Posts Tagged index finger

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


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

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[Abstract + References] Development of a hand exoskeleton system for index finger rehabilitation


In order to overcome the drawbacks of traditional rehabilitation method, the robot-aided rehabilitation has been widely investigated for the recent years. And the hand rehabilitation robot, as one of the hot research fields, remains many challenging issues to be investigated. This paper presents a new hand exoskeleton system with some novel characteristics. Firstly, both active and passive rehabilitative motions are realized. Secondly, the device is elaborately designed and brings advantages in many aspects. For example, joint motion is accomplished by a parallelogram mechanism and high level motion control is therefore made very simple without the need of complicated kinematics. The adjustable joint limit design ensures that the actual joint angles don’t exceed the joint range of motion (ROM) and thus the patient safety is guaranteed. This design can fit to the different patients with different joint ROM as well as to the dynamically changing ROM for individual patient. The device can also accommodate to some extent variety of hand sizes. Thirdly, the proposed control strategy simultaneously realizes the position control and force control with the motor driver which only works in force control mode. Meanwhile, the system resistance compensation is preliminary realized and the resisting force is effectively reduced. Some experiments were conducted to verify the proposed system. Experimentally collected data show that the achieved ROM is close to that of a healthy hand and the range of phalange length (ROPL) covers the size of a typical hand, satisfying the size need of regular hand rehabilitation. In order to evaluate the performance when it works as a haptic device in active mode, the equivalent moment of inertia (MOI) of the device was calculated. The results prove that the device has low inertia which is critical in order to obtain good backdrivability. The experiments also show that in the active mode the virtual interactive force is successfully feedback to the finger and the resistance is reduced by one-third; for the passive control mode, the desired trajectory is realized satisfactorily.


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[WEB SITE] Smartphones Reshape Sensory Processing Via Neuroplasticity

Smartphones Reshape Sensory Processing Via Neuroplasticity

Smartphones are just one of the personal digital technologies that were shown capable of shaping cortical sensory processing in the modern human brain, according to findings from a study recently published in the journal Current Biology.

First author of the study, Anne-Dominique Gindrat, MS, is a PhD candidate who has published previous investigations about difference in hand dominance and whole-scalp electroencephalography (EEG) mapping. Those studies were on non-human primates, whereas the recent article, “Use-Dependent Cortical Processing from Fingertips in Touchscreen Phone Users,” was based on a study with human subjects.

The study pointed out that smartphone users demonstrate an enhanced thumb sensory representation in the brain, and that brain activity is proportional to use accumulated over the previous 10 days.

Another significant finding noted from the study is that an episode of intense use is transiently imprinted on the sensory representation. And, in addition, sensory processing in the brain is adjusted on demand by touchscreen phone use.

The study abstract points out that cortical activity allotted to the tactile receptors on human fingertips are related to the level of skill with which a hand is used. Surgeons, masterful musicians, or video game players may exhibit a higher degree of response to touch on certain portions of their fingers than individuals who do not have highly developed touch skills. Similar characteristics are also found among certain primates who are trained in grasp-and-release skills.

Electroencephalography was used by the researchers to measure the cortical potentials in response to mechanical touch on the thumb, index, and middle fingertips of study subjects who used touchscreen phones, as well as those who used older styles of mobile phones. All three fingers demonstrated higher cortical potentials among those who used touchscreen phones.

The authors note: “Our results suggest that repetitive movements on the smooth touchscreen reshaped sensory processing from the hand and that the thumb representation was updated daily depending on its use.”

[Source: Current Biology]

Smartphones Reshape Sensory Processing Via Neuroplasticity | Rehab Managment.

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