Posts Tagged thumb
[Abstract + References] Design and Kinematics Analysis of a Bionic Finger Hand Rehabilitation Robot Mechanism
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.
Stroke, one of the leading causes of adult disability, affects approximately 800,000 individuals each year in the United States . 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. , 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 . 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 . 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 . 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.
2.1 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.
[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation
In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.
[Abstract] Gesture Interaction and Augmented Reality based Hand Rehabilitation Supplementary System – IEEE Conference Publication
[Abstract + References] Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface
In recent years, stroke has became one of the major health problems which significantly affect the daily life of the elderly, and hand rehabilitation is introduced as an auxiliary treatment. Though various kinds of mechanical devices for hand rehabilitation have been developed, some deficiencies still exist in the current rigid rehabilitation hand, such as the degrees of freedom is not enough, complexity, unsafe status, overweight, being uncomfortable, unfitness and so on. Therefore, with the growth of aging population, it is highly needed to develop some new devices to satisfy the comprehensive rehabilitation requirements. Meanwhile, inspired by the mollusks in nature, soft robot is made of soft materials that can withstand large strains. It is a new type of continuum robot with high flexibility and environmental adaptability. The soft robot has a broad application prospects in military detection techniques, such as instance search, rescue, medical application and other fields.
[Abstract] Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – Conference Paper
Nowadays, a stroke is the fourth leading cause of death in the United States. In fact, every 40 seconds, someone in the US is having a stroke. Moreover, around 50% of stroke survivors suffer damage to the upper extremity –. Many actions of treating and recovering from a stroke have been developed over the years, but recent studies show that combining the recovery process with the existing rehabilitation plan provides better results and a raise in the patients quality of life –. Part of the stroke recovery process is a rehabilitation plan . The process can be difficult, intensive and long depending on how adverse the stroke and which parts of the brain were damaged. These processes usually involve working with a team of health care providers in a full extensive rehabilitation plan, which includes hospital care and home exercises.
Electromyography (EMG), a technique used to analyze and record electric current produced by skeletal muscles, has been used to control replacement limbs, and diagnose muscle irregularities. In this work, an EMG based system comprising of an orthotic arm and finger device to aid in muscle rehabilitation, is presented. As the user attempts to contract their bicep or forearm muscles, the system senses the change in the EMG signals and in turn triggers the motors to assist with flexion and extension of the arm and fingers. As brain is a major factor for muscle growth, mental training using motor imagery was incorporated into the system. Subjects underwent mental training to show the capability of muscle growth. The measured data reveals that the subjects were able to compensate for the loss of muscle growth, due to shorter physical training sessions, with mental training. Subjects were then tested using the orthotic arm and finger rehabilitation device with motor imagery. The findings also showed a positive increase in muscle growth using the rehabilitation system. Based on the experimental results, the EMG rehabilitation system presented in this paper has the potential to increase muscle strength and improve the recovery rate for muscle injuries, partial paralysis, or muscle irregularities.
In the modern world an extended life expectancy coupled with a sedentary lifestyle raises concerns over long term health in the population. This is highlighted by the increasing incidence of disability stemming from multiple sources, for example medical conditions such as cancer or stroke . While avoiding the lifestyle factors that have a high association with these diseases would be the preferred solutions of health services the world over, as populations get progressively older and more sedentary, this becomes increasingly more difficult , . The treatment of these conditions is often complex; in stroke for example, the initial incident is a constriction of blood flow in the brain which in turn damages the nervous system’s ability to communicate with the rest of the body. This damage will occur in one hemisphere of the body but can impact both the upper and lower limbs, as well as impairing functional processes such as speech and cognitive thinking.
Glove-type wearable robotic devices are developed to assist people with impaired hand functions both in their activities of daily living (ADLs) and in rehabilitation –. Most of such wearable robotic devices generate hand movements with linkage systems actuated by electrical motors which usually are heavy and inconvenient for using. Moreover, because of the human hand variation, most wearable robotic devices require customization in order to fulfill the geometrical fitting requirements between the exoskeleton device and the human hand joints. Approximating the high dexterity of human hands usually requires high complexity in both the mechanical and controller structures of the robotic systems, and hence also results in high costs for most users.