Posts Tagged finger

[Patent Application] FINGER JOINT REHABILITATION DEVICE – Full Text

Abstract

A finger joint rehabilitation device comprising: at least one of an index finger joint rehabilitation exercise aid part, a middle finger joint rehabilitation exercise aid part, a ring finger joint rehabilitation exercise aid part, a little finger joint rehabilitation exercise aid part and a thumb joint rehabilitation exercise aid part; at least one corrugated tube; one protective brace fixed on a wrist and a palm; and the index finger joint rehabilitation exercise aid part, the middle finger joint rehabilitation exercise aid part, the ring finger joint rehabilitation exercise aid part, the little finger joint rehabilitation exercise aid part and the thumb joint rehabilitation exercise aid part are all provided with the corrugated tube and are all fixed on the protective brace.

Continue —->  FINGER JOINT REHABILITATION DEVICE – BEIJING HENGTONG XINJIA TECHNOLOGY DEVELOPMENT CO, LTD.

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[WEB SITE] DigiTrainer – Video

DigiTrainer is a tool for reducing the muscle tone and increasing mobility in the fingers

DIGITRAINER

SIMPLY EFFECTIVE HAND THERAPY

“The principle of action is unique: It is based on the combination of vibration, movement and pressure. The vibration promotes perception and blood circulation to the hand and fingers. ”

DigiTrainer (formerly RehaDigit) can reduce the muscle tone and increase mobility in the fingers of the hand.
Following a stroke, brain injury or spinal cord injury, for example, the muscles and soft-tissues of the hand can become tight and the sensory pathways disrupted.
In order to recover lost tactile sense and to trigger new movement capabilities, intensive rehabilitation is needed and this should start as soon as possible following the injury.
For example, with a cervical level spinal cord injury it is important to avoid complications by early positioning, stretches and oedema management. The hand is perhaps the most important resource after the brain in these cases so the hands must be kept supple if we are to have a chance of developing functional activities. The DigiTrainer makes intensive rehab possible.
DigiTrainer provides both motor and sensory rehabilitation in a simple and effective manner. Through a series of finger-rolls the patient’s fingers are alternately bent and stretched (flexion/extension of the finger joints). The specially designed motor induces a slight vibration into the hand and this supports the relaxation of the finger muscles.

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Vibration preserves sensory pathways

Vibration preserves sensory pathways

DigiTrainer delivers the following functions

  • works for the left and the right hand

  • adjustable rotation velocity

  • adjustable vibration frequency

  • continues or periodic crescendo and decrescendo vibrations

  • ergonomic hand rest (height adjustable)

  • usage via touch screen

  • therapy time: 5-30 min

  • offer price £2,660 ex VAT and shipping

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INDICATIONS FOR USE

DigiTrainer can be used for the following indications:

  • passive bend and stretch movement of the II-V fingers in the rehabilitation of patients with hemi- and tetraparesis from moderate to strong paresis of the upper extremity

  • for example, after stroke, paraplegia, traumatic brain injury, M. Parkinson or joint injuries

  • for patients without distal activity of the wrist and finger flexors

  • incomplete and complete motoric paraplegia after spinal cord injury

  • for patients with spasticity in arms, low blood circulation and impaired hand mobility

  • for patients with functional loss after injury or surgery

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WHAT IT DOES

DigiTrainer is a CE marked Class II medical device. The items included with the product are 1 DigiTrainer, 2 adapter plates for hand rest (25mm and 20°), 1 power supply and appropriate cable and 1 user manual

Check out the video below to see DigiTrainer in action. The unit accomodates left or right hands of various sizes and allows easy programming via a touch screen interface. The therapist can control the specific nature and speed of the movement as the DigiTrainer stretches and massages the fingers. Integrated vibration relaxes tight fingers in a safe and effective way. DigiTrainer has a unique operating principle – most devices focus on movement whereas DigiTrainer also targets the sensorimotor system. Studies have confirmed the effectiveness of the device.

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CONTRAINDICATIONS

The DigiTrainer is generally a safe product but we recommend initial supervision and guidance is obtained from knowledgeable person

Contraindications for DigiTrainer include patients with:

  • fully developed shoulder-arm syndrome

  • acute arthritis in finger joints, thumb joints and/or wrist

  • severe contractures of the finger joints, thumb joints and/or wrist

  • acute disorders requiring special treatment of fingers or hand (e.g. tendinitis)

  • massively swollen hand

  • allergic exanthema of hand

  • fixed fingers.

Related Research

Stefan Hesse, H Kuhlmann, J Wilk, C Tomelleri and Stephen GB Kirker (2008) “A new electromechanical trainer for sensorimotor rehabilitation of paralysed fingers: A case series in chronic and acute stroke patients”
Journal of NeuroEngineering and Rehabilitation20085:21
DOI: 10.1186/1743-0003-5-21
https://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-5-21

R. Buschfort, J. Brocke, A. Heß, C. Werner, A. Waldner, and Stefan Hesse,
”Arm Studio to intensify upper limb rehabilitation after stroke: Concept, acceptance, utilisation and preliminary clinical results”
J Rehabil Med 2010; 42: 310–314

Stefan Hesse, Anke Heß, Cordula Werner, Nadine Kabbert, Rüdiger Buschfort
“Effect on arm function and cost of robot-assisted group therapy in subacute patients with stroke and a moderately to severely affected arm: a randomized controlled trial”
Clinical Rehabilitation 2014, Vol. 28(7) 637–647
DOI: 10.1177/0269215513516967

A. Waldner, C. Werner, S. Hesse
“Robot assisted therapy in neurorehabilitation”
EUR MED PHYS 2008;44(Suppl. 1 to No. 3)

Visit site —->  DigiTrainer

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[WEB SITE] Physical Therapist From Vive Health Demonstrates 8 Easy Hand & Finger Exercises Using Therapy Putty on YouTube – Video

Losing grip strength is a common byproduct of arthritis and a number of other health issues. Following a fast and simple set of exercises using Therapy Putty can help. Vive Health demonstrates them in a free video that is winning wide praise.

Naples, FL (PRUnderground) February 21st, 2020

Arthritis, age, and many other factors can lead to weakened grip and hand strength. Of course, this has a negative lifestyle impact that can’t be understated. Always on top of providing easy-to-follow and functional wellness tips and products Vive Health recently celebrated the release of a compelling new YouTube video addressing this serious concern, “8 Easy Hand & Finger Exercises Using Therapy Putty” with Karen Miller, PTA doing the instruction and demonstration. Only requiring a few minutes a day, and with Therapy Putty being quite affordable, this is a video that those who are going through hand and finger pain or diminishing coordination should not miss.

“For someone who has arthritis this could be the best four minutes they could ever spend watching our Therapy Putty video,” remarked a spokesperson from Vive Health. “Karen is well spoken and knowledgeable and does an amazing job showing these simple hand and finger exercises.  These exercises can help improve dexterity and fine motor skills, while also reducing or removing stress. They are great for physical therapy, occupational therapy and rehabbing a hand or hands after surgery.”

Vive Health offers premium quality Therapy Putty in a number of different strengths so that they can be used in a progressive way to help regain or build hand and finger strength and coordination. Free shipping is even available for orders over $39 in the United States.

Check out the video here.  Be sure to visit Vive Health’s website at https://www.vivehealth.com for more information.

About Vive Health

We are committed to helping you live better. Whether you are recovering from an injury, managing your health, or caring for a loved one, our mission is to provide you with what you need to feel confident and in control.

We strive to separate from the pack and become your trustworthy and affordable online medical equipment store; providing products that you’d be proud to use yourself, give to your loved ones or patients

 

via Physical Therapist From Vive Health Demonstrates 8 Easy Hand & Finger Exercises Using Therapy Putty on YouTube | PRUnderground

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[Abstract] Wearable Hand Exoskeleton Systems for Virtual Reality and Rehabilitation

Abstract

The aim is to overcome the limitations of conventional systems in terms of both wearability and portability. As the hand receives diverse physical information and manipulates different type of objects, conventional systems contain many sensors and actuators, and are both large and heavy. Thus, hand exoskeleton systems exhibiting high wearability and portability while measuring finger motions and delivering forces would be highly valuable. For VR hand exoskeleton systems, a wearable hand exoskeleton system with force-controllable actuator modules was developed to ensure free finger motion and force mode control. The linkage structure ensures motion with three degrees of freedom (DOF) and provides a large fingertip workspace; the finger postures assumed when interacting with objects are appropriate. A series elastic actuator (SEA) with an actuator and an elastic element was used to fabricate compact actuator modules. Actuator friction was eliminated using a friction compensation algorithm. A proportional differential (PD) controller, optimized by a linear quadratic (LQ) method featuring a disturbance observer (DOB), was used to ensure accurate force mode control even during motion. The force control performance of the actuator module was verified in force generation experiments including stationary and arbitrary end-effector motions. The forces applied to the fingertips, which are the principal parts of the hand that interact with objects, were kinematically analyzed via both simulations and experiments. To overcome the weak point of previous system, a wearable hand exoskeleton system featuring finger motion measurement and force feedback was developed and evaluated in terms of user experience (UX). The finger structures for the thumb, index, and middle fingers, which play important roles when grasping objects, satisfy full range of motion (ROM). The system estimates all joint angles of these three digits using a dedicated algorithm; measurement accuracy was experimentally evaluated to verify system performance. The UX performance was evaluated by 15 undergraduate students who completed questionnaires assessing usability and utilitarian value following trials conducted in the laboratory. All subjects were highly satisfied with both usability and the utilitarian nature of the system, not only because control and feedback were intuitive but also because performance was accurate. For rehabilitation, a highly portable exoskeleton featuring flexion/extension finger exercises was developed. The exoskeleton features two four-bar linkages reflecting the natural metacarpophalangeal (MCP) and proximal phalangeal (PIP) joint angles. During optimization, the design parameters were adjusted to reflect normal finger trajectories, which vary by finger length and finger joint ROM. To allow for passive physical impedance, a spring was installed to generate the forces that guided the fingers. The moments transmitted to the MCP and PIP joints were estimated via finite element method (FEM) analysis and the cross-sectional areas of the links were manually designed by reference to the expected joint moments. Finger motion and force distribution experiments verified that the system guided the fingers effectively, allowed for the desired finger motions, and distributed the required moments to the joints (as revealed by FEM analysis).; This thesis reports the development of hand exoskeleton systems, for use in virtual reality (VR) environments and for hand rehabilitation

via ScholarWorks: Wearable Hand Exoskeleton Systems for Virtual Reality and Rehabilitation

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[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings

Abstract

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints.
The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of motion, and speed of the device can all be customized to meet the needs of the user.
Twelve stroke survivors, ranging from the subacute to chronic phases of recovery (mean 10.6 months post-stroke) participated in a pilot study with the device. Participants completed 20 sessions, each lasting 45 minutes. Overall, subjects exhibited statistically significant changes (p < 0.05) in clinical outcome measures following the treatment, with the Fugl-Meyer Stroke Assessment score for the upper extremity increasing from 36 to 50 and the Barthel Index increasing from 74 to 89. Active range of wrist motion increased by 190 while spasticity decreased from 1.75 to 1.29 on the Modified Ashworth Scale.
Thus, this device shows promise for improving rehabilitation outcomes, especially for patients in countries with limited resources.

via Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings – IEEE Journals & Magazine

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[VIDEO] Stroke Rehabilitation: Use of electrical stimulation to help arm and hand recovery

This video demonstrates how to use FES, Functional Electrical Stimulation, to engage the muscles of the arm to extend the fingers.

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[Abstract] Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Abstract

Purpose

The purpose of this paper is to introduce a new design for a finger and wrist rehabilitation robot. Furthermore, a fuzzy sliding mode controller has been designed to control the system.

Design/methodology/approach

Following an introduction regarding the hand rehabilitation, this paper discusses the conceptual and detailed design of a novel wrist and finger rehabilitation robot. The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot.

Findings

With the novel design for moving the DOFs of the system, the rehabilitation for the wrist and all phalanges of fingers is done with only two actuators which are combined in one device. These features make the system a good choice for home rehabilitation. To control the robot, a fuzzy sliding mode controller has been designed for the system. The fuzzy controller does not affect the coefficient of the sliding mode controller and uses the overall error of the system to make a control signal. Thus, the dependence of the controller to the model decreases and the system is more robust. The stability of the system is proved by the Lyapunov theorem.

Originality/value

The paper provides a novel design of a hand rehabilitation robot and a controller which is used to compensate the effects of the uncertain parameters and chattering phenomenon.

via Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger | Emerald Insight

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[Abstract] Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments.

Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke.

Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed.

Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r2 = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury).

Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

via Neural Correlates of Passive Position Finger Sense After Stroke – Morgan L. Ingemanson, Justin R. Rowe, Vicky Chan, Jeff Riley, Eric T. Wolbrecht, David J. Reinkensmeyer, Steven C. Cramer, 2019

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[Abstract] Pre-therapeutic Device for Post-stroke Hemiplegic Patients’ Wrist and Finger Rehabilitation

Abstract

Background/Objectives

This paper suggests a pre-therapeutic device for post-stroke hemiplegic patients’ wrist and finger rehabilitation both to decrease and analyze their muscle tones before the main physical or occupational therapy.

Method/Statistical Analysis

We designed a robot which consists of a BLDC motor, a torque sensor, linear motion guides and bearings. Mechanical structure of the robot induces flexion and extension of wrist and finger (MCP) joints simultaneously with the single motor. The frames of the robot were 3D printed. During the flexion/extension exercise, angular position and repulsive torque of the joints are measured and displayed in real time.

Findings

A prototype was 3D printed to conduct preliminary experiment on normal subject. From the neutral joint position (midway between extension and flexion), the robot rotated 120 degrees to extension direction and 30 degrees to flexion direction. First, the subject used the machine with the usual wrist and finger characteristics without any tones. Second, the same subject intentionally gave strength to the joints in order to imitate affected upper limb of a hemiplegic patient. During extension exercise, maximum repulsive torque of the normal hand was 2 Nm whereas that of the firm hand was almost 5 Nm. The result revealed that the device was capable enough to not only rotate rigid wrist and fingers with the novel robotic structure, but also present quantitative data such as the repulsive torque according to the joint orientation as an index of joint spasticity level.

Improvements/Applications

We are planning to improve the system by applying torque control and arranging experiments at hospitals to obtain patients’ data and feedbacks to meet actual needs in the field.

via Indian Journals

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[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

 

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