Posts Tagged Medical treatment

[Abstract + References] Upper Limb Rehabilitation Therapies Based in Videogames Technology Review

Abstract

Worldwide, stroke is the third cause of physical disability, rehabilitation therapy is a main topic of focus for the recovery of life quality. Rehabilitation of these patients presents great challenges since many of them do not find the motivation to perform the necessary exercises, or do not have the economic resources or the adequate support to receive physiotherapy. For several years now, an alternative that has been in development is game-based rehabilitation, since this could be used in a hospital environment and eventually at patients home. The aim of this review is to present the advances in videogames technology to be used for rehabilitation and training purposes- in preparation for prosthetics fitting or Neuroprosthesis control training–, as well as the devices that are being used to make this alternative more tangible. Videogames technology rehabilitation still has several challenges to work on, more research and development of platforms to have a larger variety of games to engage with different age-range patients is still necessary.
1. Y. X. Hung , P. C. Huang , K. T. Chen , and W. C. Chu , “ What do stroke patients look for in game-based rehabilitation: A survey study ,” Med. (United States) , vol. 95 , no. 11 , pp. 1 – 10 , 2016 .

2. E. Vogiatzaki , Y. Gravezas , N. Dalezios , D. Biswas , A. Cranny , and S. Ortmann , “ Telemedicine System for Game-Based Rehabilitation of Stroke Patients in the FP7- ‘ StrokeBack ’ Project ,” 2014 .

3. W. Johnson , O. Onuma , and S. Sachdev , “ Stroke: a global response is needed ,” Bull. World Heal. Organ ., vol. 94 p. 634 – 634A , 2016 .

4. A. Tabor , S. Bateman , E. Scheme , D. R. Flatla , and K. Gerling , “ Designing Game-Based Myoelectric Prosthesis Training ,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI ’17 , 2017 , pp. 1352 – 1363 .

5. B. Lange et al. , “ Interactive game-based rehabilitation using the Microsoft Kinect ,” Proc. – IEEE Virtual Real ., no. November 2016 , pp. 171 – 172 , 2012 .

6. C. Prahm , I. Vujaklija , F. Kayali , P. Purgathofer , and O. C. Aszmann , “ Game-Based Rehabilitation for Myoelectric Prosthesis Control ,” JMIR Serious Games , vol. 5 , no. 1 , pp. 1 – 13 , 2017 .

7. B. D. Winslow , M. Ruble , and Z. Huber , “ Mobile, Game-Based Training for Myoelectric Prosthesis Control ,” Front. Bioeng. Biotechnol .,vol. 6 , no. July , pp. 1 – 8 , 2018 .

8. “ The SENIAM Project ,” 2019 . [Online]. Available: http://www.seniam.org . [Accessed: 21-Jan-2019 ].

9. M. B. I. Reaz , M. S. Hussain , and F. Mohd-Yasin , “ Techniques of EMG signal analysis: Detection, processing, classification and applications ,” Biol. Proced. Online , vol. 8 , no. 1 , pp. 11 – 35 , 2006 .

10. R. S. Armiger and R. J. Vogelstein , “ Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms ,” Circuits Syst. Conf. BIOCAS 2008 , pp. 121 – 124 , 2008 .

11. H. Oppenheim , R. S. Armiger , and R. J. Vogelstein , “ WiiEMG: A real-time environment for control of the Wii with surface electromyography ,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems , 2010 , pp. 957 – 960 .

12. G. I. Yatar and S. A. Yildirim , “ Wii Fit balance training or progressive balance training in patients with chronic stroke: a randomised controlled trial ,” J. Phys. Ther. Sci ., vol. 27 , no. 4 , pp. 1145 – 1151 , 2015 .

13. N. Norouzi-Gheidari , M. F. Levin , J. Fung , and P. Archambault , “ Interactive virtual reality game-based rehabilitation for stroke patients ,” in 2013 International Conference on Virtual Rehabilitation, ICVR 2013 2013 .

14. B. Lange , C. Chang , E. Suma , B. Newman , A. S. Rizzo , and M. Bolas , “ Development and Evaluation of Low Cost Game-Based Balance Rehabilitation Tool Using the Microsoft Kinect Sensor ,” 2011 , pp. 1831 – 1834 .

15. Y. Chen et al. , “ Game Analysis, Validation, and Potential Application of EyeToy Play and Play 2 to Upper-Extremity Rehabilitation ,” no. December , 2014 .

16. P. Visconti , F. Gaetani , G. A. Zappatore , and P. Primiceri , “ Technical features and functionalities of Myo armband: An overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses ,” Int. J Smart Sens. Intell. Syst ., vol. 11 , no. 1 , pp. 1 – 25 , 2018 .

17. S. S. Esfahlani and G. Wilson , “ Development of Rehabilitation System (ReHabgame) through Monte-Carlo Tree Search Algorithm ,” 2018 , pp. 1 – 8 .

18. “ Welcome to Myo Support ,” 2019 . [Online]. Available: https://support.getmyo.com/hc/en-us [Accessed: 19-Jan-2019 ].

19. “ PAULA 1.2 | Myo Software | Myo Hands and Components |Upper Limb Prosthetics | Prosthetics | Ottobock US Healthcare .”[Online]. Available: https://professionals.ottobockus.com/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/PAULA-1-2/p/646C52~5V1~82 [Accessed: 21-Jan-2019 ].

20. J. Lewis , P. Merritt , M. Bowler , and D. Brown , “ Evaluation of the suitability of games based stroke rehabilitation using the Novint Falcon ,” 2018 , no. August .

21. G. Ghazaei , A. Alameer , P. Degenaar , G. Morgan , and K. Nazarpour , “ Deep learning-based artificial vision for grasp classification in myoelectric hands ,” J. Neural Eng ., vol. 14 , no. 3 , 2017 .

22. B. Terlaak , H. Bouwsema , C. K. V. D. Sluis , and R. M. Bongers , “ Virtual training of the myosignal ,” PLoS One , vol. 10 , no. 9 , 2015 .

23. J. W. Burke , M. D. J. McNeill , D. K. Charles , P. J. Morrow , J. H. Crosbie , and S. M. McDonough , “ Designing Engaging, Playable Games for Rehabilitation ,” in 8th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT) , 2010 , pp. 195 – 201 .

 

via Upper Limb Rehabilitation Therapies Based in Videogames Technology Review – IEEE Conference Publication

, , , , , , , , , ,

Leave a comment

[Abstract + References] Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study

Abstract

Stroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network’s topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contrale-sional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient’s cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.
1. J-H. Shin , H. Ryu & S. H. Jang . A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. Journal of NeuroEngineering and Rehabilitation. 2014: 11-32

2. M. S. Cameirão , S. B. i Badia , E. D. Oller & P. F. M. J. Verschure . Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. Journal of NeuroEngineering and Rehabilitation. 2010: 7-48

3. R. M. Yerkes & J. D. Dodson . The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology. 1908. 18: 459-482

4. E. J. Calabrese . Converging concepts: Adaptive response, preconditioning, and the YerkesDodson Law are manifestations of hormesis. Ageing Research Reviews. 2008: 7(1), 820.

5. Page S. J. , Fulk G. D. , Boyne P. Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People With Minimal to Moderate Impairment Due to Chronic Stroke. Physical Therapy 92(6): 791798, 2012. doi: 10.2522/ptj.20110009

6. Ogawa S , Lee TM , Kay AR , Tank DW . Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990; 87(24):9868-72. doi: 10.1073/pnas.87.24.9868

7. NK. Logothetis , J. Pauls , M. Augath , T. Trinath , A. Oeltermann . Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001. 412(6843):150-7

8. M.D. Fox , M. E. Raichle . Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007. 8(9):700-11.

9. de Campos, B. M. , Coan, A. C. , Lin Yasuda, C. , Casseb, R. F. and Cendes, F. (2016), Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum. Brain Mapp. doi: 10.1002/hbm.23231

10. J. D. Power , A. L. Cohen , S. M. Nelson , G. S. Wig , K. A. Barnes , J. A. Church , A. C. Vogel , T. O. Laumann , F. M. Miezin , B. L. Schlagger , S. E. Petersen . Functional network organization of the human brain. Neuron. 2011: 72(4): 665 – 678.

11. Rubinov M. and Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 2010, 52(3): 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

12. M. E. J. Newman . A measure of betweenness centrality based on random walks. Soc. Netw. 2005. 27: 39 – 57.

 

via Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study – IEEE Conference Publication

, , , , , ,

Leave a comment

[Abstract] Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game – IEEE Conference Publication

Abstract:

This paper presented a game-based rehabilitation of the upper limb after stroke. We designed and developed a game for supporting stroke patients to have an exercise their arms, and the game had functions for recording their playing and showing a performance report. The performance report can infer the progress of bilateral uppper-limb rehabilitation and use for comparing among patient cases. This is because the game used a Kinect device to detect the arm movements in aspect of precision and speed.

 

1. L. Anderson, G. A. Sharp, R. J. Norton, H. Dalal, S. G. Dean, K. Jolly, A. Cowie, A. Zawada, R. S. Taylor, “Home-based versus centre-based cardiac rehabilitation”, The Cochrane Library, 2017.

2. K. Thomson, A. Pollock, C. Bugge, M. C. Brady, “Commercial gaming devices for stroke upper limb rehabilitation: a survey of current practice”, Disability and Rehabilitation: Assistive Technology, vol. 11, no. 6, pp. 454-461, 2016.

3. L. Y. Joo, T. S. Yin, D. Xu, E. Thia, P. F. Chia, C. W. K. Kuah, K. K. He, “A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke”, Journal of rehabilitation medicine, vol. 42, no. 5, pp. 437-441, 2010.

4. K. Price, “Health promotion and some implications of consumer choice”, Journal of nursing management, vol. 14, no. 6, pp. 494-501, 2006.

5. J. A. M. Bravo, P. Paliyawan, T. Harada, R. Thawonmas, “Intelligent assistant for providing instructions and recommending motions during full-body motion gaming”, Consumer Electronics (GCCE) 2017 IEEE 6th Global Conference on. IEEE, pp. 1-2, 2017.

 

via Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game – IEEE Conference Publication

 

, , , , , , , , , , ,

Leave a comment

[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.

 

via Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation – IEEE Journals & Magazine

, , , , , , , , , , , ,

Leave a comment

[Abstract + References] Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games – IEEE Conference Publication

Abstract

 Serious Games and Virtual Reality (VR) are being considered at present as an alternative to traditional rehabilitation therapies. In this paper, the ongoing development of a framework focused on rehabilitation and assessment of the upper limb motor function based on serious games as a source of entertainment for physiotherapy patients is described. A set of OpenSource Serious Games for rehabilitation has been developed, using the last version of Microsoft1® Kinect™ as low cost monitoring sensor and the software Unity. These Serious Games captures 3D human body data and it stored them in the patient database to facilitate a later clinical analysis to the therapist. Also, a VR-based system for the automated assessment of motor function based on Fugl-Meyer Assessment Test (FMA) is addressed. The proposed system attempts to be an useful therapeutic tool for tele-rehabilitation in order to reduce the number of patients, time spent and cost to
hospitals.

I. Introduction

Biomechanical analysis is an important feature during the evaluation and clinical diagnosis of motor deficits caused by traumas or neurological diseases. For that reason Motion capture (MoCap) systems are widely used in biomechanical studies, in order to collect position data from anatomical landmarks with high accuracy. Their results are used to estimate joint movements, positions, and muscle forces. These quantitative results improve the tracking of changes in motor functions over time, being more accurately than clinical ratings [1]. For clinical applications, these results are usually transformed into clinically meaningful and interpretable parameters, such as gait speed, motion range of joints and body balance.

References

1.
D. A. Heldman, A. J. Espay, P. A. LeWitt, J. P. Giuffrida, “Clinician versus machine: reliability and responsiveness of motor endpoints in parkinson’s disease”, Parkinsonism & related disorders, vol. 20, no. 6, pp. 590-595, 2014.
2.
K. Otte, B. Kayser, S. Mansow-Model, J. Verrel, F. Paul, A. U. Brandt, T. Schmitz- Hubsch, “Accuracy and reliability of the kinect version 2 for clinical measurement of motor function”, PloS one, vol. 11, no. 11, pp. e0166532, 2016.
3.
O. O’Neil, C. Gatzidis, I. Swain, “A state of the art survey in the use of video games for upper limb stroke rehabilitation” in Virtual Augmented Reality and Serious Games for Healthcare 1, Springer, pp. 345-370, 2014.
4.
H. Mousavi Hondori, M. Khademi, “A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation”, Journal of medical engineering, vol. 2014, no. 846514, 2014.
5.
J. A. Gil-Gomez, R. Lloréns, M. Alcafiiz, C. Colomer, “Effectiveness of a wii balance board-based system (ebavir) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury”, Journal of neuroengineering and rehabilitation, vol. 8, no. 1, pp. 30, 2011.
6.
E. D. Ofia, C. Balaguer, R. Cano de la Cuerda, S. Collado Vázquez, A. Jardon, “Effectiveness of serious games for leap motion on the functionality of the upper limb in parkinsons disease: A feasibility study”, Computational Intelligence and Neuroscience, vol. 2018, 2018.
7.
K. Salter, N. Campbell, M. Richardson et al., “Outcome measures in stroke rehabilitation”, Evidence-Based Review of Stroke Rehabilitation. Heart and Stroke Foundation. Canadian Partnership for Stroke Recovery, 2014.
8.
E. D. Ofia, R. Cano de la Cuerda, P. Sanchez-Herrera, C. Balaguer, A. Jardon, “A review of robotics in neurorehabilitation: Towards an automated process for upper limb”, Journal of Healthcare Engineering, vol. 2018, 2018.
9.
Medicaa balance for life, [online] Available: http://www.medicaa.com.
10.
Virtual Rehab, Virtual rehabilitation system.
11.
K. Tanaka, J. Parker, G. Baradoy, D. Sheehan, J. R. Holash, L. Katz, “A comparison of exergaming interfaces for use in rehabilitation programs and research”, Loading…, vol. 6, no. 9, 2012.
12.
J. E. Deutsch, M. Borbely, J. Filler, K. Huhn, P. Guarrera-Bowlby, “Use of a low-cost commercially available gaming console (wii) for rehabilitation of an adolescent with cerebral palsy”, Physical therapy, vol. 88, no. 10, pp. 1196-1207, 2008.
13.
H. Sin, G. Lee, “Additional virtual reality training using xbox kinect in stroke survivors with hemiplegia”, American Journal of Physical Medicine & Rehabilitation, vol. 92, no. 10, pp. 871-880, 2013.
14.
J. Wiemeyer, A. Kliem, “Serious games in prevention and rehabil-itationa new panacea for elderly people?”, European Review of Aging and Physical Activity, vol. 9, no. 1, pp. 41, 2011.
15.
A. Pfister, A. M. West, S. Bronner, J. A. Noah, “Comparative abilities of microsoft kinect and vicon 3d motion capture for gait analysis”, Journal of medical engineering & technology, vol. 38, no. 5, pp. 274-280, 2014.
16.
S. K. Jun, X. Zhou, D. K. Ramsey, V. N. Krovi, “A comparative study of human motion capture and computational analysis tools”, The 2nd International Digital Human Modeling Symposium, 2003.
17.
A. M. d. C. Souza, M. A. Gadelha, E. A. Coutinho, S. R. d. Santos, A. Pantoja, A. Pereira, “A video-tracking based serious game for motor rehabilitation of post-stroke hand impairment”, SBC Journal on 3D Interactive Systems, vol. 3, no. 2, pp. 37-46, 2012.
18.
Z. Luo, C. K. Lim, I. M. Chen, S. H. Yeo, “A virtual reality system for arm and hand rehabilitation”, Frontiers of Mechanical Engineering, vol. 6, no. 1, pp. 23-32, 2011.
19.
O. Wasenmuller, D. Stricker, “Comparison of kinect v l and v2 depth images in terms of accuracy and precision”, Asian Conference on Computer Vision Workshop (ACCV workshop), 2016.
20.
J. Van der Putten, J. Hobart, J. Freeman, A. Thompson, “Measuring change in disability after inpatient rehabilitation: comparison of the responsiveness of the barthel index and the functional independencemeasure”, Journal of Neurology Neurosurgery & Psychiatry, vol. 66, no. 4, pp. 480-484, 1999.
21.
E. D. Ofia, A. Jardon, C. Balaguer, Y. Gao, S. Fallah, Y. Jin, C. Lekakou, “The automated box and blocks test an autonomous assessment method of gross manual dexterity in stroke rehabilitation” in Towards Autonomous Robotic Systems TAROS 2017, Cham: Springer, vol. 10454, pp. 101-114, 2017.
22.
C. Rodriguez-de Pablo, J. C. Perry, F. I. Cavallaro, H. Zabaleta, T. Keller, “Development of computer games for assessment and training in post-stroke arm telerehabilitation”, Engineering in Medicine and Biology Society (EMBC) 2012 Annual International Conference of the IEEE, pp. 4571-4574, 2012.
23.
V. Vallejo, P. Wyss, A. Chesham, A. V. Mitache, R. M. Muri, U. P. Mosimann, T. Nef, “Evaluation of a new serious game based multitasking assessment tool for cognition and activities of daily living: Comparison with a real cooking task”, Computers in human behavior, vol. 70, pp. 500-506, 2017.
24.
B. Bonnechere, V. Sholukha, L. Omelina, M. Van Vooren, B. Jansen, S. V. S. Jan, “Suitability of functional evaluation embedded in serious game rehabilitation exercises to assess motor development across lifespan”, Gait & posture, vol. 57, pp. 35-39, 2017.
25.
E. van der Meulen, M. A. Cidota, S. G. Lukosch, P. J. Bank, A. J. van der Helm, V. T. Visch, “A haptic serious augmented reality game for motor assessment of parkinson’s disease patients”, Mixed and Augmented Reality (ISMAR-Adjunct) 2016 IEEE International Symposium on, pp. 102-104, 2016.
26.
C. Bosecker, L. Dipietro, B. Volpe, H. Igo Krebs, “Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke”, Neu-rorehabilitation and neural repair, vol. 24, no. 1, pp. 62-69, 2010.
28.
L. Santisteban, M. Teremetz, J. P. Bleton, J. C. Baron, M. A. Maier, P. G. Lindberg, “Upper limb outcome measures used in stroke rehabilitation studies: a systematic literature review”, PloS one, vol. 11, no. 5, pp. e0154792, 2016.
29.
J. W. Burke, M. McNeill, D. K. Charles, P. J. Morrow, J. H. Crosbie, S. M. McDonough, “Optimising engagement for stroke rehabilitation using serious games”, The Visual Computer, vol. 25, no. 12, pp. 1085-1099, 2009.
30.
K. Sathian, L. J. Buxbaum, L. G. Cohen, J. W. Krakauer, C. E. Lang, M. Corbetta, S. M. Fitzpatrick, “Neurological principles and rehabilitation of action disorders common clinical deficits”, Neu-rorehabilitation and neural repair, vol. 25, no. 5 suppl, pp. 21S-32S, 2011.
31.
P. W. Duncan, M. Propst, S. G. Nelson, “Reliability of the fugl-meyer assessment of sensorimotor recovery following cerebrovascular accident”, Physical therapy, vol. 63, no. 10, pp. 1606-1610, 1983.
32.
J. Sanford, J. Moreland, L. R. Swanson, P. W. Stratford, C. Gow-land, “Reliability of the fugl-meyer assessment for testing motor performance in patients following stroke”, Physical therapy, vol. 73, no. 7, pp. 447-454, 1993.
33.
A. Deakin, H. Hill, V. M. Pomeroy, “Rough guide to the fugl-meyer assessment: Upper limb section”, Physiotherapy, vol. 89, no. 12, pp. 751-763, 2003.
34.
D. J. Gladstone, C. J. Danells, S. E. Black, “The fugl-meyer assessment of motor recovery after stroke: a critical review of its measurement properties”, Neurorehabilitation and neural repair, vol. 16, no. 3, pp. 232-240, 2002.
35.
W. S. Kim, S. Cho, D. Baek, H. Bang, N. J. Paik, “Upper extremity functional evaluation by fugl-meyer assessment scoring using depth-sensing camera in hemiplegic stroke patients”, PloS one, vol. 11, no. 7, pp. e0158640, 2016.

via Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games – IEEE Conference Publication

, , , , , , , , , , ,

Leave a comment

[Abstract] sEMG Bias-driven Functional Electrical Stimulation System for Upper Limb Stroke Rehabilitation

Abstract:

It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio (SNR) in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.
Published in: IEEE Sensors Journal ( Early Access ) Date of Publication: 18 June 2018

Related Articles

via sEMG Bias-driven Functional Electrical Stimulation System for Upper-Limb Stroke Rehabilitation – IEEE Journals & Magazine

, , , , , , , , , , , , ,

Leave a comment

[Abstract] Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton

Abstract:

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.

via Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton – IEEE Journals & Magazine

, , , , , , , , ,

Leave a comment

[Abstract] Robot-assisted mirroring exercise as a physical therapy for hemiparesis rehabilitation

Abstract:

The paper suggests a therapeutic device for hemiparesis that combines robot-assisted rehabilitation and mirror therapy. The robot, which consists of a motor, a position sensor, and a torque sensor, is provided not only to the paralyzed wrist, but also to the unaffected wrist to induce a symmetric movement between the joints. As a user rotates his healthy wrist to the direction of either flexion or extension, the motor on the damaged side rotates and reflects the motion of the normal side to the symmetric angular position. To verify performance of the device, five stroke patients joined a clinical experiment to practice a 10-minute mirroring exercise. Subjects on Brunnstrom stage 3 had shown relatively high repulsive torques due to severe spasticity toward their neutral wrist positions with a maximum magnitude of 0.300kgfm, which was reduced to 0.161kgfm after the exercise. Subjects on stage 5 practiced active bilateral exercises using both wrists with a small repulsive torque of 0.052kgfm only at the extreme extensional angle. The range of motion of affected wrist increased as a result of decrease in spasticity. The therapeutic device not only guided a voluntary exercise to loose spasticity and increase ROM of affected wrist, but also helped distinguish patients with different Brunnstrom stages according to the size of repulsive torque and phase difference between the torque and the wrist position.

Source: Robot-assisted mirroring exercise as a physical therapy for hemiparesis rehabilitation – IEEE Conference Publication

, , , , , , , , ,

Leave a comment

[Abstract] EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication

Abstract:

Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Source: EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication

, , , , , , ,

Leave a comment

[Abstract] Enhancing clinical implementation of virtual reality

Abstract:

Despite an emerging evidence base and rapid increases in the development of clinically accessible virtual reality (VR) technologies for rehabilitation, clinical adoption remains low. This paper uses the Theoretical Domains Framework to structure an overview of the known barriers and facilitators to clinical uptake of VR and discusses knowledge translation strategies that have been identified or used to target these factors to facilitate adoption. Based on this discussion, we issue a ‘call to action’ to address identified gaps by providing actionable recommendations for development, research and clinical implementation.

Source: Enhancing clinical implementation of virtual reality – IEEE Xplore Document

, , , , , , , , ,

Leave a comment

%d bloggers like this: