Posts Tagged hand rehabilitation

[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.

References

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    Ottobock WaveFlex Hand CPM Device. Available at http://professionals.ottobockus.com/cps/rde/xbcr/ob_us_en/12122525_6_WaveFlex_CPM_PATIENT_IFU.pdf. Cited 25.06.2015
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    Idrogenet GLOREHA supports upper limb rehabilitation. Available at http://www.gloreha.com/index.php/en/. Cited 24.06.2015
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    Festo AG, Co KG (2015) New scope for interaction between humans and machines. Available at http://www.festo.com/net/SupportPortal/Files/156734/Brosch_FC_ExoHand_EN_lo_L.pdf. Cited 24.06.2015
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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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[Abstract + References] Development of a hand exoskeleton system for index finger rehabilitation

Abstract

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

References

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[Abstract] A novel exoskeleton robotic system for hand rehabilitation – Conceptualization to prototyping

Abstract

This research presents a novel hand exoskeleton rehabilitation device to facilitate tendon therapy exercises. The exoskeleton is designed to assist fingers flexion and extension motions in a natural manner. The proposed multi-Degree Of Freedom (DOF) system consists of a direct-driven, optimized and underactuated serial linkage mechanism having capability to exert extremely high force levels perpendicularly on the finger phalanges. Kinematic and dynamic models of the proposed device have been derived. The device design is based on the results of multi-objective optimization algorithm and series of experiments conducted to study capabilities of the human hand. To permit a user-friendly interaction with the device, the control is based on minimum jerk trajectory generation. Using this control system, the transient response and steady state behavior of the proposed device are analyzed after designing and fabricating a two-fingered prototype. The pilot study shows that the proposed rehabilitation system is capable of flexing and extending the fingers with accurate trajectories.

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[Abstract] Design of a Low-Cost Exoskeleton for Hand Tele-Rehabilitation After Stroke

Abstract

The impairment of finger movements after a stroke results in a significant deficit in hands everyday performances. To face this kind of problems different rehabilitation techniques have been developed, nevertheless, they require the presence of a therapist to be executed. To overcome this issue have been designed several apparatuses that allow the patient to perform the training by itself. Thus, an easy to use and effective device is needed to provide the right training and complete the rehabilitation techniques in the best way. In this paper, a review of state of the art in this field is provided, along with an introduction to the problems caused by a stroke and the consequences for the mobility of the hand. Then follows a complete review of the low cost home based exoskeleton project design. The objective is to design a device that can be used at home, with a lightweight and affordable structure and a fast mounting system. For implementing all these features, many aspects have been analysed, starting from the rehabilitation requirements and the ergonomic issues. This device should be able to reproduce the training movements on an injured hand without the need for assistance by an external tutor.

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[Student thesis] Exoskeleton for hand rehabilitation – Full Text PDF

Abstract

This document presents the development of a first proposal prototype of a rehabilitation exoskeleton hand. The idea was to create a lighter, less complex and cheaper exoskeleton than the existing models in the market but efficient enough to carry out rehabilitation therapies.The methodology implemented consists of an initial literature review followed by data collection resulting in a pre-design in two dimensions using two different software packages, MUMSA and WinmecC. First, MUMSA provides the parameters data of the movement of the hand to be done accurately. With these parameters, the mechanisms of each finger are designed using WinmecC. Once the errors were solved and the mechanism was achieved, the 3D model was designed.The final result is presented in two printed 3D models with different materials. The models perform a great accurate level on the motion replica of the fingers by using rotary servos. The properties of the model can change depending on the used material. ABS material gives a flexible prototype, and PLA material does not achieve it. The use of distinct methods to print has a high importance on the difficulties of development throughout the entire process of production. Despite found difficulties in the production, the model was printed successfully, obtaining a compact, strong, lightweight and eco-friendly with the environment prototype.

fulltext (4120 kB)

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[ARTICLE] Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation – Full Text

Abstract

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.

1. Introduction

Hand rehabilitation is extremely important for recovering from post-stroke or post-surgery residual impairments and its effectiveness depends on frequency, duration and quality of the rehabilitation sessions [1]. Traditional rehabilitation requires a therapist for driving and controlling patients during sessions. Procedure effectiveness is evaluated subjectively by the therapist, basing on experience. In the last years, several automated (tele)rehabilitation gloves, based on mechanical devices or tracking sensors, have been presented [2,3,4,5,6,7,8,9,10]. These gloves allow the execution of therapy at home and rehabilitation effectiveness can be analytically calculated and summarized in numerical parameters, controlled by therapists through Internet. Moreover, these equipment can be easily interfaced with virtual reality (VR) environments [11], which have been proven to increase rehabilitation efficacy [12]. Mechanical devices are equipped with pressure sensors and pneumatic actuators for assisting and monitoring the hand movements and for applying forces to which the patient has to oppose [13,14]. However, they are expensive, cumbersome, patient specific (different patients cannot reuse the same system) and hand specific (the patient cannot use the same system indifferently with both hands). Tracking-based gloves consist of computer vision algorithms for the analysis and interpretation of videos from depth sensing sensors to calculate hand kinematics in real time [10,15,16,17,18,19]. Besides depth sensors, LEAP [20] is a small and low-cost hand 3D tracking device characterized by high-resolution and high-reactivity [21,22,23], used in VR [24], and has been recently presented and tested with success in the hand rehabilitation, with exercises designed in VR environments [25]. Despite the advantages of using LEAP with VR, a single sensor does not allow accurate quantitative evaluation of hand and fingers tracking in case of occlusions. The system proposed in [10] consisted on two orthogonal LEAPs designed to reduce occlusions and to improve objective hand-tracking evaluation. The two sensors were fixed to a wood support that maintained them orthogonal each other. The previous prototype was useful to test the robustness of each sensor, in presence of the other, to the potential infra-red interferences, to evaluate the maintenance of the maximum operative range of each sensor and, finally, to demonstrate the hand tracking idea. However, it was imprecise, due to the usage of raw VG support and positioning system, the non-optimal reciprocal positioning of the sensors, and the impossibility of performing a reciprocal calibration independent of the sensors measurements. This fact did not allow the evaluation of the intrinsic precision of the VG and to perform accurate, real-time quantitative hand tracking measurements. In this paper, we present a method for constructing an engineered version of the LEAP based VG, a technique for its accurate calibration and for collecting accurate positioning measurements and high-quality evaluation of positioning errors, specific of VG. Moreover, real-time experimental hand tracking measurements were collected (a video demonstrating its real-time performance and precision was also provided in the Supplementary Materials), presented and discussed.[…]

 

Continue —>  Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation

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Figure 1
VG mounted on its aluminium support.

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