Posts Tagged hand rehabilitation

[ARTICLE] Robotics for rehabilitation of hand movement in stroke survivors – Full Text

This article aims to give an overall review of research status in hand rehabilitation robotic technology, evaluating a number of devices. The main scope is to explore the current state of art to help and support designers and clinicians make better choices among varied devices and components. The review also focuses on both mechanical design, usability and training paradigms since these parts are interconnected for an effective hand recovery. In order to study the rehabilitation robotic technology status, the devices have been divided in two categories: end-effector robots and exoskeleton devices. The end-effector robots are more flexible than exoskeleton devices in fitting the different size of hands, reducing the setup time and increasing the usability for new patients. They suffer from the control of distal joints and haptic aspects of object manipulation. In this way, exoskeleton devices may represent a new opportunity. Nevertheless their design is complex and a deep investigation of hand biomechanics and physical human–robot interaction is required. The main hand exoskeletons have been developed in the last decade and the results are promising demonstrated by the growth of the commercialized devices. Finally, a discussion on the complexity to define which design is better and more effective than the other one is summarized for future investigations.

Over the past years, rehabilitation engineering has played a crucial role in improving the hand and finger function after stroke. The applications of robotics and mechatronic devices have rapidly expanded from the industrial environment to human assistance in rehabilitation and functional improvements. Rehabilitation engineering has increased the quality lives of individuals with disabilities, offering dedicated training that performs better than conventional methods.

In this way, there are many challenges and opportunities to integrate engineering concepts into hand rehabilitation, and increasing population wellbeing and wealth as well as reducing healthcare costs. This motivates researchers to study, design, and develop novel rehabilitative and assistive technologies and devices to help people to motor functions. Specifically, the current challenge is to transfer the research results and new knowledge to stakeholders creating a general awareness of the importance of rehabilitation engineering.

This review aims to present and discuss the main robotic technologies for hand recovery rehabilitation in stroke survivors, evaluating and comparing previous and current works and researches. This study explores the current state of art to help and support designers and clinicians make better choices among varied devices and components. The review also focuses on both mechanical design (e.g. concept), usability (e.g. setup, lightness, portability) and training paradigms (e.g. hand, hand/wrist or entire arm) since these parts are interconnected for an effective hand recovery. An overview of the main advantages and drawbacks in applying robotics to hand motor impairments is provided in order to give a general view of the relationship between hand rehabilitation devices, rehabilitation theories and results. The challenge is to restore the hand movements such as opening, closing, grasping and releasing movements. Second, a discussion on the application and new challenges of rehabilitation robotic devices is summarized for future investigations. In particular, the main challenges are to develop safe devices with less complex designs, increasing potential for portability and efficacy. In fact, future development for patient treatment should include the device portability to increase the potential applications. The preliminary results have highlighted the robot-assisted therapy currently works hand in hand rather than a replacement of traditional therapy. Therapies and rehabilitation strategies should be not only more effective but also more cost-efficient.

Stroke is one of the leading causes of long-term disability, affecting approximately 14% of world’s population.1,2 33% of survivors reports very limited or no functional use of the upper limb.3 Rehabilitation activities based on repeated exercises have been identified suitable in recovering some degree of motion, in particular, a simple flexion and extension of fingers has demonstrated improvements in hand functionality.4,5 In this way, medical devices and robot-assisted strategies may provide a number of advantages guaranteeing the range of motion (ROM) and avoiding inappropriate movements. Nevertheless, only a limited part of the proposed devices by the literature has been clinically tested, highlighting as the design complexity and development costs may negatively impact the system implementation. The previous and current robots and devices are often too complex to be used by patients limiting any testing on the real users.

Note that the hand functional improvement may be the result of a set of compensatory strategies based on an initial support assisted by the physiotherapist. Usually, these approaches may be suggested during the first months after stroke, when the impairment reduction may be preferred to extensive functional training. In this phase of impairment, the patients show a loss of control and a decreased tactile sensation and proprioception, reducing the physical independence and social integration. The patient’s motivation associated with verbal encouragement may significantly impact the therapy efficacy.

Over the last decades, a set of studies has evaluated the influence of the robot-assisted therapies on arm motor improvement and impairment reduction using randomized clinical controlled trials.612 The obtained results have not shown a complete consensus; nevertheless, the therapy assisted by robotics seems to obtain results beyond what is done by conventional methods.1317 In particular, researchers have been slow to investigate the hand function due to the complexity of this limb.11,12,1820

In any case, a number of studies observed that the rehabilitation training can improve the hand motor in terms of pull, push, and grip strengths, confirming that robotic training is at least as effective as conventional training.13,2124 A significant part of the obtained outcomes have been also proved by Fugl-Meyer Assessment (FMA) and Functional Independence Measure (FIM) tests, performed after the robot treatment.2527

Despite these promising results, the literature review shows also researches that did not observed significant difference between conventional and robotic training groups, highlighting as the conventional therapies are more effective in decreasing levels of impairment and disability.2,8,28,29 Mazzoleni et al.29 and Colombo et al.30 have underlined that there are other significant factors that may impact the efficacy of the training outcome, such as recovery stage, intensity, or duration of the rehabilitation therapy. This point needs to be considered to evaluate and compare different therapy treatments. In the light of these considerations, there are not evident conclusions that sustain the robot-therapy efficacy, suggesting further investigations.31,32

 

Robot-based methods may be used independently by patients in different levels of impairment. Robots permit to obtain a quantifiable measure of subjective performance, repeating treatment protocols without the need of continuous involvement of therapists saving a significant amount of the human labor that may lead to high cost.8,10 In fact, traditional therapist–based methods require several sessions of rehabilitation training, inducing impractical and unaffordable therapies for many patients. Robotic therapy techniques guarantee a safe, intensive, and task-oriented rehabilitation at relatively moderate costs.14,1533 They may apply forces with precision, improving accuracy and reducing variance. These actions are potentially effective to strengthen muscle, ROM, and motor coordination. Advanced robots provide also tactile feedback that may correct the impaired movements. In addition, robot-assisted therapies may be quantified easily and collect a number of parameters useful to track the patient’s status (e.g. spasticity or level of voluntary control).34

A further advantage of robotic rehabilitation consists of the possibility to be combined with other technologies (e.g. virtual reality (VR), brain computer interface (BCI) technology or haptic stimuli).3537 This combination allows to motivate the patients to perform the rehabilitation tasks without the constant supervision, guaranteeing repetitive movements and informative feedback. On the other hand, robot-assisted therapy permits the therapist to conduct rehabilitation tasks for two or more patients at the same time, improving the service efficiency.

Finally, it has been noted that robotics may improve the accessibility to rehabilitation. In fact, a patient prefers to use the unaffected limb in daily activities, damaging the recovery of the impaired limb.38 The possibility to perform rehabilitation in remote locations (e.g. home) using robotics devices may involve better the patient in the recovery process.

Despite these noted advantages, a number of limits and constraints of rehabilitation robot-based cannot be ignored. First, there is a significant gap between the outcomes of rehabilitation robots and people’s expectations. This element may negatively impact patient’s motivations during the therapy. In particular, the personalization is still difficult due to the design complexity of devices. Another further issue is the determination of the most efficient dosage of rehabilitation training.

Although the literature has demonstrated the main advantages and benefits of robot applications, more studies involving a large participant size are required to confirm whether robotic-assisted therapy performs better than conventional methods, evaluating and comparing the treatment dosage. In particular, a lack of robust methods to evaluate the efficacy of the robot-assisted therapy making difficult to define which design is better and more effective than the other one. A deep investigation is needed to explore whether the obtained results on the patient can be maintained in the long term and how the potential improvements can be transformed into the motor skills in performing the activities of daily living (ADL). The user’s safety needs to be guaranteed during the training, avoiding the nonlinear movement of the patient. Further limits are noted on the current robotic devices regarding their design, often complex and unconvinced for the user, or the high costs for the treatment access.3941 The ratio between the price and performance is rather dissatisfactory due to the high cost of development combined with a relatively benefit for patients and clinics.4244 These drawbacks need to be considered in the overall evaluation of robotic application. They represent an open challenge to improve the integration of engineering concepts into hand rehabilitation, increasing population wealth, as well as reducing healthcare costs. These issues justify the low penetration of robotics in the market and the requirements of new investigations. Only a limited number of stroke patients (5%–15%) who requires assistive devices and technologies may access to this service. On the other hand, the studies and researches on rehabilitation robots are becoming strategic for the society due to the fact that the costs of excluding people with disabilities are high and borne by community.45

A primary categorization of rehabilitation robotic technologies is based on the design concepts of the device: end-effector or exoskeleton.

An end-effector device (also called endpoint control) recreates dynamic environments corresponding to ADL, determining the movements at the joint level. Usually, the patient’s joint rotation is distally executed using a support (e.g. a table or a tripod) to facilitate the training and avoiding muscle fatigue. It means that the more proximal joints are not directly controlled by the robot. End-effector devices may be dedicated to hand rehabilitation or to be integrated in more complex structures for the arm recovery.

