Posts Tagged Hand exoskeleton

[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.

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

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).

Advertisements

, , , ,

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

[ARTICLE] Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient – Full Text

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user’s motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the “gun” sign/opening. A 52-year-old woman, 8 months after stroke, made 20×2-hour visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4×10-minute robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 kg to 2.7 kg, her pinch force increased from 1.5 kg to 2.5 kg, her score of Box & Block test increased from 3 to 7, her score of Fugl-Meyer (Part C) increased from 0 to 7, her hand function increased from Stage 1 to Stage 2 in Chedoke-McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

Introduction

Robot-assisted upper limb training is considered to be more efficient (1) and economic (2) than conventional therapy in neurorehabilitation. Controlling the robot with the user’s own electromyography (EMG) signals connects the user’s intended motion and his actual movements. It can therefore enhance therapeutic effects and promote motor learning (35). Various EMG-driven robots and exoskeletons have been developed for neurorehabilitation (68), primarily based on one-to-one mapping, which typically maps one channel of EMG signal to a corresponding single degree-of-freedom (DOF) or variable such as speed and torque using a conventional “on-off” or proportional strategy. Robots based on such control strategy work well on training joints with only a few DOFs such as elbow and wrist. However, a human hand has up to 27 DOFs (9) and is controlled by complex temporal and spatial coordination of multiple muscles. It is therefore not feasible to regain hand dexterity through conventional control strategies. Myoelectric pattern-recognition techniques have been developed to extract motion intentions from EMG signals (10, 11). The extracted intentions can then be used to control a multiple-DOF robot such as a prosthesis (12). Previous studies have also shown that motion intentions can still be extracted after neurological impairment (1315). We therefore developed an intent-driven hand training system. The system employs an exoskeleton hand, which is controlled by myoelectric pattern recognition. As soon as the user’s intention is detected (usually within 250 ms), the system is able to assist to accomplish the intended motions (16).

Case Report

Subject

A 52-year-old woman participated in this robotic hand-assisted training 8 months after stroke. She was right-handed before stroke and had hemiplegia on her right side after her stroke. She was able to walk independently with an ankle foot orthosis but had difficulties in moving her right arm. Her fingers were flexed naturally. She was unable to move any of the fingers on her right hand, but EMG signals were able to be recorded from her forearm. Her Fugl–Meyer score (Part A–D, max 66) was 16, with a 0 in Part C (Hand, max 14). She had no pain when her whole hand was passively opened or closed. She did not receive any other hand or upper limb therapies while participating in this study. During her visits, she was able to understand and follow all the instructions.

Exoskeleton Hand

The exoskeleton hand, Hand of Hope (Rehab-Robotics, Hong Kong), was used in this study to help the subject move her hand (Figure 1). The exoskeleton hand has five individual fingers. Each finger is actuated by a linear actuator that can pull and push linearly. The mechanical design of the fingers converts these linear movements into the rotations of a virtual metacarpophalangeal (MCP) joint and a virtual proximal interphalangeal (PIP) joint. Both joints rotate together to help the hand perform closing and opening movements (7). The motion range is 55° and 65° for MCP and PIP joints, respectively. The subject’s palm and five fingers are fixed to the exoskeleton hand with Velcro belts. Each finger can be bent or straightened individually by the exoskeleton hand. The exoskeleton hand stands on a brace, which also supports the subject’s forearm, so that the subject can be totally relaxed when attached to the exoskeleton. The exoskeleton hand used in this study can perform six different motion patterns, including hand closing (HC); hand opening (HO); thumb, index, and middle fingers closing (TIMC or tripod pinch); thumb, index, and middle fingers opening; middle, ring, and little fingers closing (MRLC or the “gun” sign); and middle, ring, and little fingers opening. The exoskeleton hand can perform HC, TIMC, or MRLC when it is open. However, after performing any one from these three patterns, it can only return to the original open status (e.g., there is no direct way from the “tripod pinch” to the “gun” sign).

Figure 1. Training with the exoskeleton hand driven by myoelectric pattern recognition.

Continue —> Frontiers | Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient | Stroke

, , , , , , , ,

Leave a comment

[ARTICLE] An Original Classification of Rehabilitation Hand Exoskeletons – Full Text PDF

Abstract

The hand is an organ of grasping as well as sensation, communication, and fine dexterity. Since the 80’s, many researchers have been attempting to develop robotic devices aiming at replicating the functions of the human hand in the fields of industrial robotics, tele-manipulation, humanoid robotics, and upper limb prosthetics.

A special kind of robotic hand is the hand exoskeleton, that is directly attached to the human hand with the aim of providing assistance in motion/power generation. Hand exoskeletons are increasingly widespread in robot-based rehabilitation of patients suffering from different pathologies (in particular neurological diseases).

This paper reviews the state-of-the-art of hand exoskeletons developed for rehabilitation purposes and proposes a new systematic classification according to three key points related to the kinematic architecture: (i) mobility of a single finger exoskeleton, (ii) number of physical connections between the exoskeleton and the human finger phalanges, and (iii) way of integration of the exoskeleton mechanism with the human parts.

The discussion based upon the classification can be helpful to understand the reasons of adopting certain solutions for specific applications and the advantages and drawbacks of different designs, based on the work already done by other researchers.

The final purpose of the proposed classification is then to provide guidelines useful for the design of new hand exoskeletons on the basis of a systematic analysis. As an example, the solution designed, manufactured and clinically tested by the authors is reported.

Full Text PDF

, , , , , , , , ,

Leave a comment

[ARTICLE] Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation – Full Text PDF/HTML

Abstract

Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises.

In this paper, we present a novel master–slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator’s hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task.

Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers’ hands movements.

1. Introduction

Traditional rehabilitation is performed in a one-to-one fashion, namely one therapist (or sometimes several) working with one patient, leading to high personnel and management costs, especially for demanding patients such as those with brain or post surgery injuries. Due to the high hospitalization costs, all these patients are leaving clinics and returning to their homes sooner than in the past [1], when their rehabilitative program is not yet finished. These patients can greatly benefit from a telerehabilitation equipment, which is able to provide remote assistance and relief without the burden of going to the clinic on a daily basis. On the other hand, therapists can surely benefit from non-invasive systems capable of acquiring information about their movements which are then sent to the patient (or even to many patients), possibly in real-time to allow a direct control; modern vision-based techniques offer interesting sparks in such way. The possibility to provide high quality rehabilitation programs regardless of patients physical location and leveraging on vision is thus certainly attractive.

Continue —> Sensors | Free Full-Text | Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation | HTML

Sensors 16 00208 g002 1024

Figure 2. HX while holding the sensorized object in a pinch (a) and lateral (b) grasping exercise. The DoMs of the HX device are: (1) the flexion/extension of the index MCP; (2) of the index P-DIP (under-actuated); (3) of the thumb MCP and IP (under-actuated) and (4) the CMC opposition. Other Degrees-of-Freedom (DoF), like thumb intra/extra rotation and the index abduction/adduction, are passive [29]. The HX is used to grasp the sensorized object, whose squeezable soft-pads provide force information on the basis of a optoelectronic deformation transduction [34].

, , , , , ,

Leave a comment

[Abstract] A novel motion-coupling design for a jointless tendon-driven finger exoskeleton for rehabilitation

Abstract

We have designed a new jointless tendon-driven exoskeleton plan for the human hand that provides a correct and stable motion sequence while keeping the structure lightweight, compact and portable. Before the development, anatomy analysis and a kinematics study of the human finger were performed, and bending angle relationships among the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints were analyzed. Detailed implementation is discussed, including the basic theory of the joint motion coupling method, related formula derivations and mechanical design of an experimental device. An experimental setup was built, and series of experiments was conducted to examine and evaluate the developed joint motion coupling plan.The results indicated that the new plan worked correctly as desired, that an incorrect finger motion sequence did not occur and that the new coupled tendon driven plan can drive finger bending as naturally as a human. The compactness and light weight of the entire structure of the device means that its parts can be arranged for a hand glove or fingerstall more easily than most bar-linkage exoskeleton structures.

 

Source: A novel motion-coupling design for a jointless tendon-driven finger exoskeleton for rehabilitation

, , , , , , ,

Leave a comment

[ARTICLE] An index finger exoskeleton with series elastic actuation for rehabilitation: Design, control and performance characterization

Abstract

Rehabilitation of the hands is critical for the restoration of independence in activities of daily living for individuals exhibiting disabilities of the upper extremities. There is initial evidence that robotic devices with force-control-based strategies can help in effective rehabilitation of human limbs. However, to the best of our knowledge, none of the existing hand exoskeletons allow for accurate force or torque control.

In this work, we present a novel index finger exoskeleton with Bowden-cable-based series elastic actuation allowing for bidirectional torque control of the device with high backdrivability and low reflected inertia. We present exoskeleton and finger joint torque controllers along with an optimization-based offline parameter estimator. Finally, we carry out tests with the developed prototype to characterize its kinematics, dynamics, and controller performance.

Results show that the device preserves the characteristics of natural motion of finger and can be controlled to achieve both exoskeleton and finger joint torque control. Finally, dynamic transparency tests show that the device can be controlled to offer minimal resistance to finger motion. Beyond the present application of the device as a hand rehabilitation exoskeleton, it has the potential to be used as a haptic device for teleoperation.

Source: An index finger exoskeleton with series elastic actuation for rehabilitation: Design, control and performance characterization

, , , , ,

Leave a comment

[ARTICLE] Experiments and kinematics analysis of a hand rehabilitation exoskeleton with circuitous joints – OPEN ACCESS

Abstract

Aiming at the hand rehabilitation of stroke patients, a wearable hand exoskeleton with circuitous joint is proposed. The circuitous joint adopts the symmetric pinion and rack mechanism (SPRM) with the parallel mechanism. The exoskeleton finger is a serial mechanism composed of three closed-chain SPRM joints in series. The kinematic equations of the open chain of the finger and the closed chains of the SPRM joints were built to analyze the kinematics of the hand rehabilitation exoskeleton.

The experimental setup of the hand rehabilitation exoskeleton was built and the continuous passive motion (CPM) rehabilitation experiment and the test of human-robot interaction force measurement were conducted. Experiment results show that the mechanical design of the hand rehabilitation robot is reasonable and that the kinematic analysis is correct, thus the exoskeleton can be used for the hand rehabilitation of stroke patients.

Full Text PDF

 

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

Leave a comment

[ARTICLE] Exo-Glove: A Soft Wearable Robot for the Hand with a Soft Tendon Routing System – Full Text PDF

Abstract

…This article describes a soft wearable hand robot called the Exo-Glove that uses a soft tendon routing system and an underactuation adaptive mechanism. The proposed system can be used to develop other types of soft wearable robots. The glove part of the system is compact and weighs 194 g.

Results conducted using a healthy subject showed sufficient performance for the execution of daily life activities, namely, a pinch force of 20 N, a wrap grasp force of 40 N, and a maximum grasped object size of 76 mm. Use of an underactuation mechanism enabled the grasping of objects of various shapes without active control.

A subject suffering from paralysis of the hands due to spinal cord injury was able to use the glove to grasp objects of various shapes…

[PDF] Exo-Glove: A Soft Wearable Robot for the Hand with a Soft Tendon Routing System

, , , , , , , , , , ,

Leave a comment

[ARTICLE] Design and development of a hand exoskeleton for rehabilitation following stroke – Full Text PDF

Abstract

In Australia, a major cause of disability is the stroke and it is the second highest cause of death after coronary heart disease. Studies have predicted that form 2008 to 2017 more than 0.5 million people is likely to suffer from stroke in Australia. In addition, after stroke 88 % of the patients suffer from disability and stays at home.

In this paper, a post stroke therapeutic device has been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. Out of twenty-one degrees of freedom of hand fingers, the prototype of the hand exoskeleton allowed fifteen degrees of freedom. The device is designed to be portable so that the user can engage in other activities while using the device. A prototype of the device is fabricated to provide complete flexion and extension motion of individual fingers of the left hand (impaired hand) based on the movements of the right hand (healthy hand) fingers. In addition, testing of the device on a healthy subject was conducted to validate if the design met the requirements.

Full Text PDF –> Design and development of a hand exoskeleton for rehabilitation  following stroke 

, , , , , , ,

Leave a comment

%d bloggers like this: