Posts Tagged finger

[Abstract+References] Motion-Based Serious Games for Hand Assistive Rehabilitation


Cerebral Palsy, trauma, and strokes are common causes for the loss of hand movements and the decrease in muscle strength for both children and adults. Improving fine motor skills usually involves the synchronization of wrists and fingers by performing appropriate tasks and activities. This demo introduces a novel patient-centered framework for the gamification of hand therapies in order to facilitate and encourage the rehabilitation process. This framework consists of an adaptive therapy-driven 3D environment augmented with our motion-based natural user interface. An intelligent game generator is developed, which translates the patient’s gestures into navigational movements with therapy-driven goals, while adapting the level of difficulty based on the patient profile and real-time performance. A comprehensive evaluation and clinical-based assessments were conducted in a local children disability center, and highlights of the results are presented.



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[ARTICLE] Reorganization of finger coordination patterns through motor exploration in individuals after stroke – Full Text




Impairment of hand and finger function after stroke is common and affects the ability to perform activities of daily living. Even though many of these coordination deficits such as finger individuation have been well characterized, it is critical to understand how stroke survivors learn to explore and reorganize their finger coordination patterns for optimizing rehabilitation. In this study, I examine the use of a body-machine interface to assess how participants explore their movement repertoire, and how this changes with continued practice.


Ten participants with chronic stroke wore a data glove and the finger joint angles were mapped on to the position of a cursor on a screen. The task of the participants was to move the cursor back and forth between two specified targets on a screen. Critically, the map between the finger movements and cursor motion was altered so that participants sometimes had to generate coordination patterns that required finger individuation. There were two phases to the experiment – an initial assessment phase on day 1, followed by a learning phase (days 2–5) where participants trained to reorganize their coordination patterns.


Participants showed difficulty in performing tasks which had maps that required finger individuation, and the degree to which they explored their movement repertoire was directly related to clinical tests of hand function. However, over four sessions of practice, participants were able to learn to reorganize their finger movement coordination pattern and improve their performance. Moreover, training also resulted in improvements in movement repertoire outside of the context of the specific task during free exploration.


Stroke survivors show deficits in movement repertoire in their paretic hand, but facilitating movement exploration during training can increase the movement repertoire. This suggests that exploration may be an important element of rehabilitation to regain optimal function.


Stroke often results in impairments of upper extremity, including hand and finger function, with 75% of stroke survivors facing difficulties performing activities of daily living [12]. Critically, impairments after stroke not only include muscle- and joint-specific deficits such as weakness, and changes in the kinetic and kinematic workspace of the fingers [34], but also coordination deficits such as reduced independent joint control [5] and impairments in finger individuation and enslaving [6789]. Therefore, understanding how to address these coordination deficits is critical for improving hand rehabilitation.

Typical approaches to hand rehabilitation emphasize repetition [10] and functional practice based on evidence that such experience can cause reorganization in the brain [11]. Although this has proven to be reasonably successful, functional practice (such as repetitive grasping of objects) does not specify the coordination pattern to be used when performing the tasks. As a result, because of the redundancy in the human body, there is a risk that stroke survivors may adopt atypical compensatory movements to perform tasks [12]. These compensatory movements have been mainly identified during reaching [1314], but there is evidence that they are also present in finger coordination patterns during grasping [15]. Although there is still debate over the role of compensatory movements in rehabilitation [16], there is at least some evidence both in animal and humans that continued use of these compensatory patterns may be detrimental to true recovery [171819].

To address this issue, there has been a greater focus on directly facilitating the learning of new coordination patterns. Specifically, in hand rehabilitation, virtual tasks (such as playing a virtual piano) have been examined as a way to train finger individuation [2021]. In these protocols, individuation is encouraged by asking participants to press a particular key with a finger, while keeping other fingers stationary. A similar approach to improve hand dexterity was also adopted by developing a glove that could be used as a controller for a popular guitar-playing video game [22]. However, directly instructing desired coordination patterns to be produced becomes challenging as the number of degrees of freedom involved in the coordination pattern increase. For example, the hand has approximately 20 kinematic degrees of freedom, and providing verbal, visual or auditory feedback for simultaneously controlling all these degrees of freedom would be a major challenge. A potential solution that has been suggested is not to directly instruct the coordination pattern itself, but rather let participants explore different coordination patterns [23]. This idea of motor exploration is based on dynamical systems theory that suggests that variability and exploration may help participants escape sub-optimal pre-existing coordination patterns and potentially settle in more optimal coordination patterns [24252627]. Such exploration has been shown to be important in adapting existing movement repertoire [28], and has also been shown to be associated with faster rates of learning [29].

In order to test the hypothesis that exploration of novel coordination patterns can improve overall movement repertoire, I used a body-machine interface [3031] to examine how stroke survivors explore and reorganize finger coordination patterns with practice. A body-machine interface maps body movements (in this case finger movements) to the control of a real or virtual object (in this case a screen cursor), which can provide a way to elicit different coordination patterns in the context of an intuitive task. Specifically I examined: (i) how stroke survivors reorganize their finger coordination patterns, (ii) how training to explore novel coordination patterns affects their ability to reorganize their coordination pattern, and (iii) if training to explore novel coordination patterns has an effect on their overall movement repertoire. In this context, I use the term “novel” to indicate coordination patterns that require finger individuation. This assumption is motivated by the finding that stroke survivors have difficulty producing finger individuation even under explicit instruction [69], and therefore it is highly likely that they would not use coordination patterns requiring finger individuation frequently in activities of daily living.[…]

Continue —>  Reorganization of finger coordination patterns through motor exploration in individuals after stroke | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 a Experimental setup – participants wore a data glove and moved their fingers to control a screen cursor b Schematic of task – participants moved a cursor between two targets using movements of four fingers (thumb excluded). c Experimental protocol. Participants came in for 5 total sessions – an initial assessment phase, followed by a learning phase. d Weighting coefficients of the index and middle (blue), and ring and little (red) fingers during the initial assessment phase, and e weighting coefficients during the learning phase

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[Abstract] A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients


Stroke survivors who experience severe hemipare-sis often cannot completely recover the use of their hand and arm. Many of the rehabilitation devices currently available are designed to increase the functional recovery right after the stroke when, in some cases, biological restoring and plastic reorganization of the central nervous system can take place. However, this is not always the case. Even after extensive therapeutic interventions, the probability of regaining functional use of the impaired hand is low. In this respect, we present a novel robotic system composed of a supernumerary robotic finger and a wearable cutaneous finger interface. The supernumerary finger is used to help grasping objects while the wearable interface provides information about the forces exerted by the robotic finger on the object being held. We carried out two experiments, enrolling 16 healthy subjects and 2 chronic stroke patients. Results showed that using the supernumerary finger greatly improved the grasping capabilities of the subjects. Moreover, providing cutaneous feedback significantly improved the performance of the considered task and was preferred by all subjects.

Source: A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients – IEEE Xplore Document

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[ARTICLE] A Finger Exoskeleton Robot for Finger Movement Rehabilitation – Full Text HTML


In this study, a finger exoskeleton robot has been designed and presented. The prototype device was designed to be worn on the dorsal side of the hand to assist in the movement and rehabilitation of the fingers. The finger exoskeleton is 3D-printed to be low-cost and has a transmission mechanism consisting of rigid serial links which is actuated by a stepper motor. The actuation of the robotic finger is by a sliding motion and mimics the movement of the human finger. To make it possible for the patient to use the rehabilitation device anywhere and anytime, an Arduino™ control board and a speech recognition board were used to allow voice control. As the robotic finger follows the patients voice commands the actual motion is analyzed by Tracker image analysis software. The finger exoskeleton is designed to flex and extend the fingers, and has a rotation range of motion (ROM) of 44.2°.

1. Introduction

Statistically, one in six people in the world will have a stroke [1] at some time, or develop some debilitating bone condition. Most strokes are caused by an interruption of the blood supply to part of the brain. It is very important for stroke patients to move the parts of the body that have been affected to restore and retrain movement. This rehabilitation is very important for the patient and is particularly so for the achievement of full movement. This not only helps to maintain muscle tension and strength, and increase durability, but also promotes blood circulation [2].
Rehabilitation systems have been extensively studied for effective restoration and training of muscle activity in the arm or hand [3,4]. The degree of upper limb rehabilitation is also used in clinical tests [5]. However, a finger exoskeleton is more difficult to design than one for the arm because it requires many more degrees of freedom (DOF) of motion and this involves small moving parts [6]. The design of a typical finger mechanism is complicated, has involved control requirements, and is usually very expensive. To reduce the cost and simplify the fabrication and operation, many people working on the problem began to use underactuated mechanisms in the design of a robot finger [7,8].
An underactuated mechanism has fewer driving sources than the number of DOF. Such an underactuated finger mechanism can be simple in structure, and is easily made even simpler by linking the motion of individual joints, or linking the motion of one finger to another finger [9]. Tendon-actuated and linkage mechanisms are the most common underactuated mechanisms in current use. However, the development and progress of robotic engineering has allowed the underactuated robot to include more DOF and has also lowered the complexity in many different applications.
A tendon-driven mechanism [10] can simply use a nylon line to stretch and bend the fingers. It has the advantage of simplicity and also absorbs shock; however, the line itself is under tension, which puts more load on the finger joints that increases friction forces, and is itself subject to elastic deformation. This kind of mechanism can only be used under a small load. Linkage-type mechanisms driven by auxiliary links to control the fingers have advantages. They are easy to analyze and mechanically rigid, but the many links lead to a loose structure and a humanoid robot finger comparable in size to that of a real finger is not easy to achieve [11].
Various hand exoskeleton technologies for rehabilitation and assistive robotics have recently been developed [12]. To design a proper hand or finger exoskeleton, the biomechanics of the hand/finger, robotic mechanisms, and control methods must be considered. Hand exoskeletons can be driven by different actuators, including electric actuators, pneumatic actuators, and smart material actuators [12]. Allota [13] used external servo motors to drive the exoskeleton fingers, whereas the radio control (RC) servomotors pulled the cables to actuate the fingers in the opening or closure phase. Polygerinos [14] used a soft pneumatic glove to produce bending motions to follow the motion of human fingers.
In this paper, a rehabilitative robotic finger is presented that can be used to maintain muscle strength through repetitive action, which also has the effect of functional recovery by rebuilding the sensorimotor links through the reorganization process in the damaged brain. To avoid the limitations of the heavy and bulky exoskeleton, the design of the finger used an underactuated mechanism, and a 3D printer was used to fabricate a prototype. Thus, the exoskeleton is affordable and competes with conventional therapy costs. In continuous passive motion therapy, a patient usually cannot control the movement through conscious effort; therefore, we used auto speech recognition to help patients control rehabilitation efforts themselves. A specific key word was used to start the robot and a carefully chosen stepper motor was used to power the mechanism. The actual motion was analyzed using the Open Source Physics tool, Tracker.

2. Design and Simulation

The design of the exoskeleton robot was undertaken with a number of important considerations in mind, the most pertinent of which were shape, size, cost, and weight. The weight and cost of the exoskeleton are critical to the users. In our design, the cost (around 30 US dollars) is affordable and competes with conventional therapy costs, while the weight is less than 45 grams. The device needed to fit on a finger and its movement had to follow the finger of the disabled patient. Before embarking on the project, we first studied finger bending motion as well as the general structure of finger muscles and bones. The input torque is set to 30 N-mm according to the motor selected. In the experiment, this torque can move the finger slowly, which is suitable for slight stroke patients. For moderate stroke patients, a higher torque motor with a similar size can be selected with a slight increase of cost and weight. We used Solidworks™ and Autodesk Inventor™ to both design and analyze the system.

2.1. Design

The slider-type robotic finger we designed can be divided into two main parts: the slider itself and the N-shaped linkage, as shown in Figure 1. The design concept of the slider mechanism was to locate the centers of the two arc-shaped sliders on the proximal and distal finger joints separately and to ensure the robotic finger followed human finger motion. In addition, the N-shaped linkage mechanism was designed to connect the proximal and distal arc-shaped sliders and to make them bend together. The N-shaped linkage used is simple and reduced the size of the finger.
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Figure 1. Design of the finger exoskeleton robot that allows the finger to curl from (a) extended to (d) flexed.

The prototype robotic finger has three sliders, five links, ten bolts, and one motor. As the motor rotates, the blue crank moves the gray coupler forwards or backwards. The gray coupler pushes and pulls the yellow slider arm, making it move along the slot. When the yellow slider moves, this causes the green link, or N-shaped linkage, to rotate, which in turn causes the yellow and outer red sliders to move together. The N-shaped linkage continues to push and pull the outer red slider, causing it to move along the slot. The outer red slider connects to the human finger and causes it to bend.[…]

Continue —>  Inventions | Free Full-Text | A Finger Exoskeleton Robot for Finger Movement Rehabilitation | HTML

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[Abstract] A Randomized Controlled Study: Effectiveness of Functional Electrical Stimulation on Wrist and Finger Flexor Spasticity in Hemiplegia


The objective of this study was to investigate the effectiveness of functional electrical stimulation (FES) applied to the wrist and finger extensors for wrist flexor spasticity in hemiplegic patients.


