Posts Tagged Tracking

[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.

 

via Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller – IEEE Conference Publication

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

Leave a comment

[Abstract] Towards an Immersive Virtual Reality Game for Smarter Post-Stroke Rehabilitation

Abstract:

Traditional forms of physical therapy and rehabilitation are often based on therapist observation and judgment, coincidentally this process oftentimes can be inaccurate, expensive, and non-timely. Modern immersive Virtual Reality systems provide a unique opportunity to make the therapy process smarter. In this paper, we present an immersive virtual reality stroke rehabilitation game based on a widely accepted therapy method, Constraint-Induced Therapy, that was evaluated by nine post-stroke participants. We implement our game as a dynamically adapting system that can account for the user’s motor abilities while recording real-time motion capture and behavioral data. The game also can be used for tele-rehabilitation, effectively allowing therapists to connect with the participant remotely while also having access to +90Hz real-time biofeedback data. Our quantitative and qualitative results suggest that our system is useful in increasing affordability, accuracy, and accessibility of post-stroke motor treatment.

via Towards an Immersive Virtual Reality Game for Smarter Post-Stroke 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

[Master’s thesis] Tracking, monitoring and feedback of patient exercises using depth camera technology for home based rehabilitation – ANNA RIDDERSTOLPE – Full Text PDF

Abstract

Neurological and chronic diseases have profound impacts on a person’s life. Rehabilitation is essential in order to maintain and promote maximal level of recovery by pushing the bounds of physical, emotional and cognitive impairments. However, due to the low physical mobility and poor overall condition of many patients, traveling back and forth to doctors, nurses and rehabilitation centers can be exhausting tasks. In this thesis a game-based rehabilitation platform for home usage, supporting stroke and COPD rehabilitation is presented. The main goal is to make rehabilitation more enjoyable, individualized and easily accessible for the patients.

The game-based rehabilitation tool consists of three systems with integrated components: the caregiver’s planning and follow-up system, the patient’s gaming system and the connecting server system. The server back end components allow the storage of patient specific information that can be transmitted between the patient and the caregiver system for planning, monitoring and feedback purposes. The planning and follow-up system is a server system accessed through a web-based front-end, where the caregiver schedules the rehabilitation program adjusted for each individual patient and follow up on the rehabilitation progression. The patient system is the game platform developed in this project, containing 16 different games and three assessment tests. The games are based on specific motion patterns produced in collaboration with rehabilitation specialists. Motion orientation and guidance functions is implemented specifically for each exercise to provide feedback to the user of the performed motion and to ensure proper execution of the desired motion pattern.

The developed system has been tested by several people and with three real patients. The participants feedback supported the use of the game-based platform for rehabilitation as an entertaining alternative for rehabilitation at home. Further implementation work and evaluation with real patients are necessary before the product can be used for commercial purpose.

Full Text PDF

 

, , , , , , , ,

Leave a comment

[STUDY] – Design of a physiologically informed virtual reality based interactive platform for individuals with upper limb impairment

A large number of stroke-surviving individuals exhibit deficits related to upper limb movement, thereby making post stroke rehabilitation a critical part of patients’ health care system. The patients are typically treated with conventional occupational therapy at the hospital after stroke. However, due to economic pressures and limited health care resources often the patients receive less therapy than required causing them to be deprived of the potential therapeutic benefits. Thus implementing a cost-effective home based technology-assisted rehabilitation system which is capable of providing intensive, adaptive and individualized rehabilitation service is critical. Virtual reality (VR) based rehabilitation system seems to address this challenge effectively. VR technology for rehabilitation allows us to create an interactive environment with precise control over intensity of practice that influence one’s motor control in an individualized manner. In this study we developed an interactive VR-based platform which challenges the coordination skill of individuals with upper limb impairment. Additionally, we used patient’s physiological indices to understand their stress level while they interact with the VR-based rehabilitation environment. The system developed in this work is a first step to understand its potential to provide individualized home-based rehabilitative service with minimal dependency on physiotherapist. In our initial study designed as a proof-of-concept application, one stroke-surviving patient participated in the interactive VR-based task. The preliminary results obtained from this initial study indicate the potential of mapping one’s stress level to his physiological indices. Thus these results indicate the potential applicability of such a system for various stroke-rehabilitation applications

via IEEE Xplore Abstract (Abstract) – Design of a physiologically informed virtual reality based interactive platform for individuals with….

, , , , , , , , , ,

Leave a comment

%d bloggers like this: