Posts Tagged thumb

[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation

Abstract

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.

via https://ieeexplore.ieee.org/abstract/document/8701573

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[Abstract] Gesture Interaction and Augmented Reality based Hand Rehabilitation Supplementary System – IEEE Conference Publication

Abstract:

The existing systems of hand rehabilitation always design different rehabilitation medical apparatus and systems according to the patients’ needs. This kind of system always contain problems such as complexity, using only single training programs, inconvenient to wear and high cost. For these reasons, this paper uses gesture recognition technology and augmented reality technology to design a simple and interactive hand rehabilitation supplementary system. The system uses a low-cost, non-contact device named Leap Motion as the input device, and Unity3D as the development engine, realizing three functional modules: conventional training, AR game training and auxiliary functions. This rehabilitation training project with different levels of difficulty increases the fun and challenge of the rehabilitation process. Users can use the system to assist the treatment activity of hand rehabilitation anytime and anywhere. The system, which has good application value, can also be used in other physical rehabilitation fields.

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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.

 

via Design and development of a portable exoskeleton for hand rehabilitation – IEEE Journals & Magazine

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[Abstract + References] Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface

Abstract

Comparing with the traditional way for hand rehabilitation, such as simple trainers and artificial rigid auxiliary, this paper presents an isometric and isotonic soft hand for rehabilitation supported by the soft robots theory which aims to satisfy the more comprehensive rehabilitation requirements. Salient features of the device are the ability to achieve higher and controllable stiffness for both isometric and isotonic contraction. Then we analyze the active control for isometric and isotonic movement through electroencephalograph (EEG) signal. This paper focuses on three issues. The first is using silicon rubber to build a soft finger which can continuously stretch and bend to fit the basic action of the fingers. The second is changing stiffness of the finger through the coordination between variable stiffness cavity and actuating cavity. The last is to classify different EEG states based on isometric and isotonic contraction using common spatial pattern feature extraction (CSP) methods and support vector machine classification methods (SVM). On this basis, an EEG-based manipulator control system was set up.

 

I. Introduction

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.

References

1. J Zhang, H Wang, J Tang et al., “Modeling and design of a soft pneumatic finger for hand rehabilitation [C]”, IEEE International Conference on Information and Automation, pp. 2460-2465, 2015.

2. H Godaba, J Li, Y Wang et al., “A Soft Jellyfish Robot Driven by a Dielectric Elastomer Actuator [J]”, IEEE Robotics & Automation Letters, vol. 1, no. 2, pp. 624-631, 2016.

3. Y Yang, Y. Chen, “Novel design and 3D printing of variable stiffness robotic fingers based on shape memory polymer [C]”, IEEE International Conference on Biomedical Robotics and Biomechatronics, pp. 195-200, 2016.

4. M Wehner, R L Truby, D J Fitzgerald et al., “An integrated design and fabrication strategy for entirely soft autonomous robots [J]”, Nature, vol. 536, no. 7617, pp. 451, 2016.

5. P Polygerinos, Z Wang, K C Galloway et al., “Soft robotic glove for combined assistance and at-home rehabilitation [J]”, Robotics & Autonomous Systems, vol. 73, no. C, pp. 135-143, 2014.

6. M Tian, Y Xiao, X Wang et al., “Design and Experimental Research of Pneumatic Soft Humanoid Robot Hand [M]/ /” in Robot Intelligence Technology and Applications 4. Springer International Publishing, 2017.

7. K Y Hong, J H Lim, F Nasrallah et al., “A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness [C]”, IEEE International Conference on Robotics and Automation, pp. 4967-4972, 2015.

8. J.R Wolpaw, N Birbaumer, WJ Heetderks, DJ Mcfarland, PH Peckham, G Schalk et al., “Brain-computer interface technology: a review of thefirst international meeting”, IEEE Transactions on Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, vol. 8, no. 2, pp. 164, 2000.

9. C Ethier, ER Oby, MJ Bauman, LE. Miller, “Restoration of grasp following paralysis through brain-controlled stimulation of muscles”, Nature, vol. 485, no. 7398, pp. 368, 2012.

10. C JL, B W, D JE, W W, T EC, W DJ et al., “High-performance neuroprosthetic control by an individual with tetraplegia”, Lancet, vol. 381, no. 9866, pp. 557-564, 2013.

11. UA Qidwai, M. Shakir, Fuzzy Classification-Based Control of Wheelchair Using EEG Data to Assist People with Disabilities, vol. 7666, pp. 458-467, 2012.

12. UA Qidwai, M. Shakir, Fuzzy Classification-Based Control of Wheelchair Using EEG Data to Assist People with Disabilities, vol. 7666, pp. 458-467, 2012.

13. D Broetz, C Braun, C Weber, S.R Soekadar, A Caria, N. Birbaumer, “Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report”, Neurorehabilitation & Neural Repair, vol. 24, no. 7, pp. 674, 2010.

14. BH. Dobkin, “Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation”, Journal of Physiology, vol. 579, no. Pt 3, pp. 637, 2007.

15. S.R Soekadar, N Birbaumer, LG. Cohen, Brain-Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma, Japan:Springer, 2011.

16. LR Hochberg, B Daniel, J Beata, NY Masse, JD Simeral, V Joern et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm”, Nature, vol. 485, no. 7398, pp. 372-375, 2013.

17. S R Soekadar, M Witkowski, C Gómez et al., Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia [J], vol. 1, no. 1, pp. eaag3296, 2016.

18. L B. Rosenberg, Force feedback interface having isotonic and isometric functionality: CA US 5825308 A [P], 1998.

19. L B. Rosenberg, Isotonic-isometric haptic feedback interface: US US71 02541 [P], 2006.

20. J T Gwin, D P. Ferris, “An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions [J]”, Journal of NeuroEngineering and Rehabilitation, vol. 9, no. 1, pp. 35, 9 2012-06-09.

21. S Bouisset, F Goubel, B. Maton, “[Isometric isotonic contraction and anisotonic isometric contraction: an electromyographic comparison] [J]”, Electromyography & Clinical Neurophysiology, vol. 13, no. 5, pp. 525, 1973.

 

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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] Electromyography Based Orthotic Arm and Finger Rehabilitation System

Abstract

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.

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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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[Abstract] A soft robotic glove for hand motion assistance

Abstract:

Soft robotic devices have the potential to be widely used in daily lives for their inherent compliance and adaptability, which result in high safety under unexpected situations. System complexity and requirements are much lower, comparing with conventional rigid-bodied robotic devices, which also result in significantly lower costs. This paper presents a robotic glove by utilizing soft artificial muscles providing redundant degrees of freedom (DOFs) to generate both flexion and extension hand motions for daily grasping and manipulation tasks. Different with the existing devices, to minimize the weight applied to the user’s hands, pneumatic soft actuators were located on the fore arm and drive each finger via cable-transmission mechanisms. This actuation mechanism brings extra adaptability, motion smoothness, and user safety to the system. This design makes wearable robotic gloves more light-weight and user-friendly. Both theoretical and experimental analyses were conducted to explore the mechanical properties of pneumatic soft actuators. In addition, the fingertip trajectories were analyzed using Finite Element Methods, and a series of experiments were conducted evaluating both the technical and practical performances of the proposed glove.

 

I. Introduction

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 [1]–[12]. 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.

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[Abstract] Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact

Abstract

Recent technological developments regarding wearable soft-robotic devices extend beyond the current application of rehabilitation robotics and enable unobtrusive support of the arms and hands during daily activities. In this light, the HandinMind (HiM) system was developed, comprising a soft-robotic, grip supporting glove with an added computer gaming environment. The present study aims to gain first insight into the feasibility of clinical application of the HiM system and its potential impact. In order to do so, both the direct influence of the HiM system on hand function as assistive device and its therapeutic potential, of either assistive or therapeutic use, were explored. A pilot randomized clinical trial was combined with a cross-sectional measurement (comparing performance with and without glove) at baseline in 5 chronic stroke patients, to investigate both the direct assistive and potential therapeutic effects of the HiM system. Extended use of the soft-robotic glove as assistive device at home or with dedicated gaming exercises in a clinical setting was applicable and feasible. A positive assistive effect of the soft-robotic glove was proposed for pinch strength and functional task performance `lifting full cans’ in most of the five participants. A potential therapeutic impact was suggested with predominantly improved hand strength in both participants with assistive use, and faster functional task performance in both participants with therapeutic application.

I. Introduction

Neurorehabilitation research has shown that training programs for patients after stroke should ideally consist of high intensity, task-specific and functional exercises with active contribution of the patient, to have the best chance for improving arm/hand function [1], [2]. Conventional rehabilitation involves predominantly one-to-one attention of a therapist for each patient, which is a challenge when aiming to provide high intensity training and involves high costs [3], [4]. This is impeded further by an increased ageing of the population, associated with a higher prevalence of stroke patients and less healthcare professionals available to provide such intensive training.

 

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[Abstract] Quantification method of motor function recovery of fingers by using the device for home rehabilitation – IEEE Conference Publication

Abstract:

After leaving hospital, patients can carry out rehabilitation by using rehabilitation devices. However, they cannot evaluate the recovery by themselves. For this problem, a device which can both carry out the rehabilitation and evaluation of the degree of recovery is required. This paper proposes the method that quantifies the recovery of the paralysis of fingers to evaluate a patient automatically. A finger movement is measured by a pressure sensor on the rehabilitation device we have developed. A measured data is used as a time-series signal, and the recovery of the paralysis is quantified by calculating the dissimilarity between a healthy subject’s signal and the patient’s signal. The results of those dissimilarities are integrated over all finger to be used as a quantitative scale of recovery. From the experiment conducted with hemiplegia patients and healthy subjects, we could trace the process of the recovery by the proposed method.

Source: Quantification method of motor function recovery of fingers by using the device for home rehabilitation – IEEE Conference Publication

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