The second main logic to design a rehabilitation robotic device is the exoskeleton. An exoskeleton, from Greek “exo” = outer and “skeletos” = skeleton, is a wearable robot attached to the user’s limbs, in order to enhance their movements. It focuses on the anatomy of the subject’s hand following the limb segments, each degree of freedom is aligned with the corresponding human joint. Figure 1 illustrates a number of examples. An exoskeleton should be compliant with the user’s movements and delivers at least part of the power required by the movements. In order to guarantee the natural motor of the hand joints, their design is more complex than end-effector devices. For example, a set of components (e.g. rings, hinges, external linkages, or structures) is embedded to accomplish the alignment between the forearm axial rotation of the forearm located along an axis between the ulna and the radius50,51 to support in forearm pronation and supination.

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Figure 1. Examples of rehabilitation robotic devices: (a) Gloreha,46 (b) CyberGrasp,47 (c) Hand of Hope,48 and (d) Reha-Digit.49

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Continue —> Robotics for rehabilitation of hand movement in stroke survivors – Francesco Aggogeri, Tadeusz Mikolajczyk, James O’Kane, 2019

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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.

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[Abstract] Stroke, hand rehabilitation, motor synergy pattern correction, deep relaxation, multisensory stimulation, motor synergy rehabilitation.

Abstract

Background: This study aims to determine motor synergy rehabilitation for upper limb functional recovery in stroke patients.

Methodology: A 48year old male, apparently normal till June, 2016, had an acute onset of right sided hemiparesis and slurred speech. 2 years later he reported with inability to use the upper limb and difficulty in walking independently. He was a diagnosed case of left capsule-ganglionic bleed with accelerated hypertension. His participation limitations were inability to finger feed, drink his coffee, dress and groom selfand discontinuation of his job as an automobile salesman. He received motor synergy rehabilitation for 6 weeks.

Result: At 6 weeks patient was able to perform scapular elevation and shoulder scaption up to 100° with isolated elbow and forearm movements. He re-learnt to release objects with wrist in neutral position with verbal cues. His ability to feel rough textures improved by 50% and silky texture by 40% (self-reported) throughout the limb except hand. He retrained to eat hard cut fruit, sip water from a glass with straw and comb hair with 20–25% assistance and rejoined his job once a week.

Conclusion: Muscle synergy rehabilitation can help to improve the functional use of upper limb in stroke.

Indian Journals

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[Abstract] Does hand robotic rehabilitation improve motor function by rebalancing interhemispheric connectivity after chronic stroke? Encouraging data from a randomised-clinical-trial

Highlights

  • Robotic hand training can be helpful in improving hand motor recovery.
  • Amadeo™ induces large modulations of sensorimotor rhythms and connectivity.
  • Robotic training yields improvement of hand motor performance by restoring hand motor control.

 

Abstract

Objective

The objective of this study was the evaluation of the clinical and neurophysiological effects of intensive robot-assisted hand therapy compared to intensive occupational therapy in the chronic recovery phase after stroke.

Methods

50 patients with a first-ever stroke occurred at least six months before, were enrolled and randomised into two groups. The experimental group was provided with the Amadeo™ hand training (AHT), whereas the control group underwent occupational therapist-guided conventional hand training (CHT). Both of the groups received 40 hand training sessions (robotic and conventional, respectively) of 45 min each, 5 times a week, for 8 consecutive weeks. All of the participants underwent a clinical and electrophysiological assessment (task-related coherence, TRCoh, and short-latency afferent inhibition, SAI) at baseline and after the completion of the training.

Results

The AHT group presented improvements in both of the primary outcomes (Fugl-Meyer Assessment for of Upper Extremity and the Nine-Hole Peg Test) greater than CHT (both p<0.001). These results were paralleled by a larger increase in the frontoparietal TRCoh in the AHT than in the CHT group (p<0.001) and a greater rebalance between the SAI of both the hemispheres (p<0.001).

Conclusions

These data suggest a wider remodelling of sensorimotor plasticity and interhemispheric inhibition between sensorimotor cortices in the AHT compared to the CHT group.

Significance

These results provide neurophysiological support for the therapeutic impact of intensive robot-assisted treatment on hand function recovery in individuals with chronic stroke.

 

via Does hand robotic rehabilitation improve motor function by rebalancing interhemispheric connectivity after chronic stroke? Encouraging data from a randomised-clinical-trial – ScienceDirect

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[Abstract + References] Design of wearable hand rehabilitation glove with soft hoop-reinforced pneumatic actuator

Abstract

Traditional hand rehabilitation gloves usually use electrical motor as actuator with disadvantages of heaviness, bulkiness and less compliance. Recently, the soft pneumatic actuator is demonstrated to be more suitable for hand rehabilitation compared to motor because of its inherent compliance, flexibility and safety. In order to design a wearable glove in request of hand rehabilitation, a soft hoop-reinforced pneumatic actuator is presented. By analyzing the influence of its section shape and geometrical parameters on bending performance, the preferred structure of actuator is achieved based on finite element method. An improved hoop-reinforced actuator is designed after the fabrication and initial measurement, and its mathematical model is built in order to quickly obtain the bending angle response when pressurized. A series of experiment about bending performance are implemented to validate the agreement between the finite element, mathematical and experimental results, and the performance improvement of hoop-reinforced actuator. In addition, the designed hand rehabilitation glove is tested by measuring its output force and actual wearing experience. The output force can reach 2.5 to 3 N when the pressure is 200 kPa. The research results indicate that the designed glove with hoop-reinforced actuator can meet the requirements of hand rehabilitation and has prospective application in hand rehabilitation.

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[Abstract] Design and development of a portable exoskeleton for hand rehabilitation

Abstract:

Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P=0.014), WMFT (P=0.020) and FMA_WH (P=0.021). Increase in the mean values of FMA_SE was observed but without significant difference (P=0.071). The improvement in ARAT score reflects the motor recovery in hand and finger functions. The increased FMA scores suggest there is motor improvement in the whole upper limb, and especially in the hand after the training. The product met patients’ requirements and has practical significance. It is portable, cost effective, easy to use and supports multiple control modes to adapt to different rehabilitation phases.

 

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[Abstract + References] A Novel Method for Designing and Implementing a Training Device for Hand Rehabilitation – Conference paper

 

Abstract

Improvement in hand function to promote functional recovery is an important goal of stroke rehabilitation. However, not all of the rehabilitation products are sufficiently well developed for use in daily life. This paper introduces a newly developed hand training device with a user-centred design concept, which integrates fuzzy-based quality function deployment and morphological analysis method. As a key to rehabilitation product design, the study focuses on how and to what extent certain technical attributes of products are to be met to obtain a higher level of user satisfaction. The paper also tested the function in a local hospital. Test results showed that the hand affected due to a stroke could complete the training task successfully. It also showed that the product met patients’ requirements and has practical significance. The proposed method also can be applied to the development of similar products.

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[Abstract] Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – Conference Paper

I. Introduction

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 [1]–[3]. 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 [4]–[6]. Part of the stroke recovery process is a rehabilitation plan [7]. 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.

References

1. D. Tsoupikova, N. S. Stoykov, M. Corrigan, K. Thielbar, R. Vick, Y. Li, K. Triandafilou, F. Preuss, D. Kamper, “Virtual immersion for poststroke hand rehabilitation therapy”, Annals of biomedical engineering, vol. 43, no. 2, pp. 467-477, 2015.

2. J. E. Pompeu, T. H. Alonso, I. B. Masson, S. M. A. A. Pompeu, C. Torriani-Pasin, “The effects of virtual reality on stroke rehabilitation: a systematic review”, Motricidade, vol. 10, no. 4, pp. 111-122, 2014.

3. J.-H. Shin, S. B. Park, S. H. Jang, “Effects of game-based virtual reality on health-related quality of life in chronic stroke patients: A randomized controlled study”, Computers in biology and medicine, vol. 63, pp. 92-98, 2015.

4. R. W. Teasell, L. Kalra, “Whats new in stroke rehabilitation”, Stroke, vol. 35, no. 2, pp. 383-385, 2004.

5. E. McDade, S. Kittner, “Ischemic stroke in young adults” in Stroke Essentials for Primary Care, Springer, pp. 123-146, 2009.

6. P. Langhorne, J. Bernhardt, G. Kwakkel, “Stroke rehabilitation”, The Lancet, vol. 377, no. 9778, pp. 1693-1702, 2011.

7. C. J. Winstein, J. Stein, R. Arena, B. Bates, L. R. Cherney, S. C. Cramer, F. Deruyter, J. J. Eng, B. Fisher, R. L. Harvey et al., “Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the american heart association/american stroke association”, Stroke, vol. 47, no. 6, pp. e98-e169, 2016.

8. R. Ibanez, A. Soria, A. Teyseyre, M. Campo, “Easy gesture recognition for kinect”, Advances in Engineering Software, vol. 76, pp. 171-180, 2014.

9. R. Ibañez, A. Soria, A. R. Teyseyre, L. Berdun, M. R. Campo, “A comparative study of machine learning techniques for gesture recognition using kinect”, Handbook of Research on Human-Computer Interfaces Developments and Applications, pp. 1-22, 2016.

10. S. Bhattacharya, B. Czejdo, N. Perez, “Gesture classification with machine learning using kinect sensor data”, Emerging Applications of Information Technology (EAIT) 2012 Third International Conference on, pp. 348-351, 2012.

11. K. Laver, S. George, S. Thomas, J. E. Deutsch, M. Crotty, “Virtual reality for stroke rehabilitation”, Stroke, vol. 43, no. 2, pp. e20-e21, 2012.

12. G. Saposnik, M. Levin, S. O. R. C. S. W. Group et al., “Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians”, Stroke, vol. 42, no. 5, pp. 1380-1386, 2011.

13. K. R. Anderson, M. L. Woodbury, K. Phillips, L. V. Gauthier, “Virtual reality video games to promote movement recovery in stroke rehabilitation: a guide for clinicians”, Archives of physical medicine and rehabilitation, vol. 96, no. 5, pp. 973-976, 2015.

14. A. Estepa, S. S. Piriz, E. Albornoz, C. Martínez, “Development of a kinect-based exergaming system for motor rehabilitation in neurological disorders”, Journal of Physics: Conference Series, vol. 705, pp. 012060, 2016.

15. E. Chang, X. Zhao, S. C. Cramer et al., “Home-based hand rehabilitation after chronic stroke: Randomized controlled single-blind trial comparing the musicglove with a conventional exercise program”, Journal of rehabilitation research and development, vol. 53, no. 4, pp. 457, 2016.

16. L. Ebert, P. Flach, M. Thali, S. Ross, “Out of touch-a plugin for controlling osirix with gestures using the leap controller”, Journal of Forensic Radiology and Imaging, vol. 2, no. 3, pp. 126-128, 2014.

17. W.-J. Li, C.-Y. Hsieh, L.-F. Lin, W.-C. Chu, “Hand gesture recognition for post-stroke rehabilitation using leap motion”, Applied System Innovation (ICASI) 2017 International Conference on, pp. 386-388, 2017.

18. K. Vamsikrishna, D. P. Dogra, M. S. Desarkar, “Computer-vision-assisted palm rehabilitation with supervised learning”, IEEE Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 991-1001, 2016.

19. A. Butt, E. Rovini, C. Dolciotti, P. Bongioanni, G. De Petris, F. Cavallo, “Leap motion evaluation for assessment of upper limb motor skills in parkinson’s disease”, Rehabilitation Robotics (ICORR) 2017 International Conference on, pp. 116-121, 2017.

20. L. Di Tommaso, S. Aubry, J. Godard, H. Katranji, J. Pauchot, “A new human machine interface in neurosurgery: The leap motion (®). technical note regarding a new touchless interface”, Neuro-Chirurgie, vol. 62, no. 3, pp. 178-181, 2016.

21. O. Chapelle, “Training a support vector machine in the primal”, Neural computation, vol. 19, no. 5, pp. 1155-1178, 2007.

22. Y. Ma, G. Guo, Support vector machines applications, Springer, 2014.

23. J. Guna, G. Jakus, M. Pogačnik, S. Tomažič, J. Sodnik, “An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking”, Sensors, vol. 14, no. 2, pp. 3702-3720, 2014.

24. T. DOrazio, R. Marani, V. Renó, G. Cicirelli, “Recent trends in gesture recognition: how depth data has improved classical approaches”, Image and Vision Computing, vol. 52, pp. 56-72, 2016.

25. L. Motion, Leap motion sdk, 2015.

 

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[Abstract] Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation

Abstract

In the modern world, due to an increased aging population, hand disability is becoming increasingly common. The prevalence of conditions such as stroke is placing an ever-growing burden on the limited fiscal resources of health care providers and the capacity of their physical therapy staff. As a solution, this paper presents a novel design for a wearable and adaptive glove for patients so that they can practice rehabilitative activities at home, reducing the workload for therapists and increasing the patient’s independence. As an initial evaluation of the design’s feasibility the prototype was subjected to motion analysis to compare its performance with the hand in an assessment of grasping patterns of a selection of blocks and spheres. The outcomes of this paper suggest that the theory of design has validity and may lead to a system that could be successful in the treatment of stroke patients to guide them through finger flexion and extension, which could enable them to gain more control and confidence in interacting with the world around them.

I. Introduction

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 [1]. 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 [1], [2]. 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.

 

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