Posts Tagged Medical treatment

[Abstract] Application of Gamification Tool in Hand Rehabilitation Process

Abstract

Video games are constantly evolving and today they are one of the main types of entertainment. As we live in the digital age, the implementation of IT solutions in other areas of activity remains relevant. Today, almost all processes use IT technologies: calling a taxi, ordering food, education, shopping, and so on. The use of IT technologies in the field of medicine is not uncommon. But it’s not often that you see video games being used in this area. Video games and their development are an integral part of the IT sphere. The technologies that exist now allow us to create our own game products, which can then be implemented in other processes. This research is aimed at studying the term of “gamification”, its impact on rehabilitation processes, and the study of gamification tools and game products used in the field of medicine. In this research we propose a gamified solution for hand rehabilitation process which include 3D game that work in conjunction with the VR tool called Leap Motion. Since video games attract people with reward systems, goals and many other factors, why not to use it as a key element in process of rehabilitation? In some cases, it is more effective method rather than traditional therapy.

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[Abstract + References] Automated Voluntary Finger Lifting Rehabilitation Support Device for Hemiplegic Patients to Use at Home

Abstract

We have been proposing a robotic finger rehabilitation support device for hemiplegic patients that can be used at home. This device instructs a patient to lift a finger voluntarily and provides assistance when the patient is impossible to lift. In previous studies, we have shown an automated evaluation method which monitors the level of involuntary finger movement. However, the detailed procedure of finger rehabilitation has not been clarified. In this paper, we show a practical procedure of finger rehabilitation as well as a hardware design of the device. We also discuss safety issues during finger lift assistance. Our design limits the speed of finger lift so that it avoids unwanted contraction of finger muscles by stretch reflex. Also, the angle of finger lift is limited in our design so that it will not exceed the maximum excursion of an MP joint.

References

1.J. D. Schaechter, “Motor rehabilitation and brain plasticity after hemiparetic stroke”, Progress in Neurobiology, vol. 73, no. 1, pp. 61-72, May 2004.Show Context CrossRef  Google Scholar 2.J. W. Krakauer, “Motor learning: its relevance to stroke recovery and neurorehabilitation”, Current Opinion in Neurology, vol. 19, no. 1, pp. 84, Feb. 2006.Show Context CrossRef  Google Scholar 3.S. Balasubramanian, J. Klein and E. Burdet, “Robot-assisted rehabilitation of hand function”, Current Opinion in Neurology, vol. 23, no. 6, pp. 661-670, Dec. 2010.Show Context CrossRef  Google Scholar 4.L. Bishop, A. M. Gordon and H. Kim, “Hand Robotic Therapy in Children with Hemiparesis: A Pilot Study”, American Journal of Physical Medicine & Rehabilitation, vol. 96, no. 1, pp. 1-7, Jan. 2017.Show Context CrossRef  Google Scholar 5.O. Sandoval-Gonzalez et al., “Design and Development of a Hand Exoskeleton Robot for Active and Passive Rehabilitation”, International Journal of Advanced Robotic Systems, vol. 13, no. 66, pp. 1-12, 2016.Show Context CrossRef  Google Scholar 6.P. Polygerinos, K. C. Galloway, E. Savage, M. Herman, K. O. Donnell and C. J. Walsh, “Soft robotic glove for hand rehabilitation and task specific training”, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2913-2919, 2015.Show Context View Article Full Text: PDF (3804KB) Google Scholar 7.D. Guilherme, N. DeSouza et al., “A pediatric robotic thumb exoskeleton for at-home rehabilitation: The isolated orthosis for thumb actuation (IOTA)”, International Journal of Intelligent Computing and Cybernetics, vol. 7, no. 3, pp. 233-252, 2014.Show Context Google Scholar 8.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Automated Evaluation of Hand Motor Function Recovery by Using Finger Pressure Sensing Device for Home Rehabilitation”, IEEE BIBE 2018, pp. 207-214, 2018.Show Context View Article Full Text: PDF (500KB) Google Scholar 9.K. Yamamoto, Y. Furudate, K. Chiba, Y. Ishida and S. Mikami, “Home Robotic Device for Rehabilitation of Finger Movement of Hemiplegia Patients”, 2017 IEEE/SICE International Symposium on System Integration, pp. TuE1.3:1-6, 2017.Show Context View Article Full Text: PDF (1305KB) Google Scholar 10.Y. Furudate, K. Yamamoto, K. Chiba, Y. Ishida and S. Mikami, “Quantification Method of Motor Function Recovery of Fingers by Using the Device for Home Rehabilitation”, 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 3872-3875, 2017.Show Context View Article Full Text: PDF (804KB) Google Scholar 11.K. Chiba et al., “Robotic Finger Rehabilitation Support Device for Home Use – An Analysis of the Effect of Finger Rehabilitation for the Upper Limb Function Recovery”, International Journal of Mechanical Engineering and Robotics Research, vol. 5, no. 4, pp. 288-294, Oct. 2016.Show Context CrossRef  Google Scholar 12.“Joint motion: method of measuring and recording”, Churchill Livingstone, 1965.Show Context Google Scholar 13.S. Werle, J. Goldhahn, S. Drerup, B. R. Simmen, H. Sprott and D. B. Herren, “Age- and Gender-Specific Normative Data of Grip and Pinch Strength in a Healthy Adult Swiss Population”, J Hand Surg Eur Vol, vol. 34, no. 1, pp. 76-84, Feb. 2009.Show Context CrossRef  Google Scholar 14.E. L. Altschuler et al., “Rehabilitation of hemiparesis after stroke with a mirror”, The Lancet, vol. 353, no. 9169, pp. 2035-2036, 1999.Show Context CrossRef  Google Scholar 

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[Abstract] A Smart-Band Operated Wrist Rehabilitation Robot

Abstract

Many people in the world are increasingly suffering from stroke issues. Survivors often tend to suffer from hemiplegia or related conditions, in which some portion of their body may be rendered useless. The wrist is one such part. But this injury can be recovered by conventional rehabilitation processes like physical therapy. In this paper, a device for robot-assisted physical therapy is presented for wrist rehabilitation. It can overcome the lack of availability of physical therapists and reduce the cost incurred in long-term therapy. Also, it can provide accurate regular exercises without missing any step even in the absence of the therapist. These two DOF robotic devices can learn the physical exercise (i.e. wrist-based movements) from the trained therapist through an electronic smart-band. It can also replicate these exercises when the patient wears this device over his/her wrist. Here, an accelerometer sensor and a magnetometer sensor-based smart-band are used for recognizing the wrist motions like flexion, extension, abduction, and adduction. The objective of this preliminary work is to drive accurately all the motor actuators which are attached to the robot and calibrate the feedback sensor to reflect the movement of the smart-band. In the future, this robot can be used as a teleoperated rehabilitation device through an IoT platform.

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[Abstract] A Multi-Functional Lower-and Upper-Limb Stroke Rehabilitation Robot

Abstract

It is estimated that about 15 million people a year suffer from stroke worldwide, with 5 million stroke survivors experiencing permanent motor disability requiring therapeutic services. It has been shown that early involvement in rehabilitation therapies has a desirable effect on the long-term recovery of patients. There are, however, several challenges with the current state of delivering rehabilitation services, including limitations on the number of clinics, financial needs, and human resources. Robotic systems have been proposed in the literature to help with these challenges. However, most of the existing robotic systems are expensive, not-portable, and cannot be used for both upper-and lower-limb rehabilitation. This paper presents a 3-DOF robotic device that has been designed to deliver both upper-and lower-limb therapy and incorporates a novel mechanical safety mechanism. The device is capable of teleoperation which makes it particularly suitable for telerehabilitation in the current COVID-19 environment. The rehabilitation robot can deliver therapy in assistive and resistive modes to aid patients at all stages of recovery. In the assistive mode, the robot’s motion provides input to help the patient in completing the therapy task, while in the resistive mode, the robot opposes the motions generated by the patient thereby requiring additional muscle actuation. The robot has been tested by physiotherapists to assess its validity in a clinical setting, and by healthy participants to assess its functionality, safety, and engineering design. The study found that 80physiotherapists agreed the platform has the potential to improve patient outcomes.

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[Abstract + References] A Virtual Reality Serious Game for Hand Rehabilitation Therapy – IEEE Conference Publication

Abstract

The human hand is the body part most frequently injured in occupational accidents, accounting for one out of five emergency cases and often requiring surgery with subsequently long periods of rehabilitation. This paper proposes a Virtual Reality game to improve conventional physiotherapy in hand rehabilitation, focusing on resolving recurring limitations reported in most technological solutions to the problem, namely the limited diversity support of movements and exercises, complicated calibrations and exclusion of patients with open wounds or other disfigurements of the hand. The system was assessed by seven able-bodied participants using a semistructured interview targeting three evaluation categories: hardware usability, software usability and suggestions for improvement. A System Usability Score (SUS) of 84.3 and participants’ disposition to play the game confirm the potential of both the conceptual and technological approaches taken for the improvement of hand rehabilitation therapy.

References

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3. D. Johnson, S. Deterding, K.-A. Kuhn, A. Staneva, S. Stoyanov and L. Hides, “Gamification for health and wellbeing: A systematic review of the literature”, Internet Interv., vol. 6, pp. 89-106, Nov. 2016. Show Context CrossRef  Google Scholar 

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12. M. King, L. Hale, A. Pekkari, M. Persson, M. Gregorsson and M. Nilsson, “An affordable computerised table-based exercise system for stroke survivors”, Disabil. Rehabil. Assist. Technol., vol. 5, no. 4, pp. 288-293, Jul. 2010. Show Context CrossRef  Google Scholar 

13. J. Shin et al., “Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded randomized controlled trial”, J. Neuroeng. Rehabil., vol. 13, no. 1, pp. 17, Dec. 2016. Show Context CrossRef  Google Scholar 

14. R. Lipovsky and H. A. Ferreira, “Hand therapist: A rehabilitation approach based on wearable technology and video gaming”, 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), pp. 1-2, February 2015. Show Context View Article Full Text: PDF (1901KB) Google Scholar 

15. C. Schuster-Amft et al., “Using mixed methods to evaluate efficacy and user expectations of a virtual reality-based training system for upper-limb recovery in patients after stroke: a study protocol for a randomised controlled trial”, Trials, vol. 15, no. 1, pp. 350, Dec. 2014. Show Context CrossRef  Google Scholar 

16. Y. A. Rahman, M. M. Hoque, K. I. Zinnah and I. M. Bokhary, “Helping-Hand: A data glove technology for rehabilitation of monoplegia patients”, 2014 9th International Forum on Strategic Technology (IFOST), pp. 199-204, 2014. Show Context View Article Full Text: PDF (625KB) Google Scholar 

17. M. da Silva Cameirão, S. Bermúdez, I Badia, E. Duarte and P. F. M. J. Verschure, “Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the Rehabilitation Gaming System”, Restor. Neurol. Neurosci., vol. 29, no. 5, pp. 287-298, 2011. Show Context CrossRef  Google Scholar 

18. M. R. Golomb et al., “In-Home Virtual Reality Videogame Telerehabilitation in Adolescents With Hemiplegic Cerebral Palsy”, Arch. Phys. Med. Rehabil., vol. 91, no. 1, pp. 1-8, Jan. 2010. Show Context CrossRef  Google Scholar 

19. R. Proffitt, M. Sevick, C.-Y. Chang and B. Lange, “User-Centered Design of a Controller-Free Game for Hand Rehabilitation”, Games Health J., vol. 4, no. 4, pp. 259-264, Aug. 2015. Show Context CrossRef  Google Scholar 

20. N. Arman, E. Tarakci, D. Tarakci and O. Kasapcopur, “Effects of Video Games-Based Task-Oriented Activity Training (Xbox 360 Kinect) on Activity Performance and Participation in Patients with Juvenile Idiopathic Arthritis: A Randomized Clinical Trial”, Am. J. Phys. Med. Rehabil., vol. 98, no. 3, pp. 174-181, 2019. Show Context CrossRef  Google Scholar 

21. S. Cho, W.-S. Kim, N.-J. Paik and H. Bang, “Upper-Limb Function Assessment Using VBBTs for Stroke Patients”, IEEE Comput. Graph. Appl., vol. 36, no. 1, pp. 70-78, Jan. 2016. Show Context View Article Full Text: PDF (4692KB) Google Scholar 

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Source: https://ieeexplore.ieee.org/abstract/document/9201789

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[Abstract + References] Iterative Learning Control of Gravity Compensation for Upper-Arm Robot-Assisted Rehabilitation

Abstract

Robot-assisted rehabilitation allows patients e.g. suffering from a stroke to practice without continuous supervision from a therapist. To activate neuroplasticity, the patient has to actively participate in the rehabilitation therapy and the robot should only provide as much assistance as required based on the patient’s needs and abilities. For this purpose, gravity compensation is a promising approach as simplifying movements enables the patient to increase the training’s intensity and number of repetitions. Thus, the aim of this paper is the application and implementation of an iterative learning control scheme to adjust the gravity compensation during therapy based on the patient’s abilities. For this purpose, a norm-optimal iterative learning control scheme and an optimization-based proportional-type iterative learning control algorithm are used. To validate and compare them, an experiment with a linear and a second one with a circular motion trajectory is done, while a slowly changing repetitive disturbance in form of an artificial force is applied to imitate the patient. In this case, the measured number of samples per cycle differs due to the underlying control scheme of the robot. For this reason, a mapping process based on the Dijkstra method is done. The results illustrate that both algorithms are robust against disturbances and yield good tracking performance. Thus, also other factors such as the computation effort of both algorithms should be considered in future research.

References

Source: https://ieeexplore.ieee.org/abstract/document/9143805

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[Conference Paper] HandMATE: Wearable Robotic Hand Exoskeleton and Integrated Android App for At Home Stroke Rehabilitation – Full Text

Abstract

We have developed HandMATE (Hand Movement Assisting Therapy Exoskeleton); a wearable motorized hand exoskeleton for home-based movement therapy following stroke. Each finger and the thumb is powered by a linear actuator which provides flexion and extension assistance. Force sensitive resistors integrated into the design measure grasp and extension initiation force. An assistive therapy mode is based on an admittance control strategy. We evaluated our control system via subject and bench testing. Errors during a grip force tracking task while using the HandMATE were minimal (<1%) and comparable to unassisted healthy hand performance. We also outline a dedicated app we have developed for optimal use of HandMATE at home. The exoskeleton communicates wirelessly with an Android tablet which features guided exercises, therapeutic games and performance feedback. We surveyed 5 chronic stroke patients who used the HandMATE device to further evaluate our system, receiving positive feedback on the exoskeleton and integrated app.

SECTION I.

Introduction

Stroke is the leading cause of severe long-term disability in the US [1]. The probability of regaining functional use of the impaired upper extremity is low [2]. At 6 months post stroke, 62% of survivors failed to achieve some dexterity [3]. Such impairments can inhibit the individual’s ability to perform activities of daily living (ADL). Subsequently, upper limb rehabilitation recovery to improve ADL is one of the main self-reported goals of stroke survivors [4].

Outpatient rehabilitation is recommended for survivors that have been discharged from inpatient rehabilitative services [5]. However, outpatient rehabilitation in general is largely underutilized, with only 35.5% of stroke survivors using services [6]. Factors inhibiting outpatient therapy include cost, lack of resources and transportation. Wearable robotics that enable home-based therapy have the potential to overcome these barriers. They provide assistive movement forces which enable task-specific training in real-life situations that patients are often unable to practice without a clinician. See [7] for wearable hand robots for rehabilitation review.

At home therapy is not without its limitations. The inability to motivate oneself and fatigue are the most common reported factors resulting in failure to adhere to home based exercise programs for stroke recovery [8]. While wearable robotics can reduce fatigue during exercise, it does not directly address lack of motivation. Research has shown incorporating games into home therapy can encourage compliance [9]. Zondervan et al. showed that use of an instrumented sensor glove, named the MusicGlove, improved self-reported use and quality of movement, greater than convention at home exercises [9]. Other studies showed increased motivation to complete the therapeutic exercises and optimized movement when the user is given feedback of their performance via the Microsoft Kinect [10]. Wearable robotic systems that offer feedback and gaming capability may optimize at home stroke therapy.

Such a system was presented by Nijenhuis et al. in which stroke survivors showed motor improvements after completing a 6 week self-administered training program comprised of a dynamic hand orthosis and gaming environment [11]. However, the hand device was passive, assisting only with extension, which limits the range of stroke survivors who could utilize such a system. Research groups have proposed combining their powered take-home wearable hand devices with custom integrated gaming systems [12], or guided exercises [13]; however, they have yet to conduct clinical trials. Notably, Ghassemi et al., have developed an integrated multi-user VR system to use with their X-Glove actuated orthosis, which will allow for client-therapist sessions without the patient having to travel [12].

Tablets are relatively inexpensive, portable, and straight forward to use, with 47% of internet users globally already owning one [14]. Furthermore, a recent study demonstrated the success of a tablet based at home exercise program in improving the recovery of stroke survivors [15]. Notably, the study evaluated the accessibility of tablets, concluding every participant used the tablet successfully. Therefore a wearable powered hand robot with a dedicated tablet app which will provide functional games, task-specific guided exercises and feedback of movement, could optimize at home stroke therapy.

SECTION II.

Aims

The goal of this project was to create a wearable robotic exoskeleton that enables repetitive practice of task-specific and goal orientated movements, which translates into improvements in ADL. Furthermore, for maximum use and successful integration into home-based rehabilitation, we aimed to create an Android application compatible with the robotic exoskeleton.

To meet these goals, the following design objectives were established: 1) Assistance with finger flex/extension. 2) Assistance with thumb carpometacarpal (CMC) add/abduction and thumb metacarpophalangeal (MCP) flex/extension. 3) Independent assistive control of each finger and thumb. 4) Portable for at home use, meaning the device has to be lightweight and wireless. 5) Relatively affordable. 6) Integrated with android tablet app. Specific design goals for the app included: 1) Easy to use. 2) Allow the user to control the exoskeletons assistance mode through the app. 3) Records the user’s data and prompts the user via notifications to complete the allocated daily or weekly recommended activity time.

In this paper we will evaluate if the proposed device and app goals have been achieved via bench and subject testing.

SECTION III.

Design

The HandMATE device (Fig. 1) builds upon the Hand Spring Operated Movement Enhancer (HandSOME) devices [16][17][18]. The HandSOME devices are non-motorized wearable exoskeletons that assists stroke patients with finger and thumb extension movements. The HandSOME I device assists with gross whole hand opening movements, while the HandSOME II assists isolated extension movement of 15 finger and thumb degrees of freedom (DOF), allowing performance of various grip patterns used in ADL. While both devices have been shown to significantly increase range of motion (ROM) and functional ability in chronic stroke subjects [16],[18], the HandSOME devices only assist with extension movements and require enough flexion activity to overcome the assistance of the extension springs. As many stroke patients also suffer finger and thumb flexion weakness, we decided to build upon the work of the high DOF HandSOME II and additionally utilize power actuation so we can assist with both flexion and extension movements.

Figure 1: - 
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.
Figure 1:
HandMATE device. Individually actuated fingers and thumb shown. Electronics box is affixed to back of splint.

Continue —-> https://ieeexplore.ieee.org/abstract/document/9175332

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[Abstract] A Wearable Hand Rehabilitation System with Soft Gloves

Abstract

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This paper presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the non-affected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving-force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real-time.

via A Wearable Hand Rehabilitation System with Soft Gloves – IEEE Journals & Magazine

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[Abstract + References] Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke

Abstract

Wearable grip sensing shows potential for hand rehabilitation, but few studies have studied feasibility early after stroke. Here, we studied a wearable grip sensor integrated with a musical computer game (MusicGlove). Among the stroke patients admitted to a hospital without limiting complications, 13% had adequate hand function for system use. Eleven subjects used MusicGlove at home over three weeks with a goal of nine hours of use. On average they achieved 4.1 ± 3.2 (SD) hours of use and completed 8627 ± 7500 grips, an amount comparable to users in the chronic phase of stroke measured in a previous study. The rank-order usage data were well fit by distributions that arise in machine failure theory. Users operated the game at high success levels, achieving note-hitting success >75% for 84% of the 1061 songs played. They changed game parameters infrequently (31% of songs), but in a way that logically modulated challenge, consistent with the Challenge Point Hypothesis from motor learning. Thus, a therapy based on wearable grip sensing was feasible for home rehabilitation, but only for a fraction of subacute stroke subjects. Subjects made usage decisions consistent with theoretical models of machine failure and motor learning.

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via Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke – IEEE Journals & Magazine

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