Thirty stroke patients treated as inpatients were included in the study. Patients were randomly divided into study and control groups. FES was applied to the study group. Wrist range of movement, the Modified Ashworth Scale (MAS), Rivermead Motor Assessment (RMA), Brunnstrom (BS) hand neurophysiological staging, Barthel Index (BI), and Upper Extremity Function Test (UEFT) are outcome measures.


There was no significant difference regarding range of motion (ROM) and BI values on admission between the groups. A significant difference was found in favor of the study group for these values at discharge. In the assessment within groups, there was no significant difference between admission and discharge RMA, BS hand, and UEFT scores in the control group, but there was a significant difference between the admission and discharge values for these parameters in the study group. Both groups showed improvement in MAS values on internal assessment.


It was determined that FES application is an effective method to reduce spasticity and to improve ROM, motor, and functional outcomes in hemiplegic wrist flexor spasticity.


Source: A Randomized Controlled Study: Effectiveness of Functional Electrical Stimulation on Wrist and Finger Flexor Spasticity in Hemiplegia – Journal of Stroke and Cerebrovascular Diseases

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[WEB SITE] MusicGlove for Stroke Therapy – Flint Rehab

MusicGlove: Hand Therapy with a Beat

What Is MusicGlove?

MusicGloveMusicGlove is a hand therapy device that is clinically proven to improve hand function in 2 weeks.

The device is a sensorized “glove” that allows users to perform hundreds of hand and finger exercises while playing a therapy-based musical game.

How does it work?

To use the device, you simply put the MusicGlove on your hand, plug it into your personal laptop or Flint tablet, and press play.

Then, follow along and make the appropriate pinching movements when each musical note floats down the screen.

What’s the Research Behind It?

ForTherapistsImage_croppedExercise with MusicGlove has been clinically proven to:

  • Improve hand function in 2 weeks
  • Lead to functional gains such as opening a door, washing dishes, typing, and using the restroom independently
  • Motivate safe, high-intensity movements that initiate neuroplasticity in the brain

How is it different?

Most assistive hand devices help open your hand but fail to retrain your brain how to use your hand again.

MusicGlove is unique because it’s designed to initiate neuroplasticity, the process that your brain uses to rewire itself after injury. The more you play the game, the better your brain becomes at controlling your hand!

Who Is MusicGlove For?

To use MusicGlove hand therapy actively without assistance, you need the ability to touch your thumb to at least one of your fingertips or side of your index finger.


If you cannot make this movement, then you can try using the device passively. Read this article to learn more.

MusicGlove is intended to treat:

  • Stroke
  • Spinal Cord Injury
  • Cerebral Palsy
  • Traumatic Brain Injury
  • Neurologic and muscular injury
  • Developmental disability

If you have received hand therapy in clinic and want to continue at home, MusicGlove is for you!

Are You a Clinician?

If so, please visit our MusicGlove for Clinic Use page!


Visit Site —> MusicGlove for Stroke Therapy – Flint Rehab

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[ARTICLE] Development of Device and Serious Game Contents for the Multi-finger Rehabilitation – Full Text

In modern society, with the increasing use of such compact devices as smart phones and computers, finger and hand mobility is very important for daily living. Generally, in the case where there is impaired motor function of the hands or fingers, rehabilitation involves boring repetitive exercises. In this study, serious games were implemented using a dynamometer which made it possible to measure grip width and finger grip strength according to the size of the hand. The game was developed based on rhythm games, and, by selectively training the fingers that need rehabilitation, it is possible to improve a variety of functions such as finger agility, power and endurance. In addition, by analyzing data changes during the training process, the intensity of the rehabilitation can be quantitatively assessed. Furthermore, it provided users with an active and fun rehabilitation environment because they could choose and use their own desired music files during their rehabilitation.

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[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback


The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.

Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document

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[Abstract] Design of a thumb module for the FINGER rehabilitation robot


This paper describes the design and initial prototype of a thumb curling exoskeleton for movement therapy. This add-on device for the Finger INdividuating Grasp Exercise Robot (FINGER) guides the thumb through a single-degree-of-freedom naturalistic grasping motion. This motion complements the grasping motions of the index and middle fingers provided by FINGER. The kinematic design and mechanism synthesis described herein utilized 3D motion capture and included the determination of the principle plane of the thumb motion for the simple grasping movement. The results of the design process and the creation of a first prototype indicate that this thumb module for finger allows naturalistic thumb motion that expands the capabilities of the FINGER device.

Source: IEEE Xplore Document – Design of a thumb module for the FINGER rehabilitation robot

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[ARTICLE] The Robotic Sixth Finger: a Wearable Compensatory Tool to Regain Grasping Capabilities in Paretic Hands – Full Text PDF


Among the most promising field of applications of wearable robotics there are the rehabilitation and the support in activities of daily living (ADL) of impaired people. In this paper, we propose two possible designs of a robotic extra-finger, the Robotic Sixth Finger, for grasping compensation in patients with reduced hand mobility, such as post-stroke patients. The idea is to let the patients be able to grasp an object by taking advantage of the wearable device worn on the paretic limb by means of an elastic band. The Robotic Sixth Finger and the paretic hand work jointly to hold an object. Adding a robotic opposing finger is a promising approach that can significantly improve the grasping functional compensation in different typologies of patients during everyday life activities.

1 Introduction

Wearable robots are expected to work very closely, to interact and collaborate with people in an intelligent environment [1]. Traditionally, wearable robotic structures have been mainly used in substitution of lost limbs (e.g., prosthetic limbs) or for human limb rehabilitation (e.g., exoskeletons). However, the progress in miniaturization and efficiency of the technological components is allowing more light and compact solutions, enhancing user’s safety and comfort, while opening new opportunities for wearable robot use [2]. Together with exoskeleton and prosthesis, a very promising research direction seems to be that of adding robotic limbs to human, rather than substituting or enhancing them [3]. This addition could let the humans augment their abilities and could give support in everyday tasks to impaired people. This paper investigates how to compensate the capabilities of the human hand, instead of developing additional robotic extra-arms, as discussed for instance in [4]. The idea of using an extra-finger to support the human hand in grasping functions was initially proposed in [5]. Then, independently both in [6] and [7, 8], the authors proposed the use of extra fingers to support the human hand to grasp objects whose size does not fit a hand or in executing bimanual tasks with one hand. The main difference is that in [7, 8], the goal was to minimize the size and the weight of the unique extra limb, while in [6], two extra fingers were used so to hold objects. While in [9] the authors developed a control strategy to grasp and manipulate objects, in [10] the authors mainly focused on the use of extra fingers for post-stroke patients.

Focusing on the hand, many wearable devices have been proposed in the last decade, especially for hand rehabilitation and function recovery. A review on robotassisted approaches to motor neurorehabilitation can be found in [11]. In [12] the authors presented a comprehensive review of hand exoskeleton technologies for rehabilitation and assistive engineering, from basic hand biomechanics to actuator technologies.

However, most of the devices proposed in literature are designed either to increase the functional recovery in the first months of the rehabilitation therapy, when biological restoring and reorganization of the central nervous system take place, or are designed to augment human hand capabilities of healthy subjects by coordinating the device motion to that of the hand.

To the best of our knowledge, only few works target on the robotic compensation of hand function in the latter phase of rehabilitation. This means that patients usually after 6–9 months of rehabilitation must rely only on compensatory strategies by improving adaptations that increase the functional disparity between the impaired and the unaffected upper limb [13].

This work focuses on the compensation of hand function in patients with paretic limbs, e.g. chronic stroke patients. The final aim is to provide the patient with an additional robotic finger worn on the wrist. The Robotic Sixth Finger is used together with the paretic hand to seize an object, as shown in Fig. 1. The systems acts like a two-finger gripper, where one finger is represented by the Robotic Sixth Finger, while the other by the patient paretic limb. The proposed device goes beyond exoskeletons: it adds only what is needed to grasp, i.e. an extra thumb. We presented in [7] a preliminary version of a robotic extra-finger showing how this wearable device is able to enhance grasping capabilities and hand dexterity in healthy subjects. In [8], we also presented an object-based mapping algorithm to control robotic extra-limbs without requiring explicit commands by the user. The main idea of the mapping was to track human hand by means of dataglove and reproduce the main motions on the extra-finger. This kind of approach is not suitable for patients with a paretic limb due to the reduced mobility of the hand. Therefore, we developed a wearable interface embedded in a ring to activate and use the finger [14].

In this work, we propose two possible designs of devices. The first model is a modular fully actuated finger. The other design consists of an underactuated finger which is compliant and consequently able to adapt to the different shapes of the objects.

For validation purposes, pilot experiments with two chronic stroke patients were performed. The experiments consisted in wearing the Robotic Sixth Finger and performing a rehabilitation test referred to as Frenchay Arm Test [15, 10]. Finally, we present preliminary results on the use of the extra-fingers for grasping objects for Activities of Daily Living (ADL).

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