Posts Tagged TASK ANALYSIS

[Abstract + References] Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication

Abstract

The paper presents a user study to compare the performance of two rehabilitation robotic systems, called HomeRehab and PupArm. The first one is a novel tele-rehabilitation system for delivering therapy to stroke patients at home and the second one has been designed and developed to provide rehabilitation therapy to patients in clinical settings. Nine patients with different neurological disorders participated in the study. The patients performed 16 movements with each robotic platform and after that they filled a usability survey. Moreover, to evaluate the patient’s performance with each robotic device, 8 movement parameters were computed from each trial and for the two robotic devices. Based on the analysis of subjective assessments of usability and the data acquired objectively by the robotic devices, we can conclude that the performance and user experience with both systems are very similar. This finding will be the base of more extensive studies to demonstrate that home-therapy with HomeRehab could be as efficient as therapy in clinical settings assisted by PupArm robot.

 

1. WHO global report. Preventing Chronic Diseases: A Vital Investment, World Health Organization, 2005.

2. J. Mackay, G. A. Mensah, The Atlas of Heart Disease and Stroke, Geneva, Switzerland:World Health Organization, 2004.

3. D. S. Nichols-Larsen, P. C. Clark, A. Zeringue, A. Greenspan, S. Blanton, “Factors Influencing Stroke Survivors Quality of Life during Subacute Recovery”, Stroke, vol. 36, pp. 14801484, 2005.

4. P. Langhorne, F. Coupar, A. Pollock, “Motor Recovery after Stroke: a Systematic Review”, The Lancet Neurology, vol. 8, no. 8, pp. 741754, 2009.

5. C. R. Carnigan, H. I. Krebs, “Telerehabilitation Robotics: Bright Lights Big Future?”, Journal of Rehabilitation Research and Development, vol. 43, no. 5, pp. 695-710, 2006.

6. K. J. Ottenbacher, P. M. Smith, S. B. Illig, R. T. Linn, G. V. Ostir, C. V. Granger, “Trends in Length of Stay Living Setting Functional Outcome and Mortality following Medical Reha-bilitation”, JAMA, vol. 292, no. 14, pp. 1687-1695, 2004.

7. L. Richards, C. Hanson, M. Wellborn, A. Sethi, “Driving Motor Recovery after Stroke”, Topics in Stroke Rehabilitation, vol. 15, no. 5, pp. 397411, 2008.

8. S. M. Linder, A. B. Rosenfeldt, A. Reiss, S. Buchanan, K. Sahu, C. R. Bay, S. L. Wolf, J. L. Alberts, “The Home Stroke Rehabilitation and Monitoring System Trial: A Randomized Controlled Trial”, International Journal of Stroke, vol. 8, no. 1, pp. 1747-4949, 2013.

9. T. Larsen, T. S. Olsen, J. Sorensen, “Early Home-Supported Discharge of Stroke Patients: A Health Technology Assessment”, International Journal of Technology Assessment in Health Care, vol. 22, no. 3, pp. 313-320, 2006.

10. Ifiaki Díaz, José María Catalan, Francisco Javier Badesa, Xabier Justo, Luis Daniel Lledo, Axier Ugartemendia, Jorge juan Gil, Jorge Díez, Nicolás García-Aracil, Development of a robotic device for post-stroke home tele-rehabilitation. Advances in Mechanical Engineering, vol. 10, no. 1, pp. 1-8, 2018.

11. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, J. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

12. R. Likert, G. M. Maranell, “A method of constructing an attitude scale” in Scaling: A Sourcebook for Behavioral Scientists, Chicago, IL:Aldine Publishing, pp. 233243, 1974.

13. H. J. Krebs, N. Hogan, M. L. Aisen, B. T. Volpe, “Robot-aided neurorehabilitation”, IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 1, pp. 75-87, Mar 1998.

14. Franciso J Badesa, Ana Llinares, Ricardo Morales, Nicolas Garcia-Aracil, Jose M Sabater, Carlos Perez-Vidal, “Pneumatic planar rehabilitation robot for post-stroke patients”, Biomedical Engineering: Applications Basis and Communications, vol. 26, no. 2, pp. 1450025, 2014.

15. D. Lledo Luis, A. Diez Jorge, Bertomeu-Motos Arturo, Ezquerro Santiago, J. Badesa Francisco, M. Sabater-Navarro Jose, Garca-Aracil Nicolas, “A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients”, Frontiers in Aging Neuroscience, vol. 8, pp. 205, 2016.

16. A. Llinares, F. J. Badesa, R. Morales, N. Garcia-Aracil, J. Sabater, E. Fernandez, “Robotic assessment of the influence of age on upper-limb sensorimotor function”, Clin. Interv. Aging, vol. 8, pp. 879, 2013.

17. D. S. Dunn, Statistics and data analysis for the behavioral sciences, New York, NY, US:McGraw-Hill, 2001.

18. J. Brooke, P. W. Jordan, B. Thomas, B. A. Weerd-meester, I. L. McClealland, “SUS: A quick and dirty usability scale” in Usability Evaluation in Industry, London:Taylor and Francis, pp. 189194, 1996.

19. AM Coderre, AA Zeid, SP Dukelow et al., “Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching”, Neurorehabil Neural Repair., vol. 24, no. 6, pp. 528541, 2010.

via Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication

, , , , , , , , , , ,

Leave a comment

[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] A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study

Abstract:

When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.

I. Introduction

Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis [1]. There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes [2]. Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments [3]. It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements [4]. To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings [5], which emphasizes the importance of the home-based therapy.

 

via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication

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

Leave a comment

Efficacy and feasibility of home-based training of individuals with homonymous visual field defects.

…Findings suggest that home-based compensatory training is an inexpensive accessible rehabilitation option for individuals with HVFDs, which can result in objective benefits in searching and reading, as well as improving quality of life…

μέσω National Rehabilitation Information Center.

, , , , , , , ,

Leave a comment

%d bloggers like this: