Posts Tagged Tools

[Abstract] Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation – IEEE Conference Publication

Abstract

The objective of this paper was to compare the incidence of a rehabilitation game in motor ability with dynamic difficulty adjustment (ADD) in comparison to a manual configuration. To achieve that, a virtual tool called “Bug catcher” was developed, which is focused in upper limb rehabilitation. This tool uses a dynamic difficulty adjustment based in fuzzy logic. The population involved for the present study were made by 2 users, a 18-year-old patient with a hemiparesis that limits her motor ability in her left upper limb, and a 37-year-old patient with motor monoparesis in his right upper limb. This tool was used in both users, each one with a different configuration (automatic or manual), and the motor ability from both participants was objectively measured using Box and Blocks Test, applied before, during and after each session; additionally, a performance index (percentage of success) was defined in order to determine the progress of the participants in the virtual tool. As a result, it was obtained that user number one using the game with ADD, managed to obtain not only a better performance in the sessions but also an important advance in her motor skill in comparison to the user 2 with the manual configuration.

via Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation – IEEE Conference Publication

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

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

%d bloggers like this: