movement for the rehabilitation procedures and as an active orthosis purposes. The Fingers Extending eXoskeleton (FEX) is a serial, under-actuated mechanism capable of executing fingers’ extension. The proposed solution is easily adaptable to any finger length or position of the joints. FEX is based on the state-of-art FingerSpine serial system. Straightening force is transmitted from a DC motor to the exoskeleton structures with use of pulled tendons. In trial tests the device showed good usability and functionality. The final prototype is a result of almost half a year of the development process described in this paper.

References

  1. 1.
    Sale P, Lombardi V, Franceschini M (2012) Hand robotics rehabilitation: feasibility and preliminary results of a robotic treatment in patients with hemiparesis. Stroke Res Treat 2012:820931 Epub 26 December 2012Google Scholar
  2. 2.
    Franceschini M et al (2012) Clinical relevance of action observation in upper-limb stroke rehabilitation: a possible role in recovery of functional dexterity: a randomized clinical trial. Neurorehabil Neural Repair 26(5):456–462CrossRefGoogle Scholar
  3. 3.
    Berger RA, Weiss A-PC (2003) Hand surgery. Lippincott Williams & Wilkins, BaltimoreGoogle Scholar
  4. 4.
    Buryanov A, Kotiuk V (2010) Proportions of hand segments. Int J Morphol 28(3):755–758CrossRefGoogle Scholar
  5. 5.
    Christopher JH (1995) Force-reflecting anthropomorphic hand masters. Armstrong laboratory internal report, crew systems directorate biodynamics and biocommunications division human systems center, Air force materiel command, July 1995Google Scholar
  6. 6.
    Garrett JW (1970) Anthropometry of the hands of female air force flight personnel. Technical report AMRL-TR-69-26, USAF aerospace medical research laboratory, Wright-Patterson AFB OHGoogle Scholar
  7. 7.
  8. 8.
    Sale P, Bovolenta F, Agosti M, Clerici P, Franceschini M (2014) Short-term and long-term outcomes of serial robotic training for improving upper limb function in chronic stroke. Int J Rehabil Res 37(1):67–73CrossRefGoogle Scholar
  9. 9.
    An KN, Askew LJ, Chao EY (1986) Biomechanics and functional assessment of upper extremities, trends in ergonomics/human factors III. In: Karwowski W (ed) Elsevier Science Publishers BV, North-Holland, pp 573–580Google Scholar
  10. 10.
    Darling WG, Cole KJ (1990) Muscle activation patterns and kinetics of human index finger movements. J Neurophysiol 63(5):1098–1108Google Scholar
  11. 11.
    Yamaura H, Matsushita K, Kato R, Yokoi H (2009) Development of hand rehabilitation system for paralysis patient – universal design using wire-driven mechanism. In: 31st annual international conference of the IEEE EMBS, Minneapolis, Minnesota, 2–6 September 2009Google Scholar
  12. 12.
    Fontana M, Bergamasco M, Salsedo F (2009) Mechanical design and experimental characterization of a novel hand exoskeleton. In: Proceedings of the AIMETA 2009, Ancona, Italy, 14–17 September 2009Google Scholar
  13. 13.
    Wege A, Hommel G (2005) Development and control of a hand exoskeleton for rehabilitation of hand injuries. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Berlin, Germany, pp 3461–3466Google Scholar
  14. 14.
    Mulas M, Folgheraiter M, Gini G: An EMG-controlled exoskeleton for hand rehabilitation. In: Proceedings of the IEEE 9th international conference on rehabilitation robotics, Chicago, 28 June–1 July 2005, pp 371–374Google Scholar
  15. 15.
  16. 16.
    Kawasaki H, Ito S, Ishigure Y, Nishimoto Y, Aoki T, Mouri T, Sakaeda H, Abe M (2007) Development of a hand motion assist robot for rehabilitation therapy by patient self-motion control. In: Proceedings of the IEEE 10th international conference on rehabilitation robotics, Noordwijk, The Netherlands, 12–15 June 2007, pp 234–240Google Scholar
  17. 17.
    Hirose S (1985) Connected differential mechanism and its applications. In: Proceedings of 1985 international conference on advanced robotics, Tokyo, Japan, September, pp 319–325Google Scholar
  18. 18.
    Hirose S (1993) Biologically inspired robotics. Oxford University Press, Oxford Translated by Cave P, Goulden CGoogle Scholar
  19. 19.
    Montambault S, Gosselin CM (2001) Analysis of under actuated mechanical grippers. ASME J Mech Des 123(3):367–374CrossRefGoogle Scholar
  20. 20.
    Ryan RM (1982) Control and information in the intrapersonal sphere: an extension of cognitive evaluation theory. J Pers Soc Psychol 43(3):450–461CrossRefGoogle Scholar
  21. 21.
    McAuley E, Duncan T, Tammen V (1989) Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: a confirmatory factor analysis. Res Q Exerc Sport 60(1):48–58CrossRefGoogle Scholar
  22. 22.
    Brooke J (1996) SUS: a quick and dirty usability scale. In: Jordan PW, Weerdmeester B, Thomas A, Mclelland IL (eds) Usability evaluation in industry. Taylor & Francis, LondonGoogle Scholar
  23. 23.
    Bangor A, Kortum P, Miller J (2009) Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 4(3):114–123Google Scholar
  24. 24.
    Bangor A, Kortum P, Miller J (2008) An empirical evaluation of the system usability scale. Int J Hum Comput Interact 24(6):574–594CrossRefGoogle Scholar

Source: FEX a Fingers Extending eXoskeleton for Rehabilitation and Regaining Mobility | SpringerLink

, , , , , , , , ,

Leave a comment

%d bloggers like